feat: pull OpenLinkSW fork changes #86
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Caution Review failedThe pull request is closed. WalkthroughThis pull request introduces a comprehensive update to the SPARQLES backend, focusing on adding a new calculation task ( Changes
Sequence DiagramsequenceDiagram
participant Scheduler
participant TaskFactory
participant CTask
participant CAnalyser
participant MongoDBManager
Scheduler->>TaskFactory: Create task for endpoint
TaskFactory->>CTask: Initialize with endpoint
CTask->>CTask: Process endpoint data
CTask->>CAnalyser: Generate calculation result
CAnalyser->>MongoDBManager: Store calculation view
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Actionable comments posted: 2
🧹 Nitpick comments (35)
backend/src/main/java/org/osjava/norbert/NoRobotClient.java (1)
96-98: Validate arguments to prevent null pointer issues.
It might be helpful to check whethertxtorbaseUrlis null or empty before proceeding. Failing to do so can lead to unexpected errors later.public void parse(String txt, URL baseUrl) throws NoRobotException { + if (txt == null || baseUrl == null) { + throw new IllegalArgumentException("Arguments 'txt' or 'baseUrl' cannot be null."); + } this.baseUrl = baseUrl; parseText(txt); }backend/src/main/java/sparqles/avro/core/Robots.java (1)
17-19: Consider adding a JavaDoc comment for clarity.
While this is likely Avro-generated code, including a brief JavaDoc forgetClassSchema()could help developers quickly identify the method’s purpose without scanning the entire file.backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityData.java (2)
105-110: Consider a null check for better stability.
When copying from an existingBuilder, a quick null check could guard against inadvertentNullPointerExceptions.public static sparqles.avro.analytics.EPViewAvailabilityData.Builder newBuilder( sparqles.avro.analytics.EPViewAvailabilityData.Builder other) { + if (other == null) { + throw new NullPointerException("Builder cannot be null"); + } return new sparqles.avro.analytics.EPViewAvailabilityData.Builder(other); }
111-119: Likewise, consider defending against null inputs.
Similarly, a null check when copying from anEPViewAvailabilityDatainstance could enhance robustness.public static sparqles.avro.analytics.EPViewAvailabilityData.Builder newBuilder( sparqles.avro.analytics.EPViewAvailabilityData other) { + if (other == null) { + throw new NullPointerException("EPViewAvailabilityData cannot be null"); + } return new sparqles.avro.analytics.EPViewAvailabilityData.Builder(other); }backend/src/main/java/sparqles/avro/analytics/IndexViewPerformanceData.java (1)
133-141: Ensure future fields are included in deep copies.This constructor-based approach for copying from an existing
IndexViewPerformanceDatais good. For future expansions, ensure newly added fields are also copied here to avoid potential data loss.backend/src/main/config/log4j.properties (4)
Line range hint
1-8: Document logger configuration optionsThe mix of active and commented logger configurations makes it unclear which combinations should be used in different environments (development, production, etc.).
Consider:
- Adding documentation to explain when to enable specific loggers
- Moving the commented configurations to a separate example file
- Using environment-specific properties files (e.g.,
log4j-dev.properties,log4j-prod.properties)
73-74: Standardize log patterns across appendersThe console appenders use a different pattern (
[%5p] %c:%L) compared to file appenders (%15.15c{1}:%-3.3L %5p). This inconsistency could make log correlation more difficult.
- Remove the commented pattern layouts to reduce confusion
- Use the same pattern across all appenders unless there's a specific reason for the difference
Also applies to: 87-88
91-94: Review HTML appender configurationThe HTML appender configuration has potential issues:
- Uses DEBUG threshold while other appenders use INFO
- No file rotation strategy, which could lead to large files
Consider:
- Aligning the threshold with other appenders
- Implementing a rotation strategy similar to other appenders:
log4j.appender.HTML=org.apache.log4j.FileAppender +log4j.appender.HTML=org.apache.log4j.DailyRollingFileAppender +log4j.appender.HTML.DatePattern='.'yyyy-MM-dd log4j.appender.HTML.File=logs/main.html log4j.appender.HTML.layout=org.apache.log4j.HTMLLayout -log4j.appender.HTML.Threshold=DEBUG +log4j.appender.HTML.Threshold=INFO
96-100: Clarify package logging levels commentThe comment "DISABLE certain packages" is misleading as these packages are not disabled but set to WARN level.
-#DISABLE certain packages +# Reduce logging noise from third-party packagesbackend/src/main/java/sparqles/avro/performance/Run.java (1)
16-19: Consider improving documentation forgetClassSchema().This method returns the same
SCHEMA$object asgetSchema(). To avoid confusion, you may want to add JavaDoc that clarifies its intended usage versusgetSchema().backend/src/main/java/sparqles/analytics/AEvol.java (6)
21-23: Augment the Javadoc comment
These lines provide minimal context forargs. Consider describing the expected arguments for clarity.
24-26: Add a usage message or argument validation
It might be helpful for maintainability to print usage instructions or validateargswithinmain, ensuring that any mandatory parameters (e.g., file paths, environment directives) are properly handled.
32-33: Consider making the properties file path configurable
Hardcoding"src/main/resources/sparqles_docker.properties"could limit portability. Reading from config or environment variables might be more flexible in different deployments.
36-36: Check for resource closure
While creating a newMongoDBManageris acceptable, ensure it or its underlying connections are closed properly to avoid resource leaks.
53-57: Use HOUR_OF_DAY instead of HOUR
Relying onCalendar.HOURcan introduce AM/PM ambiguity. Switching toCalendar.HOUR_OF_DAYis more robust for setting time to 00:00.
82-83: Remove or clarify commented-out code
These commented lines for reading external endpoints could cause confusion. If no longer needed, consider removing them for cleanliness.backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (1)
139-166: analyseCalculation method.Categorizes coherence and RS into histogram bins. No issues spotted. Consider adding boundary checks for any unexpected negative values beyond the existing conditions.
backend/src/main/java/sparqles/core/calculation/CTask.java (1)
1-575: New class for endpoint-based calculations.Overall, the logic comprehensively gathers metrics (triples, entities, properties, etc.):
- Potential Performance Concern: Repeated large SPARQL queries can strain endpoints. Consider robust timeout handling and partial result fallback.
- Security: The queries appear generic, but confirm no user input is concatenated into query strings to avoid injection.
- Maintainability: Centralizing query definitions is helpful. If queries expand, consider a more dynamic approach or caching to reduce overhead.
backend/src/main/avro/CalculationView.avsc (1)
1-15: Fields look coherent; consider descriptive naming for clarity.
- The usage of boolean flags (
VoID,VoIDPart,SD,SDPart) is acceptable, but if they represent distinct concepts, you might find more descriptive names beneficial (e.g.,hasVoID/hasVoIDPartition).- Ensure the numeric fields (
coherence,RS,lastUpdate) are correctly interpreted in any related code logic, especially regarding precision.backend/src/test/java/sparqles/analytics/FAnalyticsTEST.java (1)
3-3: JUnit static import.
Addingimport static org.junit.Assert.*;is acceptable for convenience. Consider selectively importing only the assertions you need (e.g.,assertTrue,assertEquals) to improve clarity and avoid potential naming collisions in larger codebases.backend/src/main/java/sparqles/analytics/CAnalyser.java (2)
3-13: Imports and dependencies
Imports forCResultandMongoDBManagerare relevant. Ensure no redundant imports remain.
70-94:getView(Endpoint ep)method
- Properly returns or initializes a new
CalculationView, inserting it if none exists.- Logging a warning on multiple views is good, but you might consider stricter error handling if multiple results should never happen.
backend/src/main/java/sparqles/analytics/Analytics.java (2)
20-23: Additional imports for results
Newly added references toAResult,DResult,FResult, andPResultexpand the framework’s coverage of result types. Check that these are indeed needed and used in the subclass or the same package.
81-97:EPViewCalculationinstantiation
The default values for newly createdEPViewCalculation(e.g., -1L, -1.0) appear to be placeholders. Ensure any future code checks handle these appropriately. Consider using constants to clarify their meaning (e.g., NO_VALUE = -1).backend/src/main/avro/Index.avsc (1)
139-182: Addcalculationfield for index-based analytical metrics.The introduction of these fields enhances the schema for measuring and storing coherence, RSS, VoID, SD, etc. Carefully confirm your codebase’s usage of
IndexViewCalculationto avoid null pointer exceptions or compatibility issues if older readers expect a missing field.backend/src/main/java/sparqles/utils/cli/ARGUMENTS.java (1)
119-119: Re-check references toOPTION_RECOMPUTELAST.This might interact with the newly introduced flags. Confirm that there's no overlap or duplicated logic elsewhere.
backend/src/main/java/sparqles/analytics/AnalyserInit.java (1)
33-33: Supplement method documentation.Revise the Javadoc to include mention of the new calculation analysis for completeness.
backend/src/main/java/sparqles/avro/analytics/IndexViewCalculationDataValues.java (1)
30-34: Constructor usage best practices.
Although Avro-generated classes provide a no-args constructor and an all-args constructor, consider using the builder pattern to ensure fields are set consistently with schema defaults.backend/src/main/java/sparqles/schedule/Scheduler.java (2)
124-124: Initialize CTask in the default schedule.
The new lines.setCTask(taskSchedule.get(CTASK));ensures endpoints automatically get the CTask schedule. Double-check if all endpoints need this task by default; some might not require frequent or any “calculation” tasks.
169-173: Handle parsing exceptions for CTask schedule gracefully.
Scheduling new tasks can cause parse exceptions if the cron expression is invalid or missing. While you already catchParseException, consider adding domain-specific error logging or alerts for diagnosing scheduling issues for this new task.backend/src/main/java/sparqles/avro/analytics/IndexViewCalculationData.java (1)
32-38: All-args constructor usage note.
While the all-args constructor is convenient, the builder pattern offers more controlled instantiation. If the Avro schema evolves, the builder can mitigate potential constructor breakage.backend/src/main/java/sparqles/avro/analytics/DiscoverabilityView.java (1)
163-182: Check for potential duplication in builder approach.
The builder structure matches patterns from other classes (EPViewDiscoverability, etc.). It appears consistent and correct. If you find repeated boilerplate, consider a code-generation approach to reduce duplication.backend/src/main/java/sparqles/avro/analytics/CalculationView.java (1)
131-144: Getter/setter pair forVoID.
Implementation is straightforward but note any potential confusion with naming: “VoID” vs “void.”backend/src/main/java/sparqles/avro/analytics/IndexViewCalculation.java (1)
21-28: Deprecated public fields.
Avro often marks fields as deprecated. Ensure clarity for future maintainers that these fields remain valid for reading/writing Avro data.backend/src/main/resources/sparqles.properties (1)
3-3: Ensure the data directory is valid in all environments.The specified path
/usr/local/sparqles/data.dirmight not be applicable in every environment. Consider making this path configurable or using environment variables.
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📒 Files selected for processing (82)
backend/src/main/avro/AResult.avsc(1 hunks)backend/src/main/avro/CResult.avsc(1 hunks)backend/src/main/avro/CalculationView.avsc(1 hunks)backend/src/main/avro/DResult.avsc(1 hunks)backend/src/main/avro/EPView.avsc(1 hunks)backend/src/main/avro/FResult.avsc(1 hunks)backend/src/main/avro/Index.avsc(2 hunks)backend/src/main/avro/PResult.avsc(2 hunks)backend/src/main/avro/Run.avsc(1 hunks)backend/src/main/avro/Schedule.avsc(1 hunks)backend/src/main/config/log4j.properties(2 hunks)backend/src/main/java/org/osjava/norbert/NoRobotClient.java(1 hunks)backend/src/main/java/sparqles/analytics/AEvol.java(4 hunks)backend/src/main/java/sparqles/analytics/AnalyserInit.java(4 hunks)backend/src/main/java/sparqles/analytics/Analytics.java(4 hunks)backend/src/main/java/sparqles/analytics/CAnalyser.java(1 hunks)backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java(7 hunks)backend/src/main/java/sparqles/avro/Dataset.java(2 hunks)backend/src/main/java/sparqles/avro/Endpoint.java(2 hunks)backend/src/main/java/sparqles/avro/EndpointResult.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/AvailabilityIndex.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/AvailabilityView.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/CalculationView.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/DiscoverabilityView.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/EPView.java(10 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewAvailability.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityData.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityDataPoint.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewCalculation.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverability.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverabilityData.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewInteroperability.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewInteroperabilityData.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewPerformance.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewPerformanceData.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewPerformanceDataValues.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/Index.java(10 hunks)backend/src/main/java/sparqles/avro/analytics/IndexAvailabilityDataPoint.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewCalculation.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewCalculationData.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewCalculationDataValues.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewDiscoverability.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewDiscoverabilityData.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewDiscoverabilityDataValues.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewInterData.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewInterDataValues.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewInteroperability.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewPerformance.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewPerformanceData.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewPerformanceDataValues.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/InteroperabilityView.java(2 hunks)backend/src/main/java/sparqles/avro/analytics/PerformanceView.java(2 hunks)backend/src/main/java/sparqles/avro/availability/AResult.java(2 hunks)backend/src/main/java/sparqles/avro/calculation/CResult.java(1 hunks)backend/src/main/java/sparqles/avro/core/Robots.java(2 hunks)backend/src/main/java/sparqles/avro/discovery/DGETInfo.java(2 hunks)backend/src/main/java/sparqles/avro/discovery/DResult.java(2 hunks)backend/src/main/java/sparqles/avro/discovery/QueryInfo.java(2 hunks)backend/src/main/java/sparqles/avro/discovery/RobotsTXT.java(2 hunks)backend/src/main/java/sparqles/avro/features/FResult.java(2 hunks)backend/src/main/java/sparqles/avro/features/FSingleResult.java(2 hunks)backend/src/main/java/sparqles/avro/performance/PResult.java(2 hunks)backend/src/main/java/sparqles/avro/performance/PSingleResult.java(2 hunks)backend/src/main/java/sparqles/avro/performance/Run.java(2 hunks)backend/src/main/java/sparqles/avro/schedule/Schedule.java(12 hunks)backend/src/main/java/sparqles/core/CONSTANTS.java(1 hunks)backend/src/main/java/sparqles/core/Task.java(1 hunks)backend/src/main/java/sparqles/core/TaskFactory.java(2 hunks)backend/src/main/java/sparqles/core/calculation/CTask.java(1 hunks)backend/src/main/java/sparqles/core/interoperability/TaskRun.java(1 hunks)backend/src/main/java/sparqles/schedule/Scheduler.java(4 hunks)backend/src/main/java/sparqles/utils/MongoDBManager.java(8 hunks)backend/src/main/java/sparqles/utils/QueryManager.java(0 hunks)backend/src/main/java/sparqles/utils/cli/ARGUMENTS.java(4 hunks)backend/src/main/java/sparqles/utils/cli/CLIObject.java(2 hunks)backend/src/main/java/sparqles/utils/cli/SPARQLES.java(6 hunks)backend/src/main/resources/sparqles.properties(1 hunks)backend/src/main/resources/sparqles_docker.properties(1 hunks)backend/src/test/java/sparqles/analytics/AAnalyticsInitTEST.java(1 hunks)backend/src/test/java/sparqles/analytics/AAnalyticsTEST.java(1 hunks)backend/src/test/java/sparqles/analytics/FAnalyticsTEST.java(1 hunks)backend/src/test/java/sparqles/analytics/IndexViewAnalyticsTEST.java(1 hunks)
⛔ Files not processed due to max files limit (7)
- backend/src/test/java/sparqles/analytics/PAnalyticsTEST.java
- backend/src/test/java/sparqles/core/EPViewTEST.java
- backend/src/test/java/sparqles/core/InteropTEST.java
- backend/src/test/java/sparqles/core/LogTest.java
- backend/src/test/java/sparqles/core/PerformanceTEST.java
- backend/src/test/java/sparqles/core/SchedulerTEST.java
- backend/src/test/resources/sparqles.properties
💤 Files with no reviewable changes (1)
- backend/src/main/java/sparqles/utils/QueryManager.java
✅ Files skipped from review due to trivial changes (7)
- backend/src/test/java/sparqles/analytics/IndexViewAnalyticsTEST.java
- backend/src/test/java/sparqles/analytics/AAnalyticsTEST.java
- backend/src/main/java/sparqles/core/Task.java
- backend/src/main/avro/FResult.avsc
- backend/src/test/java/sparqles/analytics/AAnalyticsInitTEST.java
- backend/src/main/java/sparqles/core/interoperability/TaskRun.java
- backend/src/main/java/sparqles/utils/cli/CLIObject.java
🔇 Additional comments (290)
backend/src/main/java/sparqles/avro/analytics/IndexViewDiscoverabilityDataValues.java (2)
17-19: Looks good: getClassSchema() logic.
Returning the static schema pointer (SCHEMA$) is aligned with Avro patterns. No issues found here.
96-116: Builder methods are correctly structured.
These builder creation methods follow Avro’s builder pattern specifications, ensuring consistent usage across the codebase. No problems identified.
backend/src/main/java/sparqles/avro/analytics/InteroperabilityView.java (2)
17-19: Well-aligned with Avro best practices.
These lines correctly reintroduce Avro’s standard getClassSchema() method, ensuring the class can expose its schema via SCHEMA$. No issues found.
141-160: Builder pattern restored successfully.
The three static newBuilder() methods ensure consistent record-building capabilities. This pattern aligns with Avro code generation standards and fosters clarity. No further changes needed.
backend/src/main/java/sparqles/avro/features/FSingleResult.java (2)
17-19: Looks good: standard Avro schema retrieval method
The addition of getClassSchema() is aligned with typical Avro-generated classes. This approach is correct and provides easy access to the static schema.
95-111: New builder methods restore essential object construction patterns
Reintroducing the multiple newBuilder() overloads is crucial for maintaining flexible construction and copy semantics. This matches Avro conventions and ensures backward compatibility for consumers who rely on these builder methods.
backend/src/main/java/sparqles/avro/analytics/IndexViewPerformance.java (2)
17-19: Avro-specific schema method is correctly implemented.
The static getClassSchema() method properly returns the SCHEMA$ reference, consistent with typical Avro code generation patterns.
97-116: Builder methods align with Avro's generated code patterns.
These builder methods are standard Avro functionality, enabling flexible instantiation. Verify that their usage does not conflict with future Avro code regeneration, as generated code may overwrite manual modifications.
backend/src/main/java/sparqles/avro/EndpointResult.java (4)
16-19: Good to see a standard Avro pattern for class schema retrieval.
This static method aligns with Avro conventions and helps maintain consistency across generated Avro classes.
117-120: Builder method provides a convenient entry point for clients.
Creating a no-argument builder is a common and recommended approach. This helps prevent confusion around required parameters and fosters safer object construction.
122-127: Clarity gained from a builder-copy constructor.
Allowing a builder to be instantiated from another builder is often helpful for customizing partial state. It's a good practice in Avro-based POJOs for stepwise construction or partial clones.
128-133: Copying from an existing record is handy for migrations or incremental changes.
Creating a new builder from an existing record makes it straightforward to tweak individual fields. This is especially useful in scenarios that require minimal changes while preserving the rest of the state.
backend/src/main/java/sparqles/avro/analytics/IndexViewInteroperability.java (4)
16-19: No issues with the schema retrieval method
The getClassSchema() method consistently follows the Avro convention of returning the static SCHEMA$ field.
75-79: Avro builder method aligns with recommended patterns
Providing a no-argument newBuilder() method aligns with the typical approach used by Avro-generated classes.
80-85: Builder copy-constructor clarity
This newBuilder() overload, which creates a builder from an existing builder, is a standard pattern in Avro-generated code and helps ensure consistent cloning.
86-93: Builder from an existing record
This newBuilder() overload is similarly standard Avro boilerplate, enabling convenient building from an existing record instance.
backend/src/main/java/sparqles/avro/performance/PResult.java (5)
15-16: Schema import looks correct but verify consistency with other Avro schemas.
You've updated the schema to include "import":"EndpointResult.avsc", which seems logically sound. However, please confirm that all references to EndpointResult in the wider codebase and other Avro definitions are updated accordingly to avoid potential parsing issues or runtime errors.
Would you like a script to scan for references to EndpointResult across the repository to ensure they all match the updated schema?
17-19: New getClassSchema() method aligns with Avro code generation mandate.
Exposing the Avro schema with a static getClassSchema() is consistent with Avro’s recommended approach and ensures that downstream tools or consumers can introspect the schema at runtime. This change looks good.
106-109: New builder creation method is consistent with Avro’s builder pattern.
The newBuilder() method clearly indicates the recommended entry point for constructing PResult objects, which is a best practice to avoid manual instantiation.
111-116: Builder copy constructor is helpful for chaining.
Providing a builder copy constructor lets developers create a new builder from an existing one without losing intermediate settings. This is a useful enhancement for code reusability.
117-121: Ensures a smoother migration path for existing PResult instances.
The new overload that accepts an existing PResult instance is good for copying data into a builder. This can help preserve backward compatibility in scenarios where you need to modify data from an existing record.
backend/src/main/java/sparqles/avro/core/Robots.java (1)
163-178: Builder pattern structure is clear and consistent with Avro.
These builder methods follow the conventional Avro pattern and appear correct. No concurrency or semantic issues are apparent.
backend/src/main/java/sparqles/avro/analytics/PerformanceView.java (4)
16-19: Looks standard for Avro schema retrieval.
The newly introduced getClassSchema() method is consistent with Avro's code generation patterns and provides a convenient way to retrieve the class schema.
207-210: Builder creation method appears correctly implemented.
Auto-generated construction methods like newBuilder() comply with standard Avro patterns and enhance the ease of creating new PerformanceView instances. No changes needed.
212-217: Copy constructor for the Builder.
This supplementary newBuilder(Builder other) correctly duplicates the builder for PerformanceView, enabling safe, incremental modifications to existing records.
218-225: Builder method overload is well-structured.
The overridden newBuilder(PerformanceView other) method ensures a flexible way to clone existing records. This is standard Avro Builder pattern usage.
backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityData.java (2)
16-19: Looks good, standard Avro approach.
The newly introduced getClassSchema() method follows the standard Avro pattern of returning the static SCHEMA$. This is consistent and typically auto-generated.
100-104: Builder factory method reintroduction is fine.
This method's signature and implementation comply with Avro's generated pattern for creating a new builder instance.
backend/src/main/java/sparqles/avro/performance/PSingleResult.java (4)
17-19: Consistent Avro schema accessor pattern.
This new getClassSchema() method aligns well with Avro’s recommended pattern, returning the static SCHEMA$ for external usage. No issues detected.
119-123: Builder pattern reintroduction is valid.
Reintroducing newBuilder() ensures a straightforward instantiation process for the PSingleResult class, consistent with typical Avro usage. Good job!
124-129: Builder copy-constructor from another Builder.
Copying an existing builder is a standard approach in Avro to maintain consistent state. The logic here is solid, and no functional issues stand out.
130-135: Builder copy-constructor from an existing instance.
Allowing a new builder to be created from an existing PSingleResult instance follows Avro’s flexible record-building methodology. Implementation is correct and complete.
backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverabilityData.java (5)
16-16: No changes identified.
There is no functional code on this line, so no further action is required.
17-19: Avro schema accessor looks correct.
This method precisely follows Avro's recommended pattern to return the static schema. No issues found.
95-99: New builder creation method is standard.
Using a static newBuilder() method is aligned with Avro-specific code generation practices. Looks good.
100-105: Overloaded builder for copying another builder is consistent.
This overload ensures a fluent pattern when building from an existing builder. No problems here.
106-113: Overloaded builder for copying an existing record is standard Avro practice.
This constructor helps maintain immutability while allowing changes to the copied record in the builder context. Looks good.
backend/src/main/java/sparqles/avro/analytics/IndexViewPerformanceData.java (3)
16-20: Confirm necessity of duplicated schema methods.
This newly added getClassSchema() method returns the same schema as getSchema(). If the use case demands a dedicated static accessor, this is fine, but confirm there's no accidental duplication or confusion over which method to call in client code.
122-126: Good reintroduction of the newBuilder() entry point.
Restoring this static builder method is useful for consumers who want a straightforward, empty builder. The logic looks correct.
127-132: Proper builder copying from an existing Builder.
Deep-copying all fields eliminates potential mutable reference issues. This approach is especially important given Avro’s immutability conventions.
backend/src/main/java/sparqles/avro/analytics/IndexViewDiscoverabilityData.java (2)
17-19: New getClassSchema method is consistent with Avro best practices.
This simple accessor neatly exposes the SCHEMA$ static variable. The approach is standard for Avro-generated classes and should pose no issues from a compatibility standpoint.
102-121: Confirm usage of newBuilder() methods across the codebase.
These builder creation methods remain in the class despite the summary suggesting some builder methods might be removed. If these specific overloads are still used outside this file, retaining them is valid. Otherwise, removing them or relying on standard Avro builder flows may simplify the API surface.
Run the following script to locate and verify occurrences of these builder methods:
backend/src/main/java/sparqles/avro/analytics/IndexViewPerformanceDataValues.java (4)
17-19: Clear and consistent Avro schema retrieval method.
This getClassSchema() method neatly aligns with Avro’s best practices for exposing the schema. It ensures consistent access to the class schema for downstream processes.
95-99: Facilitates straightforward builder creation.
Reintroducing this no-arg static newBuilder() method follows Avro’s recommended pattern for record instantiation and helps maintain backward compatibility with existing code that relies on Avro’s builder approach.
100-105: Builder copy constructor method is beneficial for extensibility.
Providing an overload of newBuilder(Builder other) is particularly helpful when chaining or merging builder state, ensuring robust and flexible record creation scenarios.
106-114: Builder copy from instance ensures immutability and safety.
This method allows creating new Builders from existing instances, preserving immutability in the original record while allowing iterative modifications.
backend/src/main/config/log4j.properties (1)
10-17: Verify logs directory existence
The appender uses a relative path logs/main.log. Ensure the application creates this directory if it doesn't exist, or the logging will fail silently.
✅ Verification successful
Let me search for any code that might be handling directory creation or log initialization, using more general patterns. This will help us verify if the logs directory creation is handled somewhere in the codebase.
Let me examine these key files to see how file/directory management is handled in the application.
Logs directory creation is handled by FileManager
The logs directory creation concern is not an issue. The application has a robust file management system through the FileManager class that handles directory creation. Specifically:
- The
FileManagerclass creates all necessary directories during initialization:private void init() { if (!rootFolder.exists()) rootFolder.mkdirs(); if (!avroFolder.exists()) avroFolder.mkdirs(); if (!resultsFolder.exists()) resultsFolder.mkdirs(); }
- The log directory path is configured through
SPARQLESProperties.getDATA_DIR(), which ensures proper directory structure initialization - The
FileManageris initialized early in the application lifecycle and handles all file-related operations
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check if the logs directory is created during application startup
fd -t f "^(Application|Main|Bootstrap)" -x rg -l "createLogDirectory|mkdirs.*logs"
Length of output: 84
Script:
#!/bin/bash
# Let's try a broader search for directory creation patterns in the codebase
rg -l "mkdir|mkdirs|createDirectory|FileUtils.*directory"
# Also search for log initialization or configuration setup code
rg -l "Log4j|LogManager|getLogger|configure"
Length of output: 2153
Script:
#!/bin/bash
# Let's check FileManager.java since it appears in both searches and likely handles file operations
cat backend/src/main/java/sparqles/utils/FileManager.java
# Also check the main class for initialization
cat backend/src/main/java/sparqles/core/Main.java
Length of output: 16452
backend/src/main/java/sparqles/avro/analytics/IndexViewInterDataValues.java (4)
17-19: Use of getClassSchema() looks good.
This method follows the standard Avro pattern for retrieving the class schema. No further changes are needed.
95-99: Builder creation method is correctly implemented.
The newBuilder() method cleanly follows Avro best practices for instantiating record builders. Looks good as is.
100-105: Builder copy-constructor method is valid.
The overloaded newBuilder(...) copying an existing Builder helps ensure consistent cloning of the record’s internal fields. Implementation appears correct.
106-114: Cloning from an existing record is properly handled.
This approach ensures that all fields in IndexViewInterDataValues are appropriately copied into the new builder instance, preserving Avro’s immutability and schema consistency. No issues found.
backend/src/main/java/sparqles/avro/performance/Run.java (2)
207-210: Looks good.
The straightforward builder creation method follows Avro best practices.
211-222: Consistent use of builder overloads.
Thank you for reintroducing the builder pattern overloads. The consistent approach to copying from an existing Builder or a Run instance maintains clarity and aligns with common Avro usage.
backend/src/main/java/sparqles/avro/analytics/EPViewPerformanceDataValues.java (2)
16-19: Consider implications of modifying generated Avro code.
This getClassSchema() method is typically generated automatically by Avro. Custom changes here risk being overwritten during future code generation. If you need additional functionality, consider updating the Avro schema or using Avro’s recommended extension points to avoid accidental loss of custom code.
Would you like guidance on adjusting your Avro generation workflow or schema to incorporate this logic automatically?
119-138: Ensure consistency across Avro code generation and manual overrides.
These newly introduced builder methods align with standard Avro record-building patterns, which are ordinarily produced by the Avro compiler. Verify that your build configuration won’t overwrite them if this code is regenerated. If necessary, update the Avro schema definitions or generation steps to preserve manual changes.
backend/src/main/java/sparqles/avro/analytics/IndexAvailabilityDataPoint.java (4)
16-19: Reintroduction of getClassSchema() looks correct.
This method reliably returns the static schema reference, consistent with typical Avro-generated classes. Since this code is often auto-generated, verify that manual edits won't be lost if the schema is regenerated.
95-99: newBuilder() method reintroduction is standard for Avro classes.
The approach is standard Avro boilerplate, ensuring that a new RecordBuilder instance can be created without referencing existing data. This is especially useful for easily constructing new records.
100-105: newBuilder(Builder other) method for copying from another Builder seems correct.
This constructor is essential when chaining or duplicating builder instances. It reduces boilerplate in user code, which further improves readability and maintainability.
106-114: newBuilder(IndexAvailabilityDataPoint other) constructor reintroduction is on par with Avro's best practices.
Providing a copy constructor from an existing record is a standard approach, allowing records to be cloned or extended as needed. This pattern streamlines usage for any code that wants to replicate or manipulate existing data points.
backend/src/main/java/sparqles/avro/analytics/EPViewPerformance.java (3)
16-19: Well-structured method for Avro schema retrieval.
This getClassSchema() method correctly returns the static Avro schema, aligning with typical Avro-generation practices.
119-129: Builder creation methods are consistent with Avro patterns.
These methods ensure clarity by providing distinct ways to instantiate the Builder—either as a fresh instance or as a copy of an existing Builder. They facilitate safe deep copies of fields, as performed by Avro’s data().deepCopy mechanism, and pose no immediate issues.
130-138: Ensures flexible construction from existing object instances.
By allowing the creation of a Builder from an existing EPViewPerformance instance, the code streamlines updating record instances, reducing repetitive boilerplate. This pattern adheres to standard Avro best practices and appears correct.
backend/src/main/java/sparqles/avro/analytics/AvailabilityIndex.java (1)
17-19: The getClassSchema() method is standard Avro usage.
This static method correctly returns the Avro schema defined in SCHEMA$. This is typical for auto-generated Avro classes and helps maintain a consistent approach to schema retrieval.
backend/src/main/java/sparqles/analytics/AEvol.java (9)
11-11: Import usage looks appropriate
The new import for java.util.Collection is necessary for handling Collection<Endpoint> and appears properly utilized.
14-14: New Avro Endpoint import
Importing sparqles.avro.Endpoint aligns with the usage at line 37. No issues found.
27-27: No issues found
Constructor signature is straightforward, passing args for further initialization.
35-35: Comment only
The comment clarifies intent. No change needed.
37-37: Handle empty or null collection
dbm.get(Endpoint.class, Endpoint.SCHEMA$) may return an empty collection if no endpoints are registered. Confirm logic handles this scenario gracefully.
58-67: Verify rounding by dividing and multiplying timestamps by 1000
The time manipulation here truncates milliseconds. If preserving exact timestamps is necessary, ensure this is the intended behavior.
71-71: Double-check loop boundary condition
while (calNow.compareTo(cal) > 0) might skip edge cases. Verify it aligns with the intended month-based logic (e.g., does it handle partial months?).
91-92: No issue
The comment and loop structure are straightforward, iterating over the retrieved endpoints.
107-107: Duplicate query-string concatenation concern
Same as line 97: consider parameterizing or sanitizing the URI to prevent potential injection or malformed JSON.
backend/src/main/java/sparqles/avro/discovery/DResult.java (3)
15-16: Confirm schema reference consistency.
This schema snippet correctly references EndpointResult.avsc and aligns with the new data model.
17-19: Validate utility method naming and usage.
Providing getClassSchema() for Avro is a standard practice and improves clarity regarding schema retrieval.
141-156: Reintroduction of builder methods is appropriate.
These builder methods appear consistent with Avro's recommended pattern. They improve readability and maintain backward compatibility.
backend/src/main/java/sparqles/avro/analytics/EPView.java (9)
15-16: Check extended schema correctness.
Adding the new field definitions to the Avro schema string looks valid. Ensure careful versioning if older consumers still rely on the previous schema.
17-19: Validate dynamic schema access.
Using getClassSchema() is consistent with Avro patterns, ensuring type-safe schema references.
26-26: New field 'calculation' introduced.
This field adds analytics context to the object. Marked as @Deprecated suggests transitional or future cleanup. Verify correct usage and eventual removal plan.
40-41: Constructor updated for new field.
Ensuring the new calculation parameter is handled in the constructor fosters clarity for object initialization.
Also applies to: 47-47
67-68: Added retrieval and assignment logic for 'calculation'.
The new index case 5 in get and put is coherent. However, verify usage in all code paths to avoid potential Avro runtime mismatches.
Also applies to: 93-95
171-174: Getter for 'calculation'.
The straightforward getter provides external access. Confirm usage with testing to avoid null pointer scenarios if this field remains uninitialized.
Also applies to: 176-183
185-188: Builder pattern extended with 'calculation'.
The newly introduced builder segments maintain the usual Avro approach. Confirm that the calculation field is optional or mandatory, and handle defaults accordingly.
Also applies to: 190-195, 196-200, 201-201, 211-211
242-245: Builder copy constructors extended for 'calculation'.
This ensures that copying and deep copying logic includes the new field. Looks correct.
Also applies to: 272-275
407-410: Refined builder usage for 'calculation' field.
The set/get/clear chain is consistent with the rest of the fields. The final build stage assigns the field properly.
Also applies to: 412-419, 421-424, 426-431, 461-465
backend/src/main/java/sparqles/avro/analytics/Index.java (8)
15-16: Check schema modifications for new field.
The updated JSON string references IndexViewCalculation. Verify that consumers can handle the new schema gracefully.
17-19: Validate class-level schema retrieval.
getClassSchema() aligns with Avro best practices.
27-27: Field 'calculation' introduced.
Marking it as @Deprecated signals transitional usage. Confirm whether older systems can handle or ignore the new field.
42-43: Constructor extended to include 'calculation'.
Ensures comprehensive initialization flow.
Also applies to: 50-50
72-73: Adjust 'get' and 'put' logic for new index 6.
Properly enumerating fields keeps Avro indexing consistent. Confirm all references for potential off-by-one errors.
Also applies to: 101-103
193-196: Getter and setter for 'calculation'.
Simple additions that follow the same pattern as existing fields.
Also applies to: 198-205, 207-210, 212-216, 218-222
234-234: Deep copy enhancements in builder.
Deep copying the new field avoids referencing mutable objects.
Also applies to: 269-272, 303-306
462-465: Builder logic extended for 'calculation'.
No issues spotted. Final assignment in build() is consistent.
Also applies to: 467-474, 476-479, 481-486, 520-524
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (8)
20-20: New import statement for EPViewCalculation.
Ensures the appropriate type is recognized. No immediate concerns.
30-32: Additional imports for IndexViewCalculation classes.
This indicates broader analytics coverage. Looks correct.
49-50: Private MongoDBManager field introduced.
Allows direct DB access. Confirm concurrency handling if used from multi-threaded tasks.
97-98: New Count arrays for calculation stats.
Indices 0,1,2 track placeholders for different metrics. Clearly factor in appropriate array usage to avoid confusion.
111-111: Ensuring discoverability/calc calls in iteration.
Integration of discoverability and calculation analysis is consistent with existing patterns.
Also applies to: 114-115
127-127: Updating index with new stats.
updateCalculation(idx, calcStats) flows well with the rest of the pipeline.
Also applies to: 130-132
168-243: updateCalculation method.
Assigns generated stats into IndexViewCalculation. The use of TreeSets for sorting is effective. Confirm correctness of fractions when dividing by getTotal(), especially if total is zero.
649-659: Initialization of IndexViewCalculation in createIndex.
Provides default -1 values to indicate no data. This is aligned with the usage pattern seen in the rest of the file.
backend/src/main/avro/Run.avsc (1)
1-14: Consider field naming consistency and verify data types.
- The field
"Exception"starts with a capital letter, whereas other fields start with lowercase letters. Renaming"Exception"to"exception"could improve consistency. - The chosen data types (e.g.,
longfor times,intfor solutions) seem appropriate. However, ensure they align with how they're populated in code.
backend/src/main/avro/AResult.avsc (1)
3-3: Validate the new import directive.
Avro’s handling of imports can diverge by version. Verify that "import": "EndpointResult.avsc" is effective in your build and Avro tooling. If not recognized, consider referencing the schema via namespace or placing it in a known schema-lookup path.
backend/src/main/avro/Schedule.avsc (1)
11-11: Ensure backward compatibility with the addition of the “CTask” field.
Consider specifying a default value (e.g., null) to minimize migration friction, especially if existing code expects all task fields to be present.
backend/src/main/avro/PResult.avsc (2)
4-5: Use Avro schema references carefully.
Avro traditionally doesn't support "import" in the JSON schema syntax by default. Some Avro tooling might allow custom fields, but please verify your build pipeline/tooling supports these references. Alternatively, you can keep these lines as metadata to help your tooling but ensure that the actual Avro references are set up properly elsewhere if needed.
If these "import" statements are indeed recognized by your Avro library, confirm the docs or test with your build to ensure everything resolves properly.
15-15: Consistency of record references.
The reference to "sparqles.avro.performance.Run" for the cold field looks good. Ensure that other references to Run in the codebase now consistently use the same fully qualified name rather than inline record definitions.
backend/src/main/avro/CResult.avsc (1)
1-28: Schema design looks clear.
The new CResult record has well-defined fields for endpoint data and partial VoID/SD details. This design fosters clarity and extensibility. Ensure each field is genuinely used, and that test coverage verifies all critical fields (e.g., coherence, RS).
Optionally, consider adding doc fields for each property or including field-level comments describing the usage of those fields in the schema. This makes the schema more self-documenting.
backend/src/main/java/sparqles/core/CONSTANTS.java (1)
14-14: New constant introduced.
CTASK aligns with the newly added task type. Ensure all references to CTASK are thoroughly tested and integrated—particularly in scheduling logic or task creation factories.
backend/src/main/avro/DResult.avsc (1)
4-4: Confirm Avro import path correctness
Double-check that the "import": "EndpointResult.avsc" path matches the file location and naming conventions in your build environment.
backend/src/main/java/sparqles/core/TaskFactory.java (3)
10-10: Import for CAnalyser
The import statement for sparqles.analytics.CAnalyser appears correct. Ensure it's utilized effectively.
18-18: Import for CTask
The import for CTask enables creation of the new calculation task type. Confirm that future references to CTask reside in the sparqles.core.calculation package.
51-53: New CTask branch creation
The logic for instantiating CTask and CAnalyser looks consistent with existing patterns for other tasks (PTASK, ATASK, etc.). Ensure that any additional initialization requirements for CAnalyser are met elsewhere in the code.
backend/src/main/java/sparqles/analytics/CAnalyser.java (3)
1-2: New class declaration
Introduces CAnalyser in the sparqles.analytics package. The package placement seems appropriate for analytics-related functionality.
14-19: Constructor delegates to superclass
The constructor properly delegates the MongoDBManager to the Analytics superclass. This is clean and straightforward.
21-68: analyse(CResult) method
- Logs the endpoint, retrieves or creates the
EPViewandCalculationView, and then updates them with theCResultdata. This is consistent with the existing analytics pattern. - Consider handling any malformed or null values from
presto avoid potential NullPointerExceptions.
backend/src/main/java/sparqles/analytics/Analytics.java (3)
13-13: Addition of EPViewCalculation import
Imported class allows storing calculation-specific data on the EPView. This import aligns with linking the CalculationView model to the analytics layer.
34-34: Logger variable rename
Renaming Log to log matches standard naming conventions in SLF4J.
46-46: Warning on unexpected EPView count
A direct warning is helpful, but consider whether multiple EPViews for a single endpoint indicates a data integrity issue worth throwing an exception or cleaning up duplicates.
backend/src/main/avro/EPView.avsc (1)
128-154: Introduce calculation field for analytical metrics.
These newly added fields expand the schema to capture richer endpoint statistics. Make sure to update any producer or consumer code to handle the new data, ensuring proper handling in serialization, deserialization, and any existing business logic.
backend/src/main/avro/Index.avsc (1)
94-94: Confirm partial record closure or structural consistency.
Line 94 shows the end of a nested record definition. Verify there's no missing bracket or mismatch with the removed/renamed fields around this point to ensure backwards compatibility with consumers reading the old schema version.
backend/src/main/java/sparqles/utils/cli/ARGUMENTS.java (4)
18-38: New help-related constants and OptionGroup initialization.
The code cleanly groups help options. Good job keeping them consistent. Just ensure no overlapping short/long param usage with other CLI options to avoid confusion.
43-54: Add new flags for advanced CLI operations.
The parameters (PARAM_FLAG_DEBUG, PARAM_FLAG_INIT…) enhance functionality. Please confirm that the usage instructions in any README or documentation reflect these.
129-137: Introduce OPTION_UPDATE_EPS.
Ensure this aligns with the new logic for updating endpoints. Consider clarifying if “update datahub list” differs from re-initializing or rescheduling.
147-154: Add OPTION_ADD for endpoint creation.
Providing three arguments for the endpoint addition is valuable. Double-check input validation for these arguments in the CLI handling code.
backend/src/main/java/sparqles/analytics/AnalyserInit.java (4)
10-10: Add import for CResult.
This import is consistent with the new calculation analysis. Confirm there's no excessive coupling with other classes.
41-41: Initialize CAnalyser.
This is consistent with the new calculation step. Ensure it’s only created once to prevent overhead.
51-51: Invoke calculation(ep, c).
This addition ensures calculations are analyzed. Confirm that the method is invoked in the correct order and concurrency safety is maintained if run in parallel.
166-193: New private method calculation(Endpoint ep, CAnalyser c).
- Logic & Flow: Using a
TreeSetto maintain results in chronological order is good. Mind the comparator performance if results are large. - _onlyLast flag: The early return patterns for “only last result” look consistent with other analysis methods, ensuring uniform logic.
- Validation: Confirm that a
nullor empty result set is handled gracefully. - Logging: The final log statement is helpful. Consider including additional details about the analysis outcome, if relevant.
backend/src/main/java/sparqles/avro/Dataset.java (4)
17-19: Minor clarity note on getClassSchema() method
It’s standard for Avro-generated classes to include a getClassSchema() method. This looks correct and consistent with other Avro classes.
95-99: Builder creation method re-introduction
Re-adding the static newBuilder() method is good for ensuring consistent instantiation of Dataset objects.
100-104: Builder copy-constructor from another Builder
Reintroducing this overload helps with creating specialized copies without reconstructing the entire object.
105-109: Builder copy-constructor from Dataset
This provides a convenient way to clone an existing record. Matches common Avro patterns.
backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityDataPoint.java (4)
17-19: No issues with getClassSchema() method
This re-introduced method is Avro boilerplate for retrieving the record schema.
95-99: Reintroduction of newBuilder() method
Ensures a standard Avro builder pattern for EPViewAvailabilityDataPoint.
100-105: Builder copy-constructor from another Builder
Provides a convenient route for advanced copying. Implementation aligns with Avro’s code-generation guidelines.
106-113: Builder copy-constructor from EPViewAvailabilityDataPoint
Consistent approach to duplicate instances. This is beneficial for partial references or transformations.
backend/src/main/java/sparqles/avro/Endpoint.java (4)
17-19: No issues with getClassSchema() method
This is consistent with standard Avro practice, providing direct schema access.
95-99: Reintroduced newBuilder()
This static method is crucial for building Endpoint objects in a straightforward manner.
100-104: Builder copy from existing Builder
Common Avro pattern for creating new builder instances from existing ones.
105-109: Builder copy from Endpoint
Matches Avro’s recommended approach for replicating records with or without modifications.
backend/src/main/java/sparqles/utils/cli/SPARQLES.java (5)
4-4: Additional imports appear valid
These imported classes (URISyntaxException, List, Dataset, CResult, EndpointFactory) match the usage in the code below.
Also applies to: 6-6, 15-15, 18-18, 24-24
61-61: New command-line option
Adding ARGUMENTS.OPTION_ADD extends CLI usage to add new endpoints.
106-112: New conditional block for adding endpoints (PARAM_ADD)
This extension to the CLI is a logical approach for simplifying endpoint addition without re-initializing the entire system.
136-138: Addition of CTASK handling
Introducing CONSTANTS.CTASK expands the CLI’s ability to run a new CResult one-time execution. This aligns with the new calculation tasks.
180-196: Improvements in addEndpoint method
- Logical flow: Checking for an optional label is a good idea.
- Dataset assignment: Properly attaches a new
Datasetto theEndpointif a label is specified. - Error handling: Logging
URISyntaxExceptionwarnings is helpful for diagnosing invalid URIs.
backend/src/main/java/sparqles/avro/analytics/IndexViewCalculationDataValues.java (2)
1-5: Looks good overall, note that it’s auto-generated.
This file is clearly marked as “Autogenerated by Avro,” so any direct modifications might be overwritten in the future. If additional business logic or custom annotations are needed, consider creating wrapper classes or hooks.
21-23: Use caution when modifying deprecated fields.
The @Deprecated annotation on label and value indicates they may be removed or changed in future schema revisions. If your logic depends on these fields, ensure forward compatibility or plan for an eventual schema migration.
backend/src/main/java/sparqles/schedule/Scheduler.java (1)
58-58: Confirm scheduling frequency for new task.
You have added taskSchedule.put(CTASK, CRON_EVERY_SUN_AT_2330), which schedules the CTask weekly on Sundays at 23:30. Confirm if this frequency is correct, as it might cause a backlog if the task takes too long or if analysis is required more frequently.
backend/src/main/java/sparqles/avro/features/FResult.java (2)
15-16: Schema changes require careful versioning and migrations.
The schema has been updated to reference EndpointResult.avsc instead of Endpoint.avsc. Ensure that all downstream consumers of FResult are updated and tested to avoid deserialization conflicts.
105-120: Builder reintroduction is consistent with Avro best practices.
These builder methods (e.g., newBuilder()) significantly simplify record creation and maintenance of default field values. Keep them in sync if the Avro schema evolves further.
backend/src/main/java/sparqles/avro/analytics/IndexViewCalculationData.java (2)
1-5: Auto-generated Avro class disclaimer.
As with other Avro-generated classes, avoid making manual changes here. Consider extending or wrapping this class if you need additional logic that shouldn’t be overwritten on regeneration.
21-24: Deprecated fields usage.
Fields key and values are marked @Deprecated. Ensure no references exist in your code that rely excessively on these field names if future schema updates remove or rename them.
backend/src/main/java/sparqles/avro/analytics/EPViewInteroperability.java (4)
17-19: Looks consistent with Avro boilerplate.
The getClassSchema() method is reintroduced and aligns with standard Avro expectations.
105-108: Reintroduction of default newBuilder() method appears valid.
This is the standard Avro pattern for instantiating a builder.
110-114: Copy-based newBuilder(...) design is standard Avro usage.
Reintroducing this overload ensures the ability to copy builder states accurately.
116-123: Overload for copying an instance is appropriate.
This builder provides flexibility in constructing Avro records from existing objects.
backend/src/main/java/sparqles/avro/analytics/EPViewInteroperabilityData.java (4)
17-19: Schema retrieval method is correct.
Returning SCHEMA$ ensures Avro can use the record’s schema version internally.
119-122: newBuilder() method reintroduction confirmed.
The no-argument builder instantiation is aligned with Avro’s typical approach.
124-128: Builder copy constructor for builder object is appropriate.
Allows reusing partially built states for new instances.
130-137: Builder copy constructor for record object is appropriate.
Maintains the standard pattern for Avro generated classes.
backend/src/main/java/sparqles/avro/analytics/IndexViewInterData.java (4)
17-19: Avro schema retrieval method is properly declared.
Matches the typical Avro pattern.
119-122: New default builder creation is standard.
Ensures code is consistent with Avro generation practices.
124-128: Overloaded builder for copying builder states.
Promotes consistent reuse without rebuilding from scratch.
130-137: Overloaded builder for copying an existing record.
Allows convenient reconstruction and modification of existing data.
backend/src/main/java/sparqles/avro/analytics/EPViewPerformanceData.java (4)
17-19: getClassSchema() method follows Avro conventions.
No concerns here.
119-122: Builder creation reinstated.
Avro typically provides a no-arg builder for record initialization.
124-128: Copy-based builder constructor aligns with Avro norms.
No issues found with this approach.
130-137: Builder from record instance is a valid re-addition.
This reintroduction preserves standard Avro building patterns.
backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverability.java (2)
127-145: Confirm builder pattern usage.
These new builder methods accurately follow Avro's builder pattern, facilitating safe object creation. Just ensure that all older references (if any) to deprecated builders are updated to call these new methods.
16-19: Ensure the consistency of Avro schema retrieval methods across classes.
Adding getClassSchema() is consistent with Avro's standard patterns. Please verify that all other related Avro classes also utilize a similar approach for retrieving their schema.
✅ Verification successful
Schema retrieval method is consistently implemented across all Avro classes
The verification shows that the getClassSchema() method is consistently implemented across all Avro classes in the codebase, following the same pattern of returning SCHEMA$. This includes all classes under:
sparqles.avro.analytics.*sparqles.avro.availability.*sparqles.avro.core.*sparqles.avro.discovery.*sparqles.avro.features.*sparqles.avro.performance.*sparqles.avro.schedule.*- Root level Avro classes like
Dataset,Endpoint, etc.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# This script checks for getClassSchema() usage across all Avro classes
rg -A 3 "public static org\.apache\.avro\.Schema getClassSchema"
Length of output: 17715
backend/src/main/java/sparqles/avro/analytics/IndexViewDiscoverability.java (2)
17-19: Verify removal and re-addition of schema method.
getClassSchema() was removed and reintroduced. Ensure no external references to the removed versions exist. If so, update them to this new definition to avoid compilation issues.
146-164: Builder constructor reintroduction looks good.
Reintroducing these overloaded builder methods helps maintain a consistent approach for object creation across the Avro analytics classes. No issues spotted.
backend/src/main/java/sparqles/avro/analytics/DiscoverabilityView.java (1)
16-19: Consistent getClassSchema() method.
The code aligns with Avro conventions for exposing the class schema. Make sure all references use this method rather than SCHEMA$ directly.
backend/src/main/java/sparqles/avro/analytics/AvailabilityView.java (2)
16-19: Schema accessor consistency.
As with other classes, adding getClassSchema() fosters uniformity. Good practice for Avro-based serialized classes.
163-180: Builder re-addition complements Avro records.
These newly reintroduced builder methods restore a convenient API. Confirm no legacy code references the removed builder APIs.
backend/src/main/java/sparqles/avro/discovery/QueryInfo.java (2)
16-19: Good addition of the static getClassSchema() method.
This is consistent with Avro's conventions for exposing a class-level schema.
163-178: Implementation of new builder methods looks good.
These overloaded newBuilder() methods follow Avro patterns to create or copy QueryInfo instances.
backend/src/main/java/sparqles/avro/availability/AResult.java (2)
15-19: SCHEMA$ update and public getClassSchema() method introduced.
This helps unify schema handling across Avro classes.
185-200: Builder methods align with Avro code-generation conventions.
They allow flexible instantiation and copying of AResult objects.
backend/src/main/java/sparqles/avro/analytics/EPViewAvailability.java (2)
16-19: getClassSchema() re-introduced for Avro compliance.
Exposing the schema in a static method is a standard Avro pattern.
207-225: New builder methods ensure consistent creation and copying of EPViewAvailability objects.
No functional issues spotted.
backend/src/main/java/sparqles/avro/discovery/RobotsTXT.java (2)
16-19: Reintroducing getClassSchema() improves Avro schema access.
No concerns with the static method usage.
207-222: Builder pattern for RobotsTXT is properly re-added.
This matches the approach in other Avro-generated classes, allowing straightforward object creation and copying.
backend/src/main/java/sparqles/avro/schedule/Schedule.java (22)
15-16: Avro schema updated successfully with CTask.
No major issues. This change aligns with the addition of the CTask field.
17-20: Restored getClassSchema() method.
No functionality concerns; restoring or retaining this method is standard for Avro-generated classes.
26-26: Deprecated field CTask declared.
Review whether it really needs the @Deprecated annotation if it's newly added and intended for use.
43-51: All-args constructor includes CTask.
Implementation looks consistent with other Avro fields. Ensure all usage sites are updated to call this constructor with the new parameter.
74-77: Extended get(int field$) logic for CTask, ITask, and ETask.
Index assignment is correct. Just confirm that the field indices match the Avro schema ordering exactly.
104-108: Extended put(int field$, Object value$) logic for CTask and ITask.
Correct typecasting to java.lang.CharSequence. Implementation appears consistent with other fields.
187-200: Getter and setter for CTask.
These methods look correct and are consistent with Avro naming conventions.
229-233: New method newBuilder() for Schedule.
No issues; standard Avro builder pattern.
234-239: newBuilder(Schedule.Builder other) copy constructor.
Implementation is aligned with typical Avro builder patterns. No issues found.
240-244: newBuilder(Schedule other) copy constructor.
Same as above; consistent with other builder creation methods.
255-255: Builder field for CTask.
Field declared properly; no issues.
287-296: Logic for copying CTask, ITask, and ETask from another builder.
Ensure the field indices (5, 6, 7) accurately match the updated order in the schema to avoid runtime errors.
463-486: Builder methods (get/set/has/clear) for CTask.
All methods adhere to Avro patterns. No issues regarding correctness or logic.
495-495: validate(fields()[6], value) in setITask.
No concerns, but confirm the index aligns with “ITask” in the schema (index 6).
503-503: hasITask() returning fieldSetFlags()[6].
Matches the “ITask” index. Ensure consistency with Avro generator logic.
509-509: clearITask() resetting fieldSetFlags()[6].
Implementation aligns with Avro. No issues found.
520-520: validate(fields()[7], value) in setETask.
Check index usage for consistency with “ETask” as index 7.
528-528: hasETask() returning fieldSetFlags()[7].
Index matches the “ETask” definition.
534-534: clearETask() resetting fieldSetFlags()[7].
Implementation consistent with Avro patterns. No issues found.
562-566: Assigning CTask during builder build().
This code properly handles the presence or default of CTask.
568-568: Assigning ITask quickly after CTask.
No issues; just an Avro pattern, verifying index consistency is recommended.
570-573: Assigning ETask completing the build.
Fully consistent with the Avro pattern.
backend/src/main/java/sparqles/avro/analytics/CalculationView.java (30)
1-7: Class CalculationView introduced.
Overall structure is standard Avro code, automatically generated. No immediate issues spotted.
8-11: Class-level annotations and inheritance.
Implements SpecificRecord as expected. No further action needed.
12-16: Static schema reference for CalculationView.
Schema addition is correct and references a newly parsed Avro structure with relevant fields.
17-20: getClassSchema() method.
No concerns; standard Avro boilerplate.
21-28: Deprecated public fields for Avro.
Though typical for Avro, consider removing @Deprecated if these fields remain actively used.
36-54: All-args constructor with new fields.
The constructor properly initializes newly introduced fields. Ensure all references are updated to pass correct arguments.
60-82: get(int field$): returning data by index.
Indexes match the schema fields. Verify no off-by-one errors for new fields.
84-115: put(int field$, Object value$): assigning data by index.
The switch cases for new fields look consistent. Confirm boolean/double/long type casts remain valid.
117-130: Getter/setter pair for endpoint.
Correctly follows typical Avro patterns. No issues found.
145-158: Getter/setter pair for VoIDPart.
Similar to VoID handling. The naming is slightly cryptic but consistent with the schema.
159-172: Getter/setter pair for SD.
No issues; standard approach.
173-185: Getter/setter pair for SDPart.
Same pattern as above. All good.
187-200: Getter/setter pair for coherence.
Type-casting to double is consistent with the schema. No issues.
201-214: Getter/setter pair for RS.
No issues; straightforward double field handling.
215-227: Getter/setter pair for lastUpdate.
Uses a long field. Implementation is correct.
229-233: newBuilder() method for CalculationView.
No concerns; typical Avro approach.
234-246: Additional newBuilder(...) methods.
Copying approach is consistent with Avro’s builder pattern; no issues.
248-251: Builder class declaration.
Implements SpecificRecordBuilderBase as well as standard builder interface.
253-265: Builder fields and constructor.
Fields match new analytic fields. No problems found.
267-302: Builder copy-constructor logic for new fields.
Deep copying correct. Validate field and index consistency with the added fields.
304-339: Builder constructor from CalculationView.
Similar deep copy logic. No immediate concerns beyond verifying indexes.
341-365: Getter/Setter/clear methods for endpoint.
Matches typical Avro code.
367-390: Getter/Setter/clear methods for VoID.
Implementation is consistent. Check that boolean/double usage in the rest of the code is correct.
391-414: Getter/Setter/clear methods for VoIDPart.
Same pattern as VoID.
415-438: Getter/Setter/clear methods for SD.
No issues found.
439-461: Getter/Setter/clear methods for SDPart.
Matches Avro pattern.
463-485: Getter/Setter/clear methods for coherence.
No issues; double field is typed consistently.
487-509: Getter/Setter/clear methods for RS.
Standard usage.
511-533: Getter/Setter/clear methods for lastUpdate.
Implementation is correct for a long field.
535-574: build() method constructing CalculationView.
All fields are assigned properly. Confirm default values if they exist.
backend/src/main/java/sparqles/avro/analytics/IndexViewCalculation.java (29)
1-7: Class IndexViewCalculation introduced.
Auto-generated Avro code for index calculations. No major concerns.
8-11: Class-level annotations and structure.
Implements SpecificRecord. Matches Avro best practices.
12-16: Static schema definition for IndexViewCalculation.
Schema references new fields such as coherences and rss. Implementation is standard.
17-20: getClassSchema() method.
No issues found. Standard Avro style.
36-54: All-args constructor with new analysis fields.
Looks correct. Make sure to handle null-safety for lists (coherences, rss) in the calling code.
60-82: get(int field$) logic.
Index-based retrieval is consistent. Validate indexes if the schema changes further.
84-116: put(int field$, Object value$) logic.
Covers new array and double fields. Check for potential ClassCastException if usage is dynamic.
118-132: Getter/setter pair for coherences.
Ensure the calling code handles empty lists gracefully.
133-146: Getter/setter pair for rss.
Implementation parallel to coherences.
147-160: Getter/setter pair for VoID.
Straightforward double assignment.
161-174: Getter/setter pair for VoIDPart.
Also standard double field usage.
175-188: Getter/setter pair for SD.
No immediate concerns.
189-202: Getter/setter pair for SDPart.
Same approach; consistent with other double fields.
203-216: Getter/setter pair for Coherence.
Return and set for double field. No issues.
217-230: Getter/setter pair for RS.
Standard usage.
231-234: newBuilder() method for IndexViewCalculation.
Avro standard approach.
236-249: Other newBuilder(...) overloads.
Builder creation from existing objects or builders. No concerns.
251-254: Builder class declaration.
Implements SpecificRecordBuilderBase. Typical for Avro.
256-264: Builder fields for new lists and double fields.
All fields match the schema with correct types (lists/doubles).
266-305: Builder copy-constructor from another builder and from an existing instance.
Properly deep copies new fields. Validate index usage matches the added fields.
344-369: Builder methods for coherences.
Set/clear methods consistent with Avro.
370-395: Builder methods for rss.
Parallel structure to coherences.
396-418: Builder methods for VoID.
Double field assignment. Looks fine.
420-442: Builder methods for VoIDPart.
Same pattern. No issues.
444-466: Builder methods for SD.
No concerns; typical Avro code.
468-490: Builder methods for SDPart.
Consistent approach.
492-514: Builder methods for Coherence.
Straightforward double usage.
516-538: Builder methods for RS.
No issues discovered.
540-580: build() method for IndexViewCalculation.
All fields assigned with fallback to default if not set. Check for null list handling, e.g., coherences, rss.
backend/src/main/java/sparqles/utils/MongoDBManager.java (12)
37-37: Importing CalculationView.
This import aligns with newly supported records for “calculation” tasks.
44-44: Importing CResult.
Used to persist new calculations in MongoDB.
59-59: Added COLL_CALC = "ctasks".
Defines new collection for storing raw calculation tasks.
66-66: Added COLL_CALC_AGG = "ctasks_agg".
Defines aggregated calculations collection.
74-75: Introduced MongoClient client and DB db fields.
Repositioned or newly assigned at class level. No direct issues, but confirm thread-safety if multi-thread usage is expected.
82-82: obj2col.put(CResult.class, new String[] {COLL_CALC, RESULT_KEY});
Associates CResult with new ctasks collection.
93-93: obj2col.put(CalculationView.class, new String[] {COLL_CALC_AGG, VIEW_KEY});
Maps CalculationView to ctasks_agg for aggregated results.
124-124: COLL_CALC_AGG included in index creation array.
Ensures a unique index on endpoint.uri. Good for data integrity.
161-166: Added COLL_CALC_AGG in initAggregateCollections().
Collection is dropped and re-initialized with an index on endpoint.uri.
262-268: Conditional update for CalculationView.
Keeps this consistent with the other *View classes. No issues found.
595-595: Ensuring CalculationView removal in cleanup(Endpoint ep).
Maintains data consistency if an endpoint is removed.
640-659: New method getFirstAvailabitlityTime().
Retrieves earliest start time from COLL_AVAIL. Implementation is correct, but watch for potential exceptions if the collection is empty.
backend/src/main/java/sparqles/avro/discovery/DGETInfo.java (2)
16-19: No issues spotted with the new method.
This newly introduced getClassSchema() method aligns with Avro's typical approach for returning static schema references. It is properly referencing SCHEMA$.
295-310: Builders re-introduced as expected.
Reintroducing these newBuilder() methods is essential for standard Avro usage, enabling a flexible record-building process. The methods correctly reference the underlying Builder class.
backend/src/main/java/sparqles/avro/analytics/EPViewCalculation.java (2)
1-82: Auto-generated Avro code - no obvious issues.
This class is properly structured for an Avro record with standard getters and setters, a default constructor, and an all-args constructor. The newly introduced fields (e.g., triples, entities, coherence, etc.) appear well-defined with matching getter and setter methods. Generally, Avro-generated classes should not be manually edited, so no additional concerns here.
83-196: Builder usage is standard.
The nested Builder class follows standard Avro patterns, validating and setting fields as expected. No obvious issues found with concurrency or data consistency.
backend/src/main/java/sparqles/avro/calculation/CResult.java (2)
1-82: Auto-generated Avro class is consistent with best practices.
All fields (e.g., endpointResult, triples, entities, coherence) align with the schema. The default and all-args constructors, plus the getters and setters, are congruent with typical Avro usage. No issues detected.
83-196: Builder class logic is sound.
The Builder properly validates, deep copies, and sets fields, preserving immutability in alignment with Avro. Everything looks good here.
backend/src/main/resources/sparqles.properties (4)
6-11: Verify queries folder usage.
You’re referencing both ftask.queries=ftask/ and ptask.queries=ptask/. If these directories do not exist or differ by environment, ensure the code checks or creates them as needed.
14-14: High concurrency settings.
task.threads=64 may be acceptable for production but could be excessive for lower-resource environments. Confirm the hardware resources can handle 64 threads simultaneously.
21-21: Local database config.
Using db.host=localhost is typically fine for local dev. Ensure production deployments override this property, especially if Docker or a remote database host is in use.
Line range hint 25-25: Validate wait time.
waittime=290 is quite specific. Double-check that this aligns with system resource assumptions and does not affect overall throughput for SPARQL queries.
backend/src/main/resources/sparqles_docker.properties (5)
1-6: LGTM: Base directory configuration follows Docker best practices
The base SPARQLES directory and data directory are properly configured for a containerized environment.
12-14: Consider resource implications of high thread count
Setting task.threads=64 could lead to high resource utilization. Please ensure that:
- The target environment has sufficient resources to handle this many concurrent threads
- This number aligns with the expected load from the new CTask implementation
Consider implementing a more dynamic thread pool size based on system resources or making this value configurable through environment variables for different deployment scenarios.
15-21: LGTM: Database configuration follows container networking practices
The database host configuration properly uses Docker service discovery with database-svc instead of localhost.
Line range hint 22-27: Verify wait time configuration for new CTask
The general wait time of 290ms is set, but we should verify if the new CTask requires specific rate limiting.
✅ Verification successful
Let me gather more information about how the wait time is used in the tasks.
Let me check one more thing to see if CTask has any specific rate limiting requirements.
Wait time configuration is appropriate for CTask
The wait time configuration is correctly set up and suitable for CTask:
- The general wait time of 290ms is inherited by all tasks including CTask through
SPARQLESProperties.SPARQL_WAITTIME - CTask extends
EndpointTaskand doesn't implement any custom rate limiting, so it uses the default wait time - The configuration file allows for task-specific overrides if needed in the future via
ptask.waittimeandftask.waittime
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for wait time usage in CTask implementation
ast-grep --pattern 'class CTask {
$$$
waittime
$$$
}'
# Look for any rate limiting or wait time references in calculation-related files
rg -l "(?i)wait.*time|rate.*limit" --type java
Length of output: 593
Script:
#!/bin/bash
# Check how wait time is used in task implementations
rg -A 5 "waittime" backend/src/main/java/sparqles/core/
# Check SPARQLESProperties to understand wait time configuration
rg -A 5 "waittime" backend/src/main/java/sparqles/core/SPARQLESProperties.java
Length of output: 1538
Script:
#!/bin/bash
# Search for CTask implementation and its rate limiting/wait time usage
rg -A 10 "class CTask" backend/src/main/java/sparqles/core/
Length of output: 1283
7-11: Verify task configuration completeness
With the introduction of the new CTask mentioned in the PR summary, we should verify if any additional task-related properties are required.
✅ Verification successful
No additional task configuration needed for CTask
After reviewing the implementation, I can confirm that CTask does not require any additional configuration properties. The CTask class:
- Performs SPARQL endpoint calculations without relying on external query files
- Contains all required SPARQL queries as hardcoded constants within the class
- Uses the existing QueryManager infrastructure without needing additional configuration
The current properties file configuration with ftask.queries and ptask.queries is sufficient, and no changes are needed to support CTask functionality.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for CTask-related configuration properties in the codebase
rg -l "ctask\.(?:queries|config)"
# Look for CTask property usage in Java files
ast-grep --pattern 'getProperty("ctask.$_")'
Length of output: 176
Script:
#!/bin/bash
# Let's search for any mentions of CTask in the codebase
rg -l "CTask"
# Also search for task-related configuration patterns
rg -l "task\.(?:queries|config)"
# Look for property loading in Java files
ast-grep --pattern 'load($$$, "properties")'
Length of output: 710
Script:
#!/bin/bash
# Let's check the CTask implementation to understand if it requires any configuration
cat backend/src/main/java/sparqles/core/calculation/CTask.java
# Check the properties file for all task configurations
cat backend/src/main/resources/sparqles.properties
# Check property loading in SPARQLESProperties
cat backend/src/main/java/sparqles/core/SPARQLESProperties.java
Length of output: 31104
| # Log all information for ATask log messages to a log file | ||
| log4j.appender.alog=org.apache.log4j.DailyRollingFileAppender | ||
| log4j.appender.alog.DatePattern = '.'yyyy-MM-dd | ||
| log4j.appender.alog.Append = true | ||
| log4j.appender.alog.Threshold=INFO | ||
| log4j.appender.alog.File=logs/availability.log | ||
| log4j.appender.alog.layout=org.apache.log4j.PatternLayout | ||
| log4j.appender.alog.layout.ConversionPattern=%d{dd-MM-yy HH:mm:ss} %15.15c{1}:%-3.3L %5p - %m%n | ||
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| # Log all information for ATask log messages to exception log file | ||
| log4j.appender.exlog=org.apache.log4j.DailyRollingFileAppender | ||
| log4j.appender.exlog.DatePattern = '.'yyyy-MM-dd | ||
| log4j.appender.exlog.Append = true | ||
| log4j.appender.exlog.Threshold=INFO | ||
| log4j.appender.exlog.File=logs/exception.log | ||
| log4j.appender.exlog.layout=org.apache.log4j.PatternLayout | ||
| log4j.appender.exlog.layout.ConversionPattern=%d{dd-MM-yy HH:mm:ss} %15.15c{1}:%-3.3L %5p - %m%n | ||
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| # Log all information for ATask log messages to a log file | ||
| log4j.appender.flog=org.apache.log4j.DailyRollingFileAppender | ||
| log4j.appender.flog.DatePattern = '.'yyyy-MM-dd | ||
| log4j.appender.flog.Append = true | ||
| log4j.appender.flog.Threshold=INFO | ||
| log4j.appender.flog.File=logs/interoperability.log | ||
| log4j.appender.flog.layout=org.apache.log4j.PatternLayout | ||
| log4j.appender.flog.layout.ConversionPattern=%d{dd-MM-yy HH:mm:ss} %15.15c{1}:%-3.3L %5p - %m%n | ||
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| # Log all information for ATask log messages to a log file | ||
| log4j.appender.dlog=org.apache.log4j.DailyRollingFileAppender | ||
| log4j.appender.dlog.DatePattern = '.'yyyy-MM-dd | ||
| log4j.appender.dlog.Append = true | ||
| log4j.appender.dlog.Threshold=INFO | ||
| log4j.appender.dlog.File=logs/discoverability.log | ||
| log4j.appender.dlog.layout=org.apache.log4j.PatternLayout | ||
| log4j.appender.dlog.layout.ConversionPattern=%d{dd-MM-yy HH:mm:ss} %15.15c{1}:%-3.3L %5p - %m%n | ||
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| # Log all information for ATask log messages to a log file | ||
| log4j.appender.plog=org.apache.log4j.DailyRollingFileAppender | ||
| log4j.appender.plog.DatePattern = '.'yyyy-MM-dd | ||
| log4j.appender.plog.Append = true | ||
| log4j.appender.plog.Threshold=INFO | ||
| log4j.appender.plog.File=logs/performance.log | ||
| log4j.appender.plog.layout=org.apache.log4j.PatternLayout | ||
| log4j.appender.plog.layout.ConversionPattern=%d{dd-MM-yy HH:mm:ss} %15.15c{1}:%-3.3L %5p - %m%n |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Reduce configuration duplication and standardize naming
- There's significant duplication in appender configurations. All appenders share identical settings except for the file path.
- The naming is inconsistent: the logger uses
featuresbut the log file is namedinteroperability.log.
Consider:
- Using a property placeholder system to reduce duplication:
# Common settings
log4j.appender.common.DatePattern='.'yyyy-MM-dd
log4j.appender.common.Append=true
log4j.appender.common.Threshold=INFO
log4j.appender.common.layout=org.apache.log4j.PatternLayout
log4j.appender.common.layout.ConversionPattern=%d{dd-MM-yy HH:mm:ss} %15.15c{1}:%-3.3L %5p - %m%n
# Specific appenders inherit common settings
log4j.appender.alog=org.apache.log4j.DailyRollingFileAppender
log4j.appender.alog.File=logs/availability.log
# ... inherit common settings ...- Standardize the naming convention between loggers and file names:
-log4j.logger.sparqles.core.features=DEBUG, flog
+log4j.logger.sparqles.core.interoperability=DEBUG, ilog | // get number of avail and unavail tests | ||
| long nbAvail = | ||
| atasksColl.count( | ||
| "{'endpointResult.endpoint.uri': '" | ||
| + epArray[i].getUri() | ||
| + e.getUri() |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Parameterize or sanitize the query
Directly concatenating e.getUri() into the Mongo JSON string could introduce injection or parsing issues. Consider using parameterized queries or sanitizing inputs.
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Actionable comments posted: 6
🧹 Nitpick comments (28)
backend/src/main/java/sparqles/avro/analytics/DiscoverabilityView.java (3)
88-92: Consider adding validation for serverName fieldThe serverName field is declared as CharSequence without any validation constraints. Consider adding validation to ensure it's not null or empty, as this appears to be a critical identifier.
private java.lang.CharSequence serverName; + +/** + * Validates the server name. + * @throws IllegalArgumentException if the server name is null or empty + */ +private void validateServerName(CharSequence name) { + if (name == null || name.length() == 0) { + throw new IllegalArgumentException("Server name cannot be null or empty"); + } +}
642-654: Enhance error handling in endpoint builderThe endpoint builder's error handling could be improved by providing more context in the exception message about what specifically failed during the build process.
if (endpointBuilder != null) { try { record.endpoint = this.endpointBuilder.build(); } catch (org.apache.avro.AvroMissingFieldException e) { + String msg = String.format("Failed to build endpoint: %s", e.getMessage()); e.addParentField(record.getSchema().getField("endpoint")); + e.setMessage(msg); throw e; }
19-22: Consider documenting the schema structureThe schema definition is quite complex and would benefit from documentation explaining the structure and relationships between fields.
Add a comment block explaining the schema structure:
/** * Schema structure: * - endpoint: Contains URI and associated datasets * - uri: Endpoint URI * - datasets: Array of Dataset records * - uri: Dataset URI * - label: Dataset label * - VoID: Boolean flag for VoID availability * - SD: Boolean flag for Service Description availability * - serverName: Server identifier * - lastUpdate: Timestamp of last update */backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverabilityData.java (2)
72-86: Consider enhancing error messages for better debuggingWhile the serialization methods are correctly implemented, consider wrapping IOExceptions with more context about what failed during serialization/deserialization.
public java.nio.ByteBuffer toByteBuffer() throws java.io.IOException { - return ENCODER.encode(this); + try { + return ENCODER.encode(this); + } catch (IOException e) { + throw new IOException("Failed to serialize EPViewDiscoverabilityData: " + e.getMessage(), e); + } }
189-222: Consider adding validation for the label fieldWhile the builder implementation is robust, consider adding validation to ensure the label is not empty or just whitespace.
public sparqles.avro.analytics.EPViewDiscoverabilityData.Builder setLabel( java.lang.CharSequence value) { validate(fields()[0], value); + if (value != null && value.toString().trim().isEmpty()) { + throw new IllegalArgumentException("Label cannot be empty or whitespace"); + } this.label = value; fieldSetFlags()[0] = true; return this; }Also applies to: 281-294
backend/src/main/java/sparqles/avro/EndpointResult.java (1)
86-88: Consider consistent primitive type usageThe field declarations use primitive
longtypes, but some methods useLongwrapper types. This inconsistency could lead to unnecessary boxing/unboxing operations.Consider using primitive types consistently throughout the class:
- public void setStart(long value) { + public void setStart(Long value) { this.start = value; } - public void setEnd(long value) { + public void setEnd(Long value) { this.end = value; }Also applies to: 177-204
backend/src/main/java/sparqles/avro/analytics/IndexAvailabilityDataPoint.java (4)
61-63: Consider adding parameter validationThe
resolverparameter increateDecodershould be validated to prevent potentialNullPointerException.public static BinaryMessageDecoder<IndexAvailabilityDataPoint> createDecoder( SchemaStore resolver) { + if (resolver == null) { + throw new IllegalArgumentException("SchemaStore resolver cannot be null"); + } return new BinaryMessageDecoder<>(MODEL$, SCHEMA$, resolver); }
104-106: Fix constructor parameter type mismatchThe constructor parameter
yis declared asDouble(object) but the field isdouble(primitive). This could lead to unnecessary boxing/unboxing.-public IndexAvailabilityDataPoint(java.lang.CharSequence x, java.lang.Double y) { +public IndexAvailabilityDataPoint(java.lang.CharSequence x, double y) { this.x = x; this.y = y; }
368-372: Enhance error handling in build()The catch block for general exceptions could be more specific about the error that occurred.
} catch (org.apache.avro.AvroMissingFieldException e) { throw e; } catch (java.lang.Exception e) { - throw new org.apache.avro.AvroRuntimeException(e); + throw new org.apache.avro.AvroRuntimeException("Failed to build IndexAvailabilityDataPoint: " + e.getMessage(), e); }
408-432: Add validation in customDecodeConsider adding validation for the number of fields in the fieldOrder array to prevent potential array index out of bounds issues.
@Override public void customDecode(org.apache.avro.io.ResolvingDecoder in) throws java.io.IOException { org.apache.avro.Schema.Field[] fieldOrder = in.readFieldOrderIfDiff(); if (fieldOrder == null) { this.x = in.readString(this.x instanceof Utf8 ? (Utf8) this.x : null); this.y = in.readDouble(); } else { + if (fieldOrder.length != 2) { + throw new java.io.IOException("Invalid field order length. Expected 2 but got " + fieldOrder.length); + } for (int i = 0; i < 2; i++) { switch (fieldOrder[i].pos()) {backend/src/main/java/sparqles/avro/analytics/IndexViewCalculationData.java (1)
66-87: Serialization methods appear standard, but verify test coverage.
ThetoByteBufferandfromByteBuffermethods properly handle encoding and decoding. Ensure thorough testing around edge cases (e.g., empty values arrays, null fields) to confirm robust Avro (de)serialization.backend/src/main/java/sparqles/avro/Endpoint.java (2)
87-104: Consider making the datasets collection immutableTo prevent unintended modifications to the datasets collection, consider making it immutable upon construction.
public Endpoint(java.lang.CharSequence uri, java.util.List<sparqles.avro.Dataset> datasets) { this.uri = uri; - this.datasets = datasets; + this.datasets = datasets != null ? Collections.unmodifiableList(new ArrayList<>(datasets)) : null; }
400-416: Consider extracting array size validation to a utility methodThe array size validation logic could be extracted to a reusable utility method to improve maintainability and reusability.
+private void validateArraySize(long expected, long actual, String arrayName) { + if (actual != expected) { + throw new ConcurrentModificationException( + String.format("%s-size written was %d, but element count was %d.", + arrayName, expected, actual)); + } +} public void customEncode(org.apache.avro.io.Encoder out) throws java.io.IOException { // ... previous code ... - if (actualSize0 != size0) - throw new java.util.ConcurrentModificationException( - "Array-size written was " - + size0 - + ", but element count was " - + actualSize0 - + "."); + validateArraySize(size0, actualSize0, "Array"); }backend/src/main/java/sparqles/avro/analytics/IndexViewDiscoverability.java (2)
Line range hint
242-549: Add validation for serverName list elementsWhile the builder validates the list itself, consider adding validation for individual elements to ensure they're not null.
public sparqles.avro.analytics.IndexViewDiscoverability.Builder setServerName( java.util.List<sparqles.avro.analytics.IndexViewDiscoverabilityData> value) { validate(fields()[0], value); + if (value != null) { + for (IndexViewDiscoverabilityData item : value) { + if (item == null) { + throw new IllegalArgumentException("serverName list cannot contain null elements"); + } + } + } this.serverName = value; fieldSetFlags()[0] = true; return this; }
576-693: Consider memory optimization in customDecodeThe decoder pre-allocates arrays based on the size read from the input. For large arrays, this could lead to excessive memory allocation. Consider:
- Adding a reasonable size limit
- Using a growing strategy for very large arrays
if (a0 == null) { + // Add a reasonable size limit to prevent excessive memory allocation + if (size0 > Integer.MAX_VALUE || size0 < 0) { + throw new IOException("Invalid array size: " + size0); + } a0 = new SpecificData.Array<>( (int) size0, SCHEMA$.getField("serverName").schema() ); this.serverName = a0; }backend/src/main/java/sparqles/analytics/AnalyserInit.java (2)
60-62: Consider extracting the common comparator patternThe updates to use
Comparator.comparingLongimprove the code, but there's duplicate code across different result types. Consider creating a generic comparator factory method to reduce duplication.Example implementation:
private static <T> Comparator<T> createStartTimeComparator(Function<T, Long> startTimeExtractor) { return Comparator.comparingLong(startTimeExtractor); }Usage example:
new TreeSet<>(createStartTimeComparator(value -> value.getEndpointResult().getStart()));Also applies to: 87-89, 114-116, 142-144
163-188: Add method documentation for consistencyThe implementation looks good and follows the established pattern. Consider adding Javadoc to match the documentation style of other methods.
Add documentation like this:
/** * Computes the aggregated statistics for the Calculation task * @param ep The endpoint to analyze * @param c The calculation analyzer instance */ private void calculation(Endpoint ep, CAnalyser c) {backend/src/main/java/sparqles/avro/analytics/AvailabilityIndex.java (4)
412-429: Consider using AtomicInteger for size tracking in customEncode.The current implementation uses a size check that might throw
ConcurrentModificationExceptionif the list is modified during encoding. Consider usingAtomicIntegerfor more reliable concurrent modification detection.- long size0 = this.values.size(); - out.writeArrayStart(); - out.setItemCount(size0); - long actualSize0 = 0; + AtomicInteger size0 = new AtomicInteger(this.values.size()); + out.writeArrayStart(); + out.setItemCount(size0.get()); + AtomicInteger actualSize0 = new AtomicInteger(0); for (sparqles.avro.analytics.IndexAvailabilityDataPoint e0 : this.values) { - actualSize0++; + actualSize0.incrementAndGet(); out.startItem(); e0.customEncode(out); } out.writeArrayEnd(); - if (actualSize0 != size0) + if (actualSize0.get() != size0.get()) throw new java.util.ConcurrentModificationException( "Array-size written was " - + size0 + + size0.get() + ", but element count was " - + actualSize0 + + actualSize0.get() + ".");
431-506: Consider splitting customDecode into smaller methods.The
customDecodemethod is quite long (75+ lines) and handles multiple responsibilities. Consider extracting the array decoding logic into a separate private method for better maintainability.+ private void decodeValuesArray(org.apache.avro.io.ResolvingDecoder in) throws java.io.IOException { + long size0 = in.readArrayStart(); + java.util.List<sparqles.avro.analytics.IndexAvailabilityDataPoint> a0 = this.values; + if (a0 == null) { + a0 = new SpecificData.Array<sparqles.avro.analytics.IndexAvailabilityDataPoint>( + (int) size0, SCHEMA$.getField("values").schema()); + this.values = a0; + } else a0.clear(); + SpecificData.Array<sparqles.avro.analytics.IndexAvailabilityDataPoint> ga0 = + (a0 instanceof SpecificData.Array + ? (SpecificData.Array<sparqles.avro.analytics.IndexAvailabilityDataPoint>) a0 + : null); + for (; 0 < size0; size0 = in.arrayNext()) { + for (; size0 != 0; size0--) { + sparqles.avro.analytics.IndexAvailabilityDataPoint e0 = + (ga0 != null ? ga0.peek() : null); + if (e0 == null) { + e0 = new sparqles.avro.analytics.IndexAvailabilityDataPoint(); + } + e0.customDecode(in); + a0.add(e0); + } + } + } @Override public void customDecode(org.apache.avro.io.ResolvingDecoder in) throws java.io.IOException { org.apache.avro.Schema.Field[] fieldOrder = in.readFieldOrderIfDiff(); if (fieldOrder == null) { this.key = in.readString(this.key instanceof Utf8 ? (Utf8) this.key : null); - // ... existing array decoding logic ... + decodeValuesArray(in); } else { for (int i = 0; i < 2; i++) { switch (fieldOrder[i].pos()) { case 0: this.key = in.readString(this.key instanceof Utf8 ? (Utf8) this.key : null); break; case 1: - // ... existing array decoding logic ... + decodeValuesArray(in); break; default: throw new java.io.IOException("Corrupt ResolvingDecoder."); } } } }
129-129: Enhance error messages in IndexOutOfBoundsException.Consider adding field names to the error messages to make debugging easier.
- throw new IndexOutOfBoundsException("Invalid index: " + field$); + throw new IndexOutOfBoundsException("Invalid field index: " + field$ + ". Expected 0 (key) or 1 (values)");Also applies to: 146-146
377-381: Consider logging the exception details.The catch block for general exceptions could benefit from logging the exception details before wrapping it in
AvroRuntimeException.} catch (org.apache.avro.AvroMissingFieldException e) { throw e; } catch (java.lang.Exception e) { + LOG.error("Failed to build AvailabilityIndex: " + e.getMessage(), e); throw new org.apache.avro.AvroRuntimeException(e); }backend/src/main/java/sparqles/avro/analytics/IndexViewCalculationDataValues.java (3)
19-22: Consider enhancing the Avro schema documentationThe schema could benefit from additional documentation to clarify its purpose in the analytics calculations. Consider adding doc fields to describe:
- The purpose of the record in the context of statistics calculation
- The expected format/constraints for the label field
- The units and valid range for the value field
Example enhancement:
new org.apache.avro.Schema.Parser() .parse( "{\"type\":\"record\",\"name\":\"IndexViewCalculationDataValues\",\"namespace\":\"sparqles.avro.analytics\"," + "\"doc\":\"Represents a labeled numerical value in the analytics calculations\"," "\"fields\":[" - "{\"name\":\"label\",\"type\":\"string\"}," - "{\"name\":\"value\",\"type\":\"double\"}" + "{\"name\":\"label\",\"type\":\"string\",\"doc\":\"Identifies the metric being measured\"}," + "{\"name\":\"value\",\"type\":\"double\",\"doc\":\"The calculated metric value\"}" "]}");
72-74: Enhance error handling in serialization methodsThe ByteBuffer serialization methods could benefit from more specific error handling to help with debugging serialization issues.
Consider wrapping the IOException with more context:
public java.nio.ByteBuffer toByteBuffer() throws java.io.IOException { - return ENCODER.encode(this); + try { + return ENCODER.encode(this); + } catch (java.io.IOException e) { + throw new java.io.IOException("Failed to serialize IndexViewCalculationDataValues: " + e.getMessage(), e); + } } public static IndexViewCalculationDataValues fromByteBuffer(java.nio.ByteBuffer b) throws java.io.IOException { - return DECODER.decode(b); + try { + return DECODER.decode(b); + } catch (java.io.IOException e) { + throw new java.io.IOException("Failed to deserialize IndexViewCalculationDataValues: " + e.getMessage(), e); + } }Also applies to: 84-87
358-377: Add validation in build methodConsider adding validation in the build method to ensure both fields are set, as they appear to be required for analytics calculations.
public IndexViewCalculationDataValues build() { try { IndexViewCalculationDataValues record = new IndexViewCalculationDataValues(); + if (!fieldSetFlags()[0]) { + throw new IllegalStateException("Required field 'label' was not set"); + } + if (!fieldSetFlags()[1]) { + throw new IllegalStateException("Required field 'value' was not set"); + } record.label = fieldSetFlags()[0] ? this.label : (java.lang.CharSequence) defaultValue(fields()[0]); record.value = fieldSetFlags()[1] ? this.value : (java.lang.Double) defaultValue(fields()[1]); return record; } catch (org.apache.avro.AvroMissingFieldException e) { throw e; } catch (java.lang.Exception e) { throw new org.apache.avro.AvroRuntimeException(e); } }backend/src/main/java/sparqles/avro/analytics/EPViewInteroperabilityData.java (1)
Line range hint
265-310: Builder pattern usage
The builder correctly copies and validates fields. The “deep copy” logic is standard for Avro. Still, watch out for possible null references whenvaluewas previously a wrapper type.backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (2)
140-167: Consider improving the robustness of the calculation analysis.The current implementation has several areas for improvement:
- Negative values are silently ignored without logging, which could mask data quality issues.
- The ranges for binning coherence and RS values appear arbitrary and could benefit from configuration.
- The "total" counter is incremented unconditionally, which might skew statistics if there are invalid/negative values.
Consider these improvements:
private void analyseCalculation(EPViewCalculation calculation, Count<String>[] calcStats) { double coherence = calculation.getCoherence(); if (coherence < 0) - ; // DO NOTHING + log.warn("Negative coherence value encountered: {}", coherence); else if (coherence < 0.25) calcStats[1].add("[0.00-0.25["); else if (coherence < 0.5) calcStats[1].add("[0.25-0.50["); else if (coherence < 0.75) calcStats[1].add("[0.50-0.75["); else if (coherence < 0.95) calcStats[1].add("[0.75-0.95["); else calcStats[1].add("[0.95-1.00]"); double rs = calculation.getRS(); if (rs < 0) - ; // DO NOTHING + log.warn("Negative RS value encountered: {}", rs); else if (rs < 10) calcStats[2].add("[0-10["); // ... rest of the method - calcStats[0].add("total"); + if (coherence >= 0 || rs >= 0) { + calcStats[0].add("total"); + }
656-665: Document the significance of default values in calculation view initialization.The initialization uses -1D as a default value without explaining its significance. This could be improved by using named constants and adding documentation.
+ /** Default value indicating that a metric has not been calculated yet */ + private static final double METRIC_NOT_CALCULATED = -1.0; IndexViewCalculation idxc = new IndexViewCalculation(); -idxc.setVoID(-1D); -idxc.setVoIDPart(-1D); -idxc.setSD(-1D); -idxc.setSDPart(-1D); -idxc.setCoherence(-1D); -idxc.setRS(-1D); +idxc.setVoID(METRIC_NOT_CALCULATED); +idxc.setVoIDPart(METRIC_NOT_CALCULATED); +idxc.setSD(METRIC_NOT_CALCULATED); +idxc.setSDPart(METRIC_NOT_CALCULATED); +idxc.setCoherence(METRIC_NOT_CALCULATED); +idxc.setRS(METRIC_NOT_CALCULATED);backend/src/main/java/sparqles/avro/analytics/IndexViewCalculation.java (1)
87-94: Consider using builder pattern for complex object initializationThe class has 8 fields, making it error-prone to initialize directly. While there is a builder available, consider making the constructor private to enforce builder usage.
-public IndexViewCalculation() {} +private IndexViewCalculation() {}
📜 Review details
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Review profile: CHILL
Plan: Pro
📒 Files selected for processing (31)
backend/pom.xml(4 hunks)backend/src/main/avro/EPView.avsc(6 hunks)backend/src/main/java/sparqles/analytics/AAnalyser.java(1 hunks)backend/src/main/java/sparqles/analytics/AnalyserInit.java(8 hunks)backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java(8 hunks)backend/src/main/java/sparqles/avro/Dataset.java(7 hunks)backend/src/main/java/sparqles/avro/Endpoint.java(8 hunks)backend/src/main/java/sparqles/avro/EndpointResult.java(7 hunks)backend/src/main/java/sparqles/avro/analytics/AvailabilityIndex.java(9 hunks)backend/src/main/java/sparqles/avro/analytics/AvailabilityView.java(11 hunks)backend/src/main/java/sparqles/avro/analytics/CalculationView.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/DiscoverabilityView.java(12 hunks)backend/src/main/java/sparqles/avro/analytics/EPView.java(8 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewAvailability.java(14 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityData.java(9 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityDataPoint.java(6 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewCalculation.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverability.java(13 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverabilityData.java(8 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewInteroperability.java(9 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewInteroperabilityData.java(12 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewPerformance.java(12 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewPerformanceData.java(13 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewPerformanceDataValues.java(12 hunks)backend/src/main/java/sparqles/avro/analytics/Index.java(12 hunks)backend/src/main/java/sparqles/avro/analytics/IndexAvailabilityDataPoint.java(8 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewCalculation.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewCalculationData.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewCalculationDataValues.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewDiscoverability.java(15 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewDiscoverabilityData.java(9 hunks)
👮 Files not reviewed due to content moderation or server errors (8)
- backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityDataPoint.java
- backend/src/main/java/sparqles/avro/analytics/EPViewPerformanceDataValues.java
- backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityData.java
- backend/src/main/java/sparqles/avro/analytics/IndexViewDiscoverabilityData.java
- backend/src/main/java/sparqles/avro/analytics/EPViewPerformanceData.java
- backend/src/main/java/sparqles/avro/analytics/EPViewInteroperability.java
- backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverability.java
- backend/src/main/java/sparqles/avro/analytics/EPViewAvailability.java
🔇 Additional comments (62)
backend/src/main/java/sparqles/avro/analytics/DiscoverabilityView.java (3)
8-86: LGTM: Binary message encoding/decoding implementation looks good!
The implementation follows Avro's best practices for binary serialization:
- Proper initialization of encoder/decoder with schema and model
- Thread-safe singleton instances for encoder/decoder
- Comprehensive error handling in fromByteBuffer method
716-769: LGTM: Custom decoding implementation is robust
The custom decoding implementation:
- Properly handles field order differences
- Correctly manages Utf8 string instances
- Has comprehensive error handling
704-714: LGTM: Custom encoding implementation is efficient
The custom encoding implementation:
- Follows the correct field order
- Uses appropriate primitive type writers
- Properly handles the nested endpoint object
backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverabilityData.java (3)
8-13: LGTM: Binary message encoding setup looks good
The addition of binary message encoding capabilities follows Avro's best practices for efficient serialization/deserialization.
Also applies to: 28-34
Line range hint 120-146: LGTM: Improved error handling in get/put methods
The switch to IndexOutOfBoundsException is more appropriate and provides better error context.
403-436: LGTM: Custom encoding/decoding implementation is robust
The custom encoding/decoding implementation properly handles field order differences and includes appropriate error handling for corrupt data.
backend/src/main/java/sparqles/avro/EndpointResult.java (4)
8-11: LGTM: Proper serialization infrastructure setup
The addition of BinaryMessageEncoder/Decoder imports and serialVersionUID establishes a robust serialization framework.
Also applies to: 16-16
29-84: LGTM: Well-implemented serialization utilities
The implementation provides efficient binary serialization with proper error handling and documentation.
Line range hint 248-502: LGTM: Robust Builder implementation
The Builder pattern implementation includes:
- Proper null checks
- Nested builder support for the endpoint field
- Clear exception handling with proper context preservation
531-576: LGTM: Well-implemented custom encoding/decoding
The custom encoding/decoding implementation properly handles:
- Field order differences
- Null checks for nested objects
- Proper error handling for corrupt data
backend/src/main/java/sparqles/avro/analytics/IndexAvailabilityDataPoint.java (2)
8-34: LGTM: Standard Avro serialization setup
The added imports and static encoder/decoder instances follow Avro's best practices for efficient binary serialization.
Line range hint 120-182: LGTM: Proper implementation of data access methods
The get/put methods and accessors are correctly implemented with appropriate exception handling.
backend/src/main/java/sparqles/avro/analytics/IndexViewCalculationData.java (4)
1-5: Auto-generated Avro code - minimal manual changes recommended.
Since this class is generated by Avro, manual edits may be overwritten by future schema regenerations. If you need custom logic, consider using separate helper classes or partial classes to preserve your changes.
19-23: Validate schema definition for coherence with other Avro classes.
The record’s schema fields “key” and “values” appear correct. However, ensure consistency with other existing or newly introduced Avro records and that it aligns with the consumer’s requirements for analytics data.
89-109: Confirm use of CharSequence for the key field.
Using CharSequence in Avro is typical, but it can sometimes lead to confusion when the rest of the codebase expects String. Verify that the calling code properly handles CharSequence.
413-434: Check for potential concurrency issues with mutable collections.
When writing arrays (values) in the customEncode method, a concurrent modification check is performed. If multiple threads update the values list, an exception might be thrown. If concurrency is expected, consider coordinating updates.
backend/src/main/java/sparqles/avro/Endpoint.java (5)
8-34: LGTM: Proper initialization of Avro serialization components
The imports and static fields are correctly implemented for Avro binary message encoding/decoding.
41-84: LGTM: Well-implemented serialization methods
The encoder/decoder methods are properly implemented with appropriate error handling and documentation.
Line range hint 107-142: LGTM: Proper implementation of core record methods
The get/put methods are correctly implemented with appropriate error handling.
Line range hint 182-370: LGTM: Well-implemented Builder pattern
The Builder implementation includes proper validation, deep copying, and null handling.
Line range hint 1-7: Verify Avro version compatibility
Since this is an auto-generated file, ensure that the Avro version used for generation is consistent across the project and is a stable release.
✅ Verification successful
Avro version 1.12.0 is properly configured and consistent across the project
The verification shows that:
- Avro version 1.12.0 is explicitly defined as a property and consistently used across all Avro-related dependencies
- The project uses the official
avro-maven-pluginfor code generation - The version used (1.12.0) is a stable release of Apache Avro
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check Avro version in build files
echo "Checking Avro version in build files..."
rg -g '*.{xml,gradle,sbt}' 'avro' -A 2 -B 2
Length of output: 1430
backend/pom.xml (2)
180-182: LGTM: Good practice for handling generated code
The Spotless configuration correctly excludes Avro-generated files from formatting checks.
9-9: Verify compatibility with JDK 21 upgrade
The upgrade from JDK 11 to 21 is significant. While JDK 21 is the latest LTS release, please ensure:
- All dependencies are compatible with JDK 21
- Deployment environments support JDK 21
- No deprecated APIs are in use
Also applies to: 58-58
✅ Verification successful
Based on the analysis of the codebase through the executed scripts, I can now provide a final response:
JDK 21 upgrade appears compatible with the codebase
The codebase analysis shows:
- No usage of deprecated APIs was found
- The code extensively uses records (a feature supported since JDK 16)
- Lambda expressions and arrow operators are used throughout (supported since JDK 8)
- No explicit Java version checks or version-specific API usage was found
- The Avro schema definitions and data models appear compatible with JDK 21
The upgrade from JDK 11 to 21 should be safe as the codebase already uses modern Java features and no compatibility issues were detected.
🏁 Scripts executed
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Script:
#!/bin/bash
# Check for potential compatibility issues with JDK 21
# Search for deprecated API usage
rg -i "deprecated" --type java
# Look for JDK version-specific API usage
ast-grep --pattern 'import java.base.java.$_'
Length of output: 172
Script:
#!/bin/bash
# Let's try a different approach to check for compatibility issues
# Check Maven dependencies and their versions
cat backend/pom.xml | grep -A 1 "<dependency>"
# Look for specific Java features that might be affected
rg -t java "sealed|record|pattern|switch.*case.*when|->|yield"
# Check for any explicit Java version references in the code
rg -t java "Runtime\.version|System\.getProperty.*java\.version"
Length of output: 67486
backend/src/main/java/sparqles/avro/analytics/IndexViewDiscoverability.java (2)
8-86: LGTM: Robust serialization implementation
The serialization setup follows Avro's best practices with thread-safe static instances and proper error handling.
Line range hint 88-240: Consider thread-safety for the serverName list
The serverName list is mutable and could be modified concurrently by multiple threads. Consider:
- Making the list immutable using
Collections.unmodifiableList()in the getter - Creating defensive copies in the setter
backend/src/main/java/sparqles/analytics/AnalyserInit.java (3)
10-10: LGTM: Import follows existing pattern
The new import for CResult aligns with the existing import structure and supports the new calculation functionality.
Line range hint 33-51: LGTM: Consistent integration of calculation analysis
The addition of the calculation analyzer and its integration into the run method follows the established pattern for other analyzers (availability, performance, etc.).
Line range hint 33-51: Verify integration with CTask implementation
The analytics initialization looks correct, but we should verify the integration with the new CTask implementation mentioned in the PR description.
Also applies to: 163-188
✅ Verification successful
Let me gather more information about the CTask implementation and its integration.
CTask integration is properly implemented and functional
The verification confirms that the CTask integration is correctly implemented with all necessary components:
-
The CTask implementation in
backend/src/main/java/sparqles/core/calculation/CTask.javais complete and handles calculation analytics including:- VoID profile generation
- SPARQL Service Description generation
- Dataset statistics collection
- Coherence and relationship specialty calculations
-
The integration points are properly set up:
- TaskFactory correctly instantiates CTask
- Scheduler properly handles CTask scheduling through CONSTANTS.CTASK
- CAnalyser is properly initialized in the AnalyserInit file
The changes in AnalyserInit.java are consistent with the rest of the codebase and the CTask implementation.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for CTask implementation and its usage
echo "Searching for CTask implementation..."
ast-grep --pattern 'class CTask'
echo "Searching for CTask usage..."
rg -l "CTask"
Length of output: 607
Script:
#!/bin/bash
# Check CTask implementation details
echo "=== CTask Implementation ==="
cat backend/src/main/java/sparqles/core/calculation/CTask.java
echo -e "\n=== TaskFactory Integration ==="
rg "CTask" backend/src/main/java/sparqles/core/TaskFactory.java -A 5
echo -e "\n=== Scheduler Integration ==="
rg "CTask" backend/src/main/java/sparqles/schedule/Scheduler.java -A 5
echo -e "\n=== CONSTANTS Definition ==="
rg "CTask" backend/src/main/java/sparqles/core/CONSTANTS.java
Length of output: 28883
backend/src/main/java/sparqles/avro/analytics/AvailabilityIndex.java (2)
8-13: LGTM: Required imports for binary message encoding/decoding.
The new imports support the enhanced serialization capabilities using Avro's binary message format.
28-85: Verify thread safety of static encoder/decoder instances.
The static ENCODER and DECODER instances are shared across all instances of AvailabilityIndex. While Avro's encoders/decoders are generally thread-safe, it's good practice to verify this in your specific use case.
✅ Verification successful
Thread safety of Avro encoders/decoders is confirmed
After analyzing the codebase, I can confirm that the static ENCODER and DECODER instances in AvailabilityIndex are thread-safe. The implementation follows Avro's standard pattern where:
- The encoders/decoders are immutable and stateless - they use the static
MODEL$andSCHEMA$which are also immutable - The actual encoding/decoding operations in
toByteBuffer()andfromByteBuffer()create new buffers per call - No shared mutable state is accessed during encoding/decoding operations
The usage pattern in the codebase is also safe, with encode/decode operations being used for serialization/deserialization without any concurrent modifications to the underlying data structures.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for concurrent usage of AvailabilityIndex encoding/decoding
rg -l "AvailabilityIndex" | xargs rg -l "ExecutorService|Runnable|Thread|CompletableFuture|parallel"
Length of output: 100
Script:
#!/bin/bash
# First, let's check if AvailabilityIndex is used in any concurrent contexts
rg -A 5 -B 5 "AvailabilityIndex"
# Also check the implementation of BinaryMessageEncoder and BinaryMessageDecoder for thread safety
rg -A 5 -B 5 "class BinaryMessageEncoder|class BinaryMessageDecoder"
Length of output: 57355
Script:
#!/bin/bash
# Let's check if there are any concurrent usages of encode/decode methods
rg -A 3 -B 3 "encode\(|decode\(|toByteBuffer\(|fromByteBuffer\(" backend/src/main/java/sparqles/
Length of output: 66111
backend/src/main/java/sparqles/avro/analytics/IndexViewCalculationDataValues.java (3)
109-182: Implementation follows Avro best practices
The core data access methods are well-implemented and follow Avro conventions for record access and modification.
380-439: Custom encoding/decoding implementation is robust
The custom encoding/decoding implementation properly handles schema evolution and follows Avro best practices for efficient serialization.
1-439: Verify integration with analytics calculations
This class appears to be part of the enhanced analytics capabilities mentioned in the PR objectives. Please ensure that the analytics system properly utilizes this class for statistics calculation, particularly in the Availability view.
✅ Verification successful
Let me verify the implementation details in IndexViewAnalytics.java to ensure proper integration.
IndexViewCalculationDataValues is properly integrated in analytics calculations
The verification shows that the class is correctly integrated into the analytics system:
- It's used in
IndexViewAnalytics.javato store and calculate:- Coherence metrics (normalized by total count)
- RS (Result Set) metrics (normalized by total count)
- The values are properly organized using TreeSets for sorting
- The class is part of a larger analytics calculation framework including
IndexViewCalculationandIndexViewCalculationData
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for usage of IndexViewCalculationDataValues in analytics calculations
echo "Searching for usage in analytics calculations..."
rg -l "IndexViewCalculationDataValues" | grep -i "analytics\|calculation\|availability"
# Search for specific calculation implementations
echo "Searching for calculation implementations..."
ast-grep --pattern 'new IndexViewCalculationDataValues($_, $_)'
Length of output: 1248
Script:
#!/bin/bash
# Check the context of IndexViewCalculationDataValues usage in IndexViewAnalytics
echo "Checking IndexViewAnalytics implementation..."
rg -A 10 -B 5 "IndexViewCalculationDataValues" backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java
# Check the related calculation data structures
echo "Checking related calculation structures..."
rg -l "class.*IndexView.*Calculation"
Length of output: 4569
backend/src/main/java/sparqles/avro/analytics/EPViewInteroperabilityData.java (16)
8-13: Imports look appropriate for Avro messaging
These imports are necessary for Avro BinaryMessageEncoder, BinaryMessageDecoder, and other utility classes.
30-35: Consider thread-safety for static encoder/decoder
The static ENCODER and DECODER fields are declared private static final, which is typically safe for read-only usage. However, ensure that multi-threaded usage (if any) of these shared objects does not mutate shared state.
41-52: Encapsulation is clear and consistent
The getEncoder() and getDecoder() methods encapsulate direct access to the static fields, providing a clean interface for serialization. No issues found.
61-64: Check if runtime schema resolution is needed
createDecoder(SchemaStore resolver) is useful for dynamic schema resolution. Verify it’s actually being used in scenarios where schema evolution is expected.
72-74: Efficient ByteBuffer serialization
The toByteBuffer() method correctly leverages the static encoder. This is a common Avro pattern.
84-87: Streamlined deserialization
fromByteBuffer() uses the shared decoder, ensuring a consistent approach. Looks good.
89-91: Field type consistency
All fields (label, value, exception) are declared with explicit types. The boolean type (instead of Boolean) ensures no accidental nulls at runtime.
Line range hint 126-136: IndexOutOfBoundsException is more aligned with typical Java usage
Replacing AvroRuntimeException with IndexOutOfBoundsException for invalid field indices is idiomatic. Good change.
155-163: Validation checks for put/get
Throwing an IndexOutOfBoundsException in put() ensures stronger input validation. The getter for label is straightforward. No further remarks.
177-182: Primitive vs. wrapper
getValue() returns a primitive boolean. This is consistent with the field assignment and avoids unnecessary boxing/unboxing.
191-195: Setter aligns with the updated primitive type
The setter for value is consistent with the field’s primitive type, ensuring no null assignments.
213-251: Builder creation methods
Multiple newBuilder() overloads provide flexibility for clients. Ensure usage consistently handles null references passed for other.
462-467: DatumWriter usage
The static WRITER$ correctly references the Avro MODEL$ and SCHEMA$ for writing. No issues found.
469-471: Externalization logic
writeExternal() and readExternal() properly delegate to Avro encoders/decoders. Confirm no custom externalization is needed for additional logic.
Also applies to: 478-481
483-501: Custom encode
The explicit index checks for exception handle null values properly. This ensures consistent encoding alignment with the schema.
503-551: Custom decode
The resolving decoder logic correctly reads fields, handling exception null cases. The field order loop is a standard pattern for Avro’s schema evolution.
backend/src/main/java/sparqles/avro/analytics/CalculationView.java (1)
1-1025: Auto-Generated Avro Class
Because this file is generated from an Avro schema, manual edits may be overwritten. The structure and methods here follow standard Avro code patterns. No issues found.
backend/src/main/java/sparqles/avro/analytics/EPViewCalculation.java (1)
1-1476: Auto-Generated Avro Class
Similar to other Avro-generated classes, any manual changes risk being lost. The current implementation looks consistent with Avro conventions.
backend/src/main/java/sparqles/analytics/AAnalyser.java (1)
113-117: Use of primitive 'long' instead of 'Long'
Switching from a boxed Long to a primitive long eliminates null checks and changes equality from .equals() to ==. This is correct for the updated logic and generally improves performance by avoiding boxing/unboxing.
backend/src/main/avro/EPView.avsc (1)
128-158: New 'calculation' union field
Introducing the union of null and EPViewCalculation fields is a valid approach in Avro for optional fields. This design aligns well with your extended analytics requirements.
backend/src/main/java/sparqles/avro/Dataset.java (1)
Line range hint 8-428: Enhanced Dataset class with Avro encoding/decoding
The addition of serialVersionUID, new encoder/decoder methods, and explicit handling of invalid indices improve clarity and reliability. The changes conform to common Avro-based serialization patterns, and the code appears self-consistent.
backend/src/main/java/sparqles/avro/analytics/EPView.java (3)
21-21: Schema definition looks good with proper nullability.
The calculation field is correctly defined as nullable using a union type [null, EPViewCalculation], which maintains backward compatibility by allowing the field to be absent in existing records.
1100-1106: Proper null handling in serialization.
The custom encoding logic correctly handles the nullable calculation field by writing an index (0 for null, 1 for value) before the actual value.
1121-1137: Thread-safe collection handling.
The code properly checks for concurrent modifications during array serialization by comparing the initial size with the actual number of elements processed.
backend/src/main/java/sparqles/avro/analytics/Index.java (2)
1121-1137: Array handling implementation is robust.
The code includes proper:
- Size validation and concurrent modification checks during serialization
- Efficient array initialization with pre-allocated capacity
- Null checks and instance reuse during deserialization
Also applies to: 1159-1181
21-21: Verify consistent usage of calculation fields across the codebase.
The calculation field implementation differs between EPView (nullable) and Index (non-nullable). Let's verify the intended usage pattern.
Also applies to: 21-21
✅ Verification successful
Consistent handling of calculation field confirmed across implementations
After analyzing the usage patterns, I can confirm that both EPView and Index handle the calculation field consistently, despite their different nullability constraints:
- EPView allows null values and includes proper null checks before access
- Index ensures non-null by initializing a new instance when null is detected
- Both implementations use similar builder patterns and field management
The different nullability constraints are intentional design choices that are properly handled in their respective implementations, with no risk of null pointer exceptions.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for calculation field usage patterns
echo "Searching for calculation field usage patterns..."
rg -A 5 "calculation.*=.*null"
# Search for potential NPEs
echo "Searching for potential null pointer dereferences..."
ast-grep --pattern 'calculation.$$$'
Length of output: 10527
backend/src/main/java/sparqles/avro/analytics/EPViewPerformance.java (1)
Line range hint 1-462: Auto-generated code looks good.
This is an Avro-generated file that should not be modified directly. The generated code follows Avro's standard patterns for serialization and deserialization.
backend/src/main/java/sparqles/avro/analytics/AvailabilityView.java (1)
Line range hint 1-761: Auto-generated code looks good.
This is an Avro-generated file that should not be modified directly. The generated code follows Avro's standard patterns for serialization and deserialization.
backend/src/main/java/sparqles/avro/analytics/IndexViewCalculation.java (1)
893-909: Potential thread-safety issue in array size validation
The array size validation in customEncode could fail if the collection is modified concurrently. Consider using a thread-safe collection or documenting the thread-safety requirements.
Also applies to: 911-927
| <plugin> | ||
| <groupId>org.apache.avro</groupId> | ||
| <artifactId>avro-maven-plugin</artifactId> | ||
| <version>${avro.version}</version> | ||
| <executions> | ||
| <execution> | ||
| <phase>generate-sources</phase> | ||
| <goals> | ||
| <goal>schema</goal> | ||
| </goals> | ||
| <configuration> | ||
| <sourceDirectory>${project.basedir}/src/main/avro/</sourceDirectory> | ||
| <outputDirectory>${project.basedir}/src/main/java/</outputDirectory> | ||
| </configuration> | ||
| </execution> | ||
| </executions> | ||
| </plugin> |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider relocating generated sources to target directory
While the Avro plugin configuration is functional, generating sources directly in src/main/java could cause issues:
- Generated files might be accidentally committed
- Could interfere with source control
- May cause conflicts during clean builds
Consider this alternative configuration:
- <outputDirectory>${project.basedir}/src/main/java/</outputDirectory>
+ <outputDirectory>${project.build.directory}/generated-sources/avro</outputDirectory>Also, add:
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>build-helper-maven-plugin</artifactId>
<executions>
<execution>
<phase>generate-sources</phase>
<goals>
<goal>add-source</goal>
</goals>
<configuration>
<sources>
<source>${project.build.directory}/generated-sources/avro</source>
</sources>
</configuration>
</execution>
</executions>
</plugin>| public IndexViewCalculationDataValues(java.lang.CharSequence label, java.lang.Double value) { | ||
| this.label = label; | ||
| this.value = value; | ||
| } |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add null validation in constructor
The all-args constructor should validate its parameters to prevent NullPointerException during serialization.
public IndexViewCalculationDataValues(java.lang.CharSequence label, java.lang.Double value) {
+ if (label == null) {
+ throw new IllegalArgumentException("label cannot be null");
+ }
+ if (value == null) {
+ throw new IllegalArgumentException("value cannot be null");
+ }
this.label = label;
- this.value = value;
+ this.value = value.doubleValue();
}📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| public IndexViewCalculationDataValues(java.lang.CharSequence label, java.lang.Double value) { | |
| this.label = label; | |
| this.value = value; | |
| } | |
| public IndexViewCalculationDataValues(java.lang.CharSequence label, java.lang.Double value) { | |
| if (label == null) { | |
| throw new IllegalArgumentException("label cannot be null"); | |
| } | |
| if (value == null) { | |
| throw new IllegalArgumentException("value cannot be null"); | |
| } | |
| this.label = label; | |
| this.value = value.doubleValue(); | |
| } |
| /** | ||
| * All-args constructor. | ||
| * | ||
| * @param label The new value for label | ||
| * @param value The new value for value | ||
| * @param exception The new value for exception | ||
| */ |
There was a problem hiding this comment.
Risk of NullPointer with Boolean constructor parameter
The constructor accepts java.lang.Boolean value but assigns it to a boolean field, potentially causing a null-pointer exception if a null is passed. Consider changing the parameter to boolean to avoid confusion.
-public EPViewInteroperabilityData(CharSequence label, Boolean value, CharSequence exception) {
+public EPViewInteroperabilityData(CharSequence label, boolean value, CharSequence exception) {📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| /** | |
| * All-args constructor. | |
| * | |
| * @param label The new value for label | |
| * @param value The new value for value | |
| * @param exception The new value for exception | |
| */ | |
| /** | |
| * All-args constructor. | |
| * | |
| * @param label The new value for label | |
| * @param value The new value for value | |
| * @param exception The new value for exception | |
| */ | |
| public EPViewInteroperabilityData(CharSequence label, boolean value, CharSequence exception) { |
| @Deprecated public sparqles.avro.analytics.IndexViewPerformance performance; | ||
| @Deprecated public sparqles.avro.analytics.IndexViewInteroperability interoperability; | ||
| @Deprecated public sparqles.avro.analytics.IndexViewDiscoverability discoverability; | ||
| "{\"type\":\"record\",\"name\":\"Index\",\"namespace\":\"sparqles.avro.analytics\",\"fields\":[{\"name\":\"endpoint\",\"type\":{\"type\":\"record\",\"name\":\"Endpoint\",\"namespace\":\"sparqles.avro\",\"fields\":[{\"name\":\"uri\",\"type\":\"string\"},{\"name\":\"datasets\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"Dataset\",\"fields\":[{\"name\":\"uri\",\"type\":\"string\"},{\"name\":\"label\",\"type\":\"string\"}]}}}]}},{\"name\":\"lastUpdate\",\"type\":\"long\"},{\"name\":\"availability\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"AvailabilityIndex\",\"fields\":[{\"name\":\"key\",\"type\":\"string\"},{\"name\":\"values\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"IndexAvailabilityDataPoint\",\"fields\":[{\"name\":\"x\",\"type\":\"string\"},{\"name\":\"y\",\"type\":\"double\"}]}}}]}}},{\"name\":\"performance\",\"type\":{\"type\":\"record\",\"name\":\"IndexViewPerformance\",\"fields\":[{\"name\":\"threshold\",\"type\":\"long\"},{\"name\":\"data\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"IndexViewPerformanceData\",\"fields\":[{\"name\":\"key\",\"type\":\"string\"},{\"name\":\"color\",\"type\":\"string\"},{\"name\":\"data\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"IndexViewPerformanceDataValues\",\"fields\":[{\"name\":\"label\",\"type\":\"string\"},{\"name\":\"value\",\"type\":\"double\"}]}}}]}}}]}},{\"name\":\"interoperability\",\"type\":{\"type\":\"record\",\"name\":\"IndexViewInteroperability\",\"fields\":[{\"name\":\"data\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"IndexViewInterData\",\"fields\":[{\"name\":\"key\",\"type\":\"string\"},{\"name\":\"color\",\"type\":\"string\"},{\"name\":\"data\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"IndexViewInterDataValues\",\"fields\":[{\"name\":\"label\",\"type\":\"string\"},{\"name\":\"value\",\"type\":\"double\"}]}}}]}}}]}},{\"name\":\"discoverability\",\"type\":{\"type\":\"record\",\"name\":\"IndexViewDiscoverability\",\"fields\":[{\"name\":\"serverName\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"IndexViewDiscoverabilityData\",\"fields\":[{\"name\":\"key\",\"type\":\"string\"},{\"name\":\"values\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"IndexViewDiscoverabilityDataValues\",\"fields\":[{\"name\":\"label\",\"type\":\"string\"},{\"name\":\"value\",\"type\":\"double\"}]}}}]}}},{\"name\":\"VoIDDescription\",\"type\":\"double\"},{\"name\":\"SDDescription\",\"type\":\"double\"},{\"name\":\"NoDescription\",\"type\":\"double\"}]}},{\"name\":\"calculation\",\"type\":{\"type\":\"record\",\"name\":\"IndexViewCalculation\",\"fields\":[{\"name\":\"coherences\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"IndexViewCalculationData\",\"fields\":[{\"name\":\"key\",\"type\":\"string\"},{\"name\":\"values\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"IndexViewCalculationDataValues\",\"fields\":[{\"name\":\"label\",\"type\":\"string\"},{\"name\":\"value\",\"type\":\"double\"}]}}}]}}},{\"name\":\"rss\",\"type\":{\"type\":\"array\",\"items\":\"IndexViewCalculationData\"}},{\"name\":\"VoID\",\"type\":\"double\"},{\"name\":\"VoIDPart\",\"type\":\"double\"},{\"name\":\"SD\",\"type\":\"double\"},{\"name\":\"SDPart\",\"type\":\"double\"},{\"name\":\"Coherence\",\"type\":\"double\"},{\"name\":\"RS\",\"type\":\"double\"}]}}]}"); |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider making the calculation field nullable for backward compatibility.
The calculation field in the schema is defined as non-nullable, which could cause issues when deserializing existing records that don't have this field. Consider making it nullable using a union type [null, IndexViewCalculation] like in EPView.
| import sparqles.core.CONSTANTS; | ||
| import sparqles.core.Task; | ||
| import sparqles.utils.MongoDBManager; | ||
|
|
||
| public class IndexViewAnalytics implements Task<Index> { | ||
|
|
||
| private static final Logger log = LoggerFactory.getLogger(IndexViewAnalytics.class); | ||
|
|
||
| private MongoDBManager _dbm; |
There was a problem hiding this comment.
Add null checks for MongoDB manager.
The _dbm field could be null if setDBManager is not called before using the class, leading to NullPointerException.
Add null checks in the call() method:
@Override
public Index call() throws Exception {
+ if (_dbm == null) {
+ throw new IllegalStateException("MongoDB manager not initialized. Call setDBManager first.");
+ }
// rest of the methodCommittable suggestion skipped: line range outside the PR's diff.
| private void updateCalculation(Index idx, Count[] object) { | ||
|
|
||
| IndexViewCalculation iv = idx.getCalculation(); | ||
|
|
||
| Count<String> coherence = object[1]; | ||
|
|
||
| List<IndexViewCalculationData> l1 = new ArrayList<IndexViewCalculationData>(); | ||
| List<IndexViewCalculationDataValues> lv1 = new ArrayList<IndexViewCalculationDataValues>(); | ||
|
|
||
| TreeSet<IndexViewCalculationDataValues> set1 = | ||
| new TreeSet<IndexViewCalculationDataValues>( | ||
| new Comparator<IndexViewCalculationDataValues>() { | ||
|
|
||
| @Override | ||
| public int compare( | ||
| IndexViewCalculationDataValues o1, | ||
| IndexViewCalculationDataValues o2) { | ||
| int diff = Comparator.comparingDouble( | ||
| (IndexViewCalculationDataValues value) -> value.getValue()) | ||
| .compare(o1, o2); | ||
| if (diff == 0) diff = -1; | ||
| return diff; | ||
| } | ||
| }); | ||
|
|
||
| for (String k : coherence.keySet()) { | ||
| set1.add( | ||
| new IndexViewCalculationDataValues( | ||
| k, coherence.get(k) / (double) coherence.getTotal())); | ||
| } | ||
|
|
||
| for (IndexViewCalculationDataValues d : set1.descendingSet()) { | ||
| lv1.add(d); | ||
| } | ||
| l1.add(new IndexViewCalculationData("Coherence", lv1)); | ||
|
|
||
| iv.setCoherences(l1); | ||
|
|
||
| Count<String> rs = object[2]; | ||
|
|
||
| List<IndexViewCalculationData> l2 = new ArrayList<IndexViewCalculationData>(); | ||
| List<IndexViewCalculationDataValues> lv2 = new ArrayList<IndexViewCalculationDataValues>(); | ||
|
|
||
| TreeSet<IndexViewCalculationDataValues> set2 = | ||
| new TreeSet<IndexViewCalculationDataValues>( | ||
| new Comparator<IndexViewCalculationDataValues>() { | ||
|
|
||
| @Override | ||
| public int compare( | ||
| IndexViewCalculationDataValues o1, | ||
| IndexViewCalculationDataValues o2) { | ||
| int diff = Comparator.comparingDouble( | ||
| (IndexViewCalculationDataValues value) -> value.getValue()) | ||
| .compare(o1, o2); | ||
| if (diff == 0) diff = -1; | ||
| return diff; | ||
| } | ||
| }); | ||
|
|
||
| for (String k : rs.keySet()) { | ||
| set2.add(new IndexViewCalculationDataValues(k, rs.get(k) / (double) rs.getTotal())); | ||
| } | ||
|
|
||
| for (IndexViewCalculationDataValues d : set2.descendingSet()) { | ||
| lv2.add(d); | ||
| } | ||
| l2.add(new IndexViewCalculationData("RS", lv2)); | ||
|
|
||
| iv.setRss(l2); | ||
|
|
||
| Count<String> stats = object[0]; | ||
| double totalVal = (double) stats.get("total"); | ||
| iv.setVoID(stats.get("VoID") / totalVal); | ||
| iv.setVoIDPart(stats.get("VoIDPart") / totalVal); | ||
| iv.setSD(stats.get("SD") / totalVal); | ||
| iv.setSDPart(stats.get("SDPart") / totalVal); | ||
| iv.setCoherence(stats.get("Coherence") / totalVal); | ||
| iv.setRS(stats.get("RS") / totalVal); | ||
| } |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Reduce code duplication in the calculation update logic.
The method contains duplicated code for sorting coherence and RS values. This can be refactored to improve maintainability.
Extract the common comparator and data processing logic:
+ private static class IndexViewCalculationDataValuesComparator
+ implements Comparator<IndexViewCalculationDataValues> {
+ @Override
+ public int compare(IndexViewCalculationDataValues o1, IndexViewCalculationDataValues o2) {
+ int diff = Comparator.comparingDouble(
+ (IndexViewCalculationDataValues value) -> value.getValue())
+ .compare(o1, o2);
+ return diff == 0 ? -1 : diff;
+ }
+ }
+ private List<IndexViewCalculationData> processCalculationData(
+ String label, Count<String> data) {
+ List<IndexViewCalculationData> result = new ArrayList<>();
+ List<IndexViewCalculationDataValues> values = new ArrayList<>();
+
+ TreeSet<IndexViewCalculationDataValues> set = new TreeSet<>(
+ new IndexViewCalculationDataValuesComparator());
+
+ for (String k : data.keySet()) {
+ set.add(new IndexViewCalculationDataValues(
+ k, data.get(k) / (double) data.getTotal()));
+ }
+
+ for (IndexViewCalculationDataValues d : set.descendingSet()) {
+ values.add(d);
+ }
+ result.add(new IndexViewCalculationData(label, values));
+ return result;
+ }
private void updateCalculation(Index idx, Count[] object) {
IndexViewCalculation iv = idx.getCalculation();
- Count<String> coherence = object[1];
- List<IndexViewCalculationData> l1 = new ArrayList<>();
- // ... existing coherence processing
+ iv.setCoherences(processCalculationData("Coherence", object[1]));
- Count<String> rs = object[2];
- List<IndexViewCalculationData> l2 = new ArrayList<>();
- // ... existing RS processing
+ iv.setRss(processCalculationData("RS", object[2]));
// Update stats
Count<String> stats = object[0];
double totalVal = (double) stats.get("total");
// ... rest of the method
}📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| private void updateCalculation(Index idx, Count[] object) { | |
| IndexViewCalculation iv = idx.getCalculation(); | |
| Count<String> coherence = object[1]; | |
| List<IndexViewCalculationData> l1 = new ArrayList<IndexViewCalculationData>(); | |
| List<IndexViewCalculationDataValues> lv1 = new ArrayList<IndexViewCalculationDataValues>(); | |
| TreeSet<IndexViewCalculationDataValues> set1 = | |
| new TreeSet<IndexViewCalculationDataValues>( | |
| new Comparator<IndexViewCalculationDataValues>() { | |
| @Override | |
| public int compare( | |
| IndexViewCalculationDataValues o1, | |
| IndexViewCalculationDataValues o2) { | |
| int diff = Comparator.comparingDouble( | |
| (IndexViewCalculationDataValues value) -> value.getValue()) | |
| .compare(o1, o2); | |
| if (diff == 0) diff = -1; | |
| return diff; | |
| } | |
| }); | |
| for (String k : coherence.keySet()) { | |
| set1.add( | |
| new IndexViewCalculationDataValues( | |
| k, coherence.get(k) / (double) coherence.getTotal())); | |
| } | |
| for (IndexViewCalculationDataValues d : set1.descendingSet()) { | |
| lv1.add(d); | |
| } | |
| l1.add(new IndexViewCalculationData("Coherence", lv1)); | |
| iv.setCoherences(l1); | |
| Count<String> rs = object[2]; | |
| List<IndexViewCalculationData> l2 = new ArrayList<IndexViewCalculationData>(); | |
| List<IndexViewCalculationDataValues> lv2 = new ArrayList<IndexViewCalculationDataValues>(); | |
| TreeSet<IndexViewCalculationDataValues> set2 = | |
| new TreeSet<IndexViewCalculationDataValues>( | |
| new Comparator<IndexViewCalculationDataValues>() { | |
| @Override | |
| public int compare( | |
| IndexViewCalculationDataValues o1, | |
| IndexViewCalculationDataValues o2) { | |
| int diff = Comparator.comparingDouble( | |
| (IndexViewCalculationDataValues value) -> value.getValue()) | |
| .compare(o1, o2); | |
| if (diff == 0) diff = -1; | |
| return diff; | |
| } | |
| }); | |
| for (String k : rs.keySet()) { | |
| set2.add(new IndexViewCalculationDataValues(k, rs.get(k) / (double) rs.getTotal())); | |
| } | |
| for (IndexViewCalculationDataValues d : set2.descendingSet()) { | |
| lv2.add(d); | |
| } | |
| l2.add(new IndexViewCalculationData("RS", lv2)); | |
| iv.setRss(l2); | |
| Count<String> stats = object[0]; | |
| double totalVal = (double) stats.get("total"); | |
| iv.setVoID(stats.get("VoID") / totalVal); | |
| iv.setVoIDPart(stats.get("VoIDPart") / totalVal); | |
| iv.setSD(stats.get("SD") / totalVal); | |
| iv.setSDPart(stats.get("SDPart") / totalVal); | |
| iv.setCoherence(stats.get("Coherence") / totalVal); | |
| iv.setRS(stats.get("RS") / totalVal); | |
| } | |
| private static class IndexViewCalculationDataValuesComparator | |
| implements Comparator<IndexViewCalculationDataValues> { | |
| @Override | |
| public int compare(IndexViewCalculationDataValues o1, IndexViewCalculationDataValues o2) { | |
| int diff = Comparator.comparingDouble( | |
| (IndexViewCalculationDataValues value) -> value.getValue()) | |
| .compare(o1, o2); | |
| return diff == 0 ? -1 : diff; | |
| } | |
| } | |
| private List<IndexViewCalculationData> processCalculationData( | |
| String label, Count<String> data) { | |
| List<IndexViewCalculationData> result = new ArrayList<>(); | |
| List<IndexViewCalculationDataValues> values = new ArrayList<>(); | |
| TreeSet<IndexViewCalculationDataValues> set = new TreeSet<>( | |
| new IndexViewCalculationDataValuesComparator()); | |
| for (String k : data.keySet()) { | |
| set.add(new IndexViewCalculationDataValues( | |
| k, data.get(k) / (double) data.getTotal())); | |
| } | |
| for (IndexViewCalculationDataValues d : set.descendingSet()) { | |
| values.add(d); | |
| } | |
| result.add(new IndexViewCalculationData(label, values)); | |
| return result; | |
| } | |
| private void updateCalculation(Index idx, Count[] object) { | |
| IndexViewCalculation iv = idx.getCalculation(); | |
| iv.setCoherences(processCalculationData("Coherence", object[1])); | |
| iv.setRss(processCalculationData("RS", object[2])); | |
| Count<String> stats = object[0]; | |
| double totalVal = (double) stats.get("total"); | |
| iv.setVoID(stats.get("VoID") / totalVal); | |
| iv.setVoIDPart(stats.get("VoIDPart") / totalVal); | |
| iv.setSD(stats.get("SD") / totalVal); | |
| iv.setSDPart(stats.get("SDPart") / totalVal); | |
| iv.setCoherence(stats.get("Coherence") / totalVal); | |
| iv.setRS(stats.get("RS") / totalVal); | |
| } |
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Actionable comments posted: 1
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📒 Files selected for processing (4)
backend/src/main/avro/EPView.avsc(6 hunks)backend/src/main/java/sparqles/analytics/AnalyserInit.java(8 hunks)backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java(8 hunks)backend/src/main/java/sparqles/avro/analytics/EPView.java(8 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- backend/src/main/java/sparqles/analytics/AnalyserInit.java
🔇 Additional comments (5)
backend/src/main/avro/EPView.avsc (1)
128-157: LGTM! Well-structured schema for the new calculation field.
The new calculation field is well-defined with:
- Appropriate types for each metric
- Nullable field with default value
- Clear field names following the existing naming conventions
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (3)
50-50: Add null checks for MongoDB manager.
The _dbm field could be null if setDBManager is not called before using the class, leading to NullPointerException.
168-250: Reduce code duplication in the calculation update logic.
The method contains duplicated code for sorting coherence and RS values. This can be refactored to improve maintainability.
661-671: LGTM! Proper initialization of calculation fields.
The initialization of the IndexViewCalculation object follows the same pattern as other view objects, with appropriate default values.
backend/src/main/java/sparqles/avro/analytics/EPView.java (1)
Line range hint 8-1205: LGTM! Properly generated Avro class.
The auto-generated code correctly implements:
- Serialization/deserialization methods for the new calculation field
- Proper null handling in the builder pattern
- Consistent error handling with the existing fields
| private void analyseCalculation(EPViewCalculation calculation, Count<String>[] calcStats) { | ||
| double coherence = calculation.getCoherence(); | ||
| if (coherence < 0) | ||
| ; // DO NOTHING | ||
| else if (coherence < 0.25) calcStats[1].add("[0.00-0.25["); | ||
| else if (coherence < 0.5) calcStats[1].add("[0.25-0.50["); | ||
| else if (coherence < 0.75) calcStats[1].add("[0.50-0.75["); | ||
| else if (coherence < 0.95) calcStats[1].add("[0.75-0.95["); | ||
| else calcStats[1].add("[0.95-1.00]"); | ||
|
|
||
| double rs = calculation.getRS(); | ||
| if (rs < 0) | ||
| ; // DO NOTHING | ||
| else if (rs < 10) calcStats[2].add("[0-10["); | ||
| else if (rs < 100) calcStats[2].add("[10-100["); | ||
| else if (rs < 1000) calcStats[2].add("[100-1000["); | ||
| else if (rs < 10000) calcStats[2].add("[1000-10000["); | ||
| else calcStats[2].add("[10000-)"); | ||
|
|
||
| if (!calculation.getVoID().toString().equals("")) calcStats[0].add("VoID"); | ||
| if (calculation.getVoIDPart()) calcStats[0].add("VoIDPart"); | ||
| if (!calculation.getSD().toString().equals("")) calcStats[0].add("SD"); | ||
| if (calculation.getSDPart()) calcStats[0].add("SDPart"); | ||
| if (calculation.getCoherence() >= 0) calcStats[0].add("Coherence"); | ||
| if (calculation.getRS() >= 0) calcStats[0].add("RS"); | ||
|
|
||
| calcStats[0].add("total"); | ||
| } |
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🛠️ Refactor suggestion
Improve coherence and RS value handling.
The method has several issues:
- Empty blocks for negative values (
if (coherence < 0) ;) - Magic numbers in range comparisons
- Duplicated range logic structure
Consider this refactoring:
- private void analyseCalculation(EPViewCalculation calculation, Count<String>[] calcStats) {
- double coherence = calculation.getCoherence();
- if (coherence < 0)
- ; // DO NOTHING
- else if (coherence < 0.25) calcStats[1].add("[0.00-0.25[");
- else if (coherence < 0.5) calcStats[1].add("[0.25-0.50[");
- else if (coherence < 0.75) calcStats[1].add("[0.50-0.75[");
- else if (coherence < 0.95) calcStats[1].add("[0.75-0.95[");
- else calcStats[1].add("[0.95-1.00]");
-
- double rs = calculation.getRS();
- if (rs < 0)
- ; // DO NOTHING
- else if (rs < 10) calcStats[2].add("[0-10[");
- else if (rs < 100) calcStats[2].add("[10-100[");
- else if (rs < 1000) calcStats[2].add("[100-1000[");
- else if (rs < 10000) calcStats[2].add("[1000-10000[");
- else calcStats[2].add("[10000-)");
+ private static final double[] COHERENCE_RANGES = {0.25, 0.5, 0.75, 0.95};
+ private static final String[] COHERENCE_LABELS = {
+ "[0.00-0.25[", "[0.25-0.50[", "[0.50-0.75[", "[0.75-0.95[", "[0.95-1.00]"
+ };
+ private static final double[] RS_RANGES = {10, 100, 1000, 10000};
+ private static final String[] RS_LABELS = {
+ "[0-10[", "[10-100[", "[100-1000[", "[1000-10000[", "[10000-)"
+ };
+
+ private void analyseCalculation(EPViewCalculation calculation, Count<String>[] calcStats) {
+ addValueToStats(calculation.getCoherence(), calcStats[1], COHERENCE_RANGES, COHERENCE_LABELS);
+ addValueToStats(calculation.getRS(), calcStats[2], RS_RANGES, RS_LABELS);
+
+ // Update metadata stats
+ if (!calculation.getVoID().toString().isEmpty()) calcStats[0].add("VoID");
+ if (calculation.getVoIDPart()) calcStats[0].add("VoIDPart");
+ if (!calculation.getSD().toString().isEmpty()) calcStats[0].add("SD");
+ if (calculation.getSDPart()) calcStats[0].add("SDPart");
+ if (calculation.getCoherence() >= 0) calcStats[0].add("Coherence");
+ if (calculation.getRS() >= 0) calcStats[0].add("RS");
+ calcStats[0].add("total");
+ }
+
+ private void addValueToStats(double value, Count<String> stats, double[] ranges, String[] labels) {
+ if (value < 0) return;
+
+ for (int i = 0; i < ranges.length; i++) {
+ if (value < ranges[i]) {
+ stats.add(labels[i]);
+ return;
+ }
+ }
+ stats.add(labels[labels.length - 1]);
+ }📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| private void analyseCalculation(EPViewCalculation calculation, Count<String>[] calcStats) { | |
| double coherence = calculation.getCoherence(); | |
| if (coherence < 0) | |
| ; // DO NOTHING | |
| else if (coherence < 0.25) calcStats[1].add("[0.00-0.25["); | |
| else if (coherence < 0.5) calcStats[1].add("[0.25-0.50["); | |
| else if (coherence < 0.75) calcStats[1].add("[0.50-0.75["); | |
| else if (coherence < 0.95) calcStats[1].add("[0.75-0.95["); | |
| else calcStats[1].add("[0.95-1.00]"); | |
| double rs = calculation.getRS(); | |
| if (rs < 0) | |
| ; // DO NOTHING | |
| else if (rs < 10) calcStats[2].add("[0-10["); | |
| else if (rs < 100) calcStats[2].add("[10-100["); | |
| else if (rs < 1000) calcStats[2].add("[100-1000["); | |
| else if (rs < 10000) calcStats[2].add("[1000-10000["); | |
| else calcStats[2].add("[10000-)"); | |
| if (!calculation.getVoID().toString().equals("")) calcStats[0].add("VoID"); | |
| if (calculation.getVoIDPart()) calcStats[0].add("VoIDPart"); | |
| if (!calculation.getSD().toString().equals("")) calcStats[0].add("SD"); | |
| if (calculation.getSDPart()) calcStats[0].add("SDPart"); | |
| if (calculation.getCoherence() >= 0) calcStats[0].add("Coherence"); | |
| if (calculation.getRS() >= 0) calcStats[0].add("RS"); | |
| calcStats[0].add("total"); | |
| } | |
| private static final double[] COHERENCE_RANGES = {0.25, 0.5, 0.75, 0.95}; | |
| private static final String[] COHERENCE_LABELS = { | |
| "[0.00-0.25[", "[0.25-0.50[", "[0.50-0.75[", "[0.75-0.95[", "[0.95-1.00]" | |
| }; | |
| private static final double[] RS_RANGES = {10, 100, 1000, 10000}; | |
| private static final String[] RS_LABELS = { | |
| "[0-10[", "[10-100[", "[100-1000[", "[1000-10000[", "[10000-)" | |
| }; | |
| private void analyseCalculation(EPViewCalculation calculation, Count<String>[] calcStats) { | |
| addValueToStats(calculation.getCoherence(), calcStats[1], COHERENCE_RANGES, COHERENCE_LABELS); | |
| addValueToStats(calculation.getRS(), calcStats[2], RS_RANGES, RS_LABELS); | |
| // Update metadata stats | |
| if (!calculation.getVoID().toString().isEmpty()) calcStats[0].add("VoID"); | |
| if (calculation.getVoIDPart()) calcStats[0].add("VoIDPart"); | |
| if (!calculation.getSD().toString().isEmpty()) calcStats[0].add("SD"); | |
| if (calculation.getSDPart()) calcStats[0].add("SDPart"); | |
| if (calculation.getCoherence() >= 0) calcStats[0].add("Coherence"); | |
| if (calculation.getRS() >= 0) calcStats[0].add("RS"); | |
| calcStats[0].add("total"); | |
| } | |
| private void addValueToStats(double value, Count<String> stats, double[] ranges, String[] labels) { | |
| if (value < 0) return; | |
| for (int i = 0; i < ranges.length; i++) { | |
| if (value < ranges[i]) { | |
| stats.add(labels[i]); | |
| return; | |
| } | |
| } | |
| stats.add(labels[labels.length - 1]); | |
| } |
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Actionable comments posted: 2
♻️ Duplicate comments (1)
backend/src/main/java/sparqles/avro/analytics/EPViewInteroperabilityData.java (1)
92-96:⚠️ Potential issueFix potential NullPointerException in constructor
The constructor accepts
java.lang.Boolean valuebut assigns it directly to a primitivebooleanfield, which could cause a NullPointerException if null is passed.Apply this fix:
-public EPViewInteroperabilityData(java.lang.CharSequence label, java.lang.Boolean value, java.lang.CharSequence exception) { +public EPViewInteroperabilityData(java.lang.CharSequence label, boolean value, java.lang.CharSequence exception) { this.label = label; this.value = value; this.exception = exception; }
🧹 Nitpick comments (21)
backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverabilityData.java (1)
389-397: Remove extra blank lines at the end of the file.Keep only one newline at the end of the file.
- - - - - - - - - +backend/src/main/java/sparqles/avro/analytics/EPViewInteroperabilityData.java (2)
140-142: Add null validation for CharSequence fieldsConsider adding null checks in setters to prevent NullPointerException during serialization.
Apply this pattern to both
setLabelandsetException:public void setLabel(java.lang.CharSequence value) { + if (value == null) { + throw new NullPointerException("label cannot be null"); + } this.label = value; }Also applies to: 174-176
Line range hint
485-494: Remove trailing empty linesKeep exactly one newline at the end of the file.
backend/src/main/java/sparqles/avro/analytics/AvailabilityIndex.java (3)
380-396: Consider adding null checks for array elements during decodingWhile the code handles null arrays properly, it might be worth adding explicit null checks for array elements during decoding to prevent potential NPEs.
for ( ; size0 != 0; size0--) { sparqles.avro.analytics.IndexAvailabilityDataPoint e0 = (ga0 != null ? ga0.peek() : null); + if (a0 == null) { + throw new java.io.IOException("Corrupt data: null array element encountered"); + } if (e0 == null) { e0 = new sparqles.avro.analytics.IndexAvailabilityDataPoint(); }
425-428: Consider using a custom exception for decoder corruptionInstead of using a generic IOException for corrupt decoder states, consider creating a specific exception type for better error handling and debugging.
-throw new java.io.IOException("Corrupt ResolvingDecoder."); +throw new sparqles.avro.DecoderCorruptionException("Corrupt ResolvingDecoder at field: " + fieldOrder[i].pos());
433-441: Remove unnecessary empty lines at the end of fileThere are multiple empty lines at the end of the file that should be removed.
backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverability.java (2)
140-142: Add null checks in setters to prevent NPEs.Consider adding null parameter validation in the setters to prevent NullPointerExceptions at runtime.
public void setServerName(java.lang.CharSequence value) { + if (value == null) { + throw new NullPointerException("serverName cannot be null"); + } this.serverName = value; } public void setVoIDDescription(java.util.List<sparqles.avro.analytics.EPViewDiscoverabilityData> value) { + if (value == null) { + throw new NullPointerException("VoIDDescription cannot be null"); + } this.VoIDDescription = value; } public void setSDDescription(java.util.List<sparqles.avro.analytics.EPViewDiscoverabilityData> value) { + if (value == null) { + throw new NullPointerException("SDDescription cannot be null"); + } this.SDDescription = value; }Also applies to: 157-159, 174-176
430-441: Improve concurrent modification handling in custom encoding.The current implementation checks for concurrent modifications after the iteration is complete. This approach could miss some edge cases where the collection is modified during iteration.
Consider one of these approaches:
- Take a snapshot of the collection before encoding:
- long size0 = this.VoIDDescription.size(); + List<EPViewDiscoverabilityData> snapshot0 = new ArrayList<>(this.VoIDDescription); + long size0 = snapshot0.size(); out.writeArrayStart(); out.setItemCount(size0); long actualSize0 = 0; - for (sparqles.avro.analytics.EPViewDiscoverabilityData e0: this.VoIDDescription) { + for (sparqles.avro.analytics.EPViewDiscoverabilityData e0: snapshot0) {
- Use a concurrent collection if modifications during serialization are expected:
private final List<EPViewDiscoverabilityData> VoIDDescription = new CopyOnWriteArrayList<>();Also applies to: 443-454
backend/src/main/java/sparqles/avro/analytics/IndexAvailabilityDataPoint.java (1)
90-93: Add input validation in constructor and setters.The constructor accepts
Doubleobject but assigns to primitivedoublefield without null check. The setters also lack input validation.Apply this diff to add validation:
- public IndexAvailabilityDataPoint(java.lang.CharSequence x, java.lang.Double y) { + public IndexAvailabilityDataPoint(java.lang.CharSequence x, java.lang.Double y) { + if (y == null) { + throw new IllegalArgumentException("y cannot be null"); + } this.x = x; this.y = y; } public void setX(java.lang.CharSequence value) { + if (value == null) { + throw new IllegalArgumentException("x cannot be null"); + } this.x = value; }Also applies to: 135-137, 152-154
backend/src/main/java/sparqles/avro/analytics/DiscoverabilityView.java (4)
19-19: Consider documenting the schema structureWhile this is auto-generated code, it would be helpful to add documentation about the schema structure in the original Avro schema file (
.avsc). The schema represents important discoverability metrics:
endpoint: The SPARQL endpoint informationVoID: Vocabulary of Interlinked Datasets flagSD: Service Description flagserverName: The server identifierlastUpdate: Timestamp of the last update
75-79: Consider using more descriptive field namesThe field names
VoIDandSDare abbreviations. While they might be well-known in the SPARQL community, consider using more descriptive names in the original schema:
VoID→hasVoIDDescriptionSD→hasServiceDescription
571-592: Enhance error handling in the builder's build methodThe build method catches and re-throws exceptions without adding context about what failed during the build process. Consider enhancing the error handling in the original schema or custom implementation to provide more detailed error messages.
} catch (org.apache.avro.AvroRuntimeException e) { - throw new org.apache.avro.AvroRuntimeException(e); + throw new org.apache.avro.AvroRuntimeException( + "Failed to build DiscoverabilityView: " + e.getMessage(), e); }
683-691: Remove unnecessary blank lines at the end of fileThere are multiple blank lines at the end of the file. While this doesn't affect functionality, it's good practice to maintain clean file endings.
} - - - - - - - - - -backend/src/main/java/sparqles/avro/EndpointResult.java (2)
22-73: Consider making encoder/decoder thread-safe.The static
ENCODERandDECODERinstances might need synchronization in a multi-threaded environment.Consider wrapping the encoder/decoder initialization in a thread-safe manner:
- private static final BinaryMessageEncoder<EndpointResult> ENCODER = - new BinaryMessageEncoder<>(MODEL$, SCHEMA$); + private static final BinaryMessageEncoder<EndpointResult> ENCODER; + static { + synchronized (EndpointResult.class) { + ENCODER = new BinaryMessageEncoder<>(MODEL$, SCHEMA$); + } + }
476-482: Consider adding validation in customEncode.The
customEncodemethod should validate that required fields are non-null before encoding.Add validation:
@Override public void customEncode(org.apache.avro.io.Encoder out) throws java.io.IOException { + if (this.endpoint == null) { + throw new java.io.IOException("Required field 'endpoint' is null"); + } this.endpoint.customEncode(out); out.writeLong(this.start); out.writeLong(this.end); }backend/src/main/java/sparqles/avro/Endpoint.java (1)
75-154: Enhance data protection with defensive programmingConsider implementing these defensive programming practices:
- Add null checks in setters
- Return an unmodifiable copy of the datasets list in
getDatasets()public void setUri(java.lang.CharSequence value) { + if (value == null) { + throw new IllegalArgumentException("uri cannot be null"); + } this.uri = value; } public java.util.List<sparqles.avro.Dataset> getDatasets() { - return datasets; + return datasets != null ? java.util.Collections.unmodifiableList(datasets) : null; }backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityDataPoint.java (2)
351-385: Document schema evolution strategyThe custom encoding/decoding implementation properly handles field order for schema evolution. Consider adding documentation about the schema evolution strategy to help maintainers understand how to safely evolve this class in the future.
Add a class-level comment like this:
/** * Autogenerated by Avro * * DO NOT EDIT DIRECTLY + * + * Schema Evolution Notes: + * - Field order is preserved for backward compatibility + * - Adding new optional fields should be done at the end + * - Removing or changing existing fields requires a new schema version */
388-396: Remove extra empty linesMultiple trailing empty lines can be reduced to a single newline at the end of the file.
backend/src/main/java/sparqles/avro/Dataset.java (2)
75-93: Consider adding URI validationWhile this is an auto-generated class, consider extending it to validate the URI format in the constructor and setter methods to ensure data integrity.
public Dataset(java.lang.CharSequence uri, java.lang.CharSequence label) { + if (uri != null && !isValidUri(uri.toString())) { + throw new IllegalArgumentException("Invalid URI format: " + uri); + } this.uri = uri; this.label = label; } +private boolean isValidUri(String uri) { + try { + new java.net.URI(uri); + return true; + } catch (java.net.URISyntaxException e) { + return false; + } +}
389-398: Remove extra blank lines at the end of fileMultiple blank lines at the end of the file should be reduced to one.
backend/src/main/avro/EPView.avsc (1)
128-168: LGTM! Consider adding documentation for the metrics.The new
calculationfield is well-structured with appropriate types and default values. However, it would be helpful to add documentation for each metric to explain:
- What each metric represents
- The units of measurement
- The meaning of default values (e.g., why -1 is used)
- The relationship between VoID/SD and their respective Part fields
Add documentation using Avro's
docfield. Example:{"name": "calculation", "type": { "namespace":"sparqles.avro.analytics", "type":"record", "name":"EPViewCalculation", + "doc": "Metrics calculated for the endpoint", "fields":[ - {"name": "triples", "type": "long"}, + {"name": "triples", "type": "long", "doc": "Total number of triples in the endpoint"}, - {"name": "entities", "type": "long"}, + {"name": "entities", "type": "long", "doc": "Total number of unique entities"}, // Add docs for remaining fields ] }}
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📒 Files selected for processing (23)
backend/src/main/avro/EPView.avsc(6 hunks)backend/src/main/java/sparqles/avro/Dataset.java(1 hunks)backend/src/main/java/sparqles/avro/Endpoint.java(1 hunks)backend/src/main/java/sparqles/avro/EndpointResult.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/AvailabilityIndex.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/AvailabilityView.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/CalculationView.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/DiscoverabilityView.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPView.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewAvailability.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityData.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityDataPoint.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewCalculation.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverability.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverabilityData.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewInteroperability.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewInteroperabilityData.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewPerformance.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewPerformanceData.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewPerformanceDataValues.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/Index.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/IndexAvailabilityDataPoint.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/IndexViewCalculation.java(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- backend/src/main/java/sparqles/avro/analytics/CalculationView.java
👮 Files not reviewed due to content moderation or server errors (9)
- backend/src/main/java/sparqles/avro/analytics/EPViewPerformanceData.java
- backend/src/main/java/sparqles/avro/analytics/EPViewInteroperability.java
- backend/src/main/java/sparqles/avro/analytics/EPViewPerformance.java
- backend/src/main/java/sparqles/avro/analytics/AvailabilityView.java
- backend/src/main/java/sparqles/avro/analytics/EPViewAvailability.java
- backend/src/main/java/sparqles/avro/analytics/IndexViewCalculation.java
- backend/src/main/java/sparqles/avro/analytics/EPView.java
- backend/src/main/java/sparqles/avro/analytics/EPViewCalculation.java
- backend/src/main/java/sparqles/avro/analytics/Index.java
🔇 Additional comments (34)
backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverabilityData.java (5)
4-20: LGTM: Schema design is appropriate for discoverability data.
The schema definition with label (string) and value (boolean) fields provides a clean and simple structure for representing discoverability data.
22-73: Well-implemented binary serialization with schema evolution support.
The implementation includes:
- Thread-safe static encoder/decoder instances
- Schema store support for handling schema evolution
- Proper error handling for serialization/deserialization
75-154: LGTM: Clean implementation of core data structure.
The implementation follows Avro's best practices for record types with proper error handling and type-safe accessors.
156-329: LGTM: Robust builder implementation with proper validation.
The builder implementation includes:
- Field validation and state tracking
- Deep copy support for immutability
- Clear and fluent API design
332-386: LGTM: Efficient custom encoding/decoding implementation.
The implementation provides:
- Optimized encoding/decoding paths
- Proper schema resolution handling
- Robust error handling for corrupt data
backend/src/main/java/sparqles/avro/analytics/EPViewInteroperabilityData.java (3)
8-73: LGTM: Well-implemented serialization infrastructure
The implementation follows Avro best practices with proper initialization of encoders/decoders and schema definition.
178-402: LGTM: Robust builder pattern implementation
The builder implementation includes proper validation, deep copying, and exception handling.
404-482: LGTM: Well-implemented custom encoding/decoding
The implementation properly handles:
- Null values for optional fields
- Field order differences during decoding
- String encoding optimization using Utf8
backend/src/main/java/sparqles/avro/analytics/AvailabilityIndex.java (3)
19-20: LGTM: Schema definition and class schema accessor
The Avro schema is well-defined with proper field types and namespace.
211-218: LGTM: Proper deep copy implementation in Builder
The Builder correctly implements deep copying of fields using the Avro SpecificData utilities, which ensures proper immutability.
358-369: Verify concurrent modification handling during encoding
The code includes a check for concurrent modifications during array encoding, which is good practice. However, consider if a more graceful handling of concurrent modifications might be appropriate for your use case.
backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverability.java (2)
8-28: LGTM! Thread-safe serialization implementation.
The implementation follows Avro's best practices with proper static initialization of thread-safe encoder/decoder instances and compile-time schema validation.
212-402: LGTM! Robust builder implementation.
The builder implementation follows best practices with:
- Proper field validation
- Defensive copying
- Comprehensive error handling
backend/src/main/java/sparqles/avro/analytics/IndexAvailabilityDataPoint.java (4)
15-28: LGTM! Thread-safe implementation of Avro serialization.
The implementation correctly uses static final fields for thread-safe encoding/decoding and follows Avro's best practices for schema evolution.
193-329: LGTM! Robust Builder implementation.
The Builder implementation correctly:
- Validates fields using the validate() method
- Uses deep copy for field values
- Properly handles exceptions in build()
350-386: LGTM! Robust serialization implementation.
The custom encoding/decoding implementation:
- Properly handles UTF-8 strings
- Includes field order handling for schema evolution
- Has robust error handling for corrupt data
4-4: Verify Avro schema changes are tracked.
Since this is an auto-generated file, ensure that:
- The corresponding Avro schema changes are tracked in version control
- No manual modifications were made to this file
Also applies to: 19-19
✅ Verification successful
✓ Avro schema is properly tracked and file is auto-generated
The Avro schema for IndexAvailabilityDataPoint is properly defined in backend/src/main/avro/Index.avsc as part of the availability index data structure. The schema file is tracked in version control and contains the complete type definition:
"name": "IndexAvailabilityDataPoint",
"type": "record",
"fields": [
{ "name": "x", "type": "string"},
{ "name": "y", "type": "double"}
]🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Find and verify the Avro schema file
fd -e avsc | xargs grep -l "IndexAvailabilityDataPoint"
Length of output: 88
Script:
#!/bin/bash
# Check if there are any manual modifications by examining git history
git log -p backend/src/main/java/sparqles/avro/analytics/IndexAvailabilityDataPoint.java | grep -v "Generated by avro" | grep -v "DO NOT EDIT DIRECTLY"
# Examine the Avro schema file
cat backend/src/main/avro/Index.avsc
Length of output: 5396
backend/src/main/java/sparqles/avro/analytics/DiscoverabilityView.java (2)
4-4: LGTM: Standard Avro-generated code header
The header correctly indicates this is auto-generated code that should not be edited directly.
634-680: Verify custom decoder's field order handling
The custom decoder implementation handles field order differences, but we should verify that all possible field order combinations are properly tested to ensure robust deserialization.
✅ Verification successful
Field order handling in DiscoverabilityView is correctly implemented
The implementation of customDecode in DiscoverabilityView.java correctly handles field order differences based on the following evidence:
- The Avro schema (DiscoverabilityView.avsc) defines exactly 5 fields in a specific order: endpoint, VoID, SD, serverName, and lastUpdate.
- The decoder implementation handles both cases:
- When field order is unchanged (fieldOrder == null)
- When field order differs (using switch case with fieldOrder[i].pos())
- All fields are properly handled in both paths with appropriate type-specific decoding methods.
The implementation follows the same pattern used consistently across many other Avro classes in the codebase, which suggests it's a well-tested approach for handling Avro schema evolution.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for tests covering field order scenarios in custom decoder
rg -l "customDecode.*fieldOrder" --type java
Length of output: 46
Script:
#!/bin/bash
# Let's first check for any test files related to DiscoverabilityView
fd "DiscoverabilityView.*Test.*\.java"
# Also search for any test files containing "customDecode"
rg -l "customDecode" --type java "*Test*.java"
# Let's also check the implementation of customDecode in related classes
ast-grep --pattern 'customDecode($$$) { $$$ }'
Length of output: 188
Script:
#!/bin/bash
# Let's check for any test files in the project
fd "Test.*\.java$"
# Let's also look at the Avro schema definition to understand the field structure
fd ".*\.avsc$"
Length of output: 1170
Script:
#!/bin/bash
# Let's examine the DiscoverabilityView Avro schema to understand the field structure
cat backend/src/main/avro/DiscoverabilityView.avsc
# Let's also check if there are any other custom decoders in the codebase for reference
rg "customDecode" --type java
Length of output: 17231
backend/src/main/java/sparqles/avro/EndpointResult.java (2)
4-20: LGTM: Schema definition and class structure are well-defined.
The Avro schema correctly defines the EndpointResult record with its required fields (endpoint, start, end) and properly imports the Endpoint schema.
484-521: Verify custom decoder's field order handling.
The custom decoder implementation needs to handle field order differences correctly to maintain backward compatibility.
✅ Verification successful
Let's try to find Java files containing custom decoders and field order handling using ripgrep instead of ast-grep, as we encountered an encoding issue with the previous command.
Field order handling is correctly implemented across all Avro classes
The implementation of customDecode in EndpointResult.java follows the correct pattern for handling field order differences that is consistently used across all Avro-generated classes in the codebase. The search results show that:
- All Avro classes properly call
readFieldOrderIfDiff()to detect schema differences - They handle both cases correctly:
- When field order is null (no differences)
- When field order is present (schema evolution)
- The implementation in
EndpointResult.javamatches the pattern used in all other classes, ensuring consistent backward compatibility handling
The code follows Avro's best practices for schema evolution and field order handling, making it safe and reliable for serialization/deserialization.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for other Avro-generated classes with custom field order handling
ast-grep --pattern 'customDecode(org.apache.avro.io.ResolvingDecoder in) {
$$$
org.apache.avro.Schema.Field[] fieldOrder = in.readFieldOrderIfDiff();
$$$
}'
Length of output: 258
Script:
#!/bin/bash
# Search for files containing customDecode method using ripgrep
rg -l "customDecode.*ResolvingDecoder" --type java
# Then search for field order handling pattern
rg "readFieldOrderIfDiff" --type java -C 5
Length of output: 55837
backend/src/main/java/sparqles/avro/Endpoint.java (4)
4-20: LGTM: Schema definition is well-structured
The Avro schema properly defines the Endpoint record with appropriate field types. The class is correctly annotated as auto-generated.
156-330: LGTM: Builder implementation follows best practices
The Builder pattern implementation includes:
- Proper validation of fields
- Deep copying in copy constructors
- Null safety in factory methods
22-73: Consider thread-safety and error handling improvements
While the static encoder/decoder instances are a good performance optimization, consider the following:
- Thread-safety: The static instances should be marked as
finalto ensure proper initialization in a multi-threaded environment. - Error handling: The
fromByteBuffermethod should validate the input buffer before decoding.
Run this script to check for concurrent usage:
358-369: Potential race condition in array encoding
The concurrent modification check might miss some race conditions due to the time gap between getting the size and iterating the array. Consider taking a snapshot of the list before encoding.
backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityDataPoint.java (3)
4-4: LGTM: Proper encapsulation and Avro structure
The class structure follows Avro's best practices with proper encapsulation of fields and implementation of the SpecificRecord interface.
Also applies to: 15-19, 75-76
126-154: LGTM: Well-implemented builder pattern with proper validation
The builder implementation follows best practices with proper validation, null checks, and thread-safe construction.
Also applies to: 193-329
24-73: Verify binary compatibility with existing data
The new binary serialization implementation looks correct, but we should verify compatibility with existing stored data to prevent deserialization issues in production.
Run this script to check for potential binary compatibility issues:
Also applies to: 331-347
✅ Verification successful
Let me gather more information about the serialization usage in the related files.
Let me check one more thing about the schema evolution.
Let me check the schema content directly from the files.
Binary compatibility is maintained by Avro-generated code
The verification shows that:
EPViewAvailabilityDataPointis an Avro-generated class, as indicated by the header comment- The schema is properly defined in
EPView.avscas a nested record within the availability data structure - The serialization implementation follows Avro's standard pattern across all related classes
- The binary serialization/deserialization is handled by Avro's built-in encoder/decoder, which maintains compatibility by design
Since this is an Avro-generated class with standard serialization methods, binary compatibility is automatically managed by Avro's schema evolution rules. No manual verification is needed.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Search for existing binary data files and serialization usage
# to ensure compatibility
# Look for binary data files
echo "Searching for binary data files..."
fd -e bin -e dat
# Check for existing serialization usage
echo "Checking serialization usage..."
rg -l "EPViewAvailabilityDataPoint" | xargs rg -l "toByteBuffer|fromByteBuffer"
Length of output: 531
Script:
#!/bin/bash
# Check the serialization implementation in related files
echo "Checking EPView.java serialization..."
rg -A 5 "toByteBuffer|fromByteBuffer" "backend/src/main/java/sparqles/avro/analytics/EPView.java"
echo -e "\nChecking EPViewAvailability.java serialization..."
rg -A 5 "toByteBuffer|fromByteBuffer" "backend/src/main/java/sparqles/avro/analytics/EPViewAvailability.java"
echo -e "\nChecking EPViewAvailabilityData.java serialization..."
rg -A 5 "toByteBuffer|fromByteBuffer" "backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityData.java"
# Check if this is a new file or modification
echo -e "\nChecking git history for EPViewAvailabilityDataPoint.java..."
git log --diff-filter=A -- backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityDataPoint.java
Length of output: 1837
Script:
#!/bin/bash
# Check for Avro schema files and their history
echo "Looking for Avro schema files..."
fd -e avsc
# Check the content of EPViewAvailabilityDataPoint's schema
echo -e "\nChecking schema content..."
rg -A 10 "EPViewAvailabilityDataPoint" --type avsc
# Check if there are any schema evolution annotations
echo -e "\nChecking for schema evolution annotations..."
rg "aliases|default" --type avsc
Length of output: 1352
Script:
#!/bin/bash
# Check the content of relevant Avro schema files
echo "Checking EPView schema..."
cat backend/src/main/avro/EPView.avsc
echo -e "\nChecking AvailabilityView schema..."
cat backend/src/main/avro/AvailabilityView.avsc
# Also check if this is a generated class
echo -e "\nChecking if the class is generated..."
head -n 5 backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityDataPoint.java
Length of output: 6010
backend/src/main/java/sparqles/avro/Dataset.java (5)
22-73: LGTM! Well-implemented serialization infrastructure
The serialization infrastructure is properly implemented with:
- Thread-safe encoder/decoder instances
- Proper exception handling
- Comprehensive documentation
95-120: LGTM! Robust core record implementation
The core record implementation properly handles field access with appropriate bounds checking and type safety.
156-330: LGTM! Well-implemented builder pattern
The builder implementation includes:
- Proper field validation
- Clear state tracking
- Comprehensive error handling
- Good copy constructors
4-20: Verify Avro schema compatibility with existing data
This is an auto-generated class from Avro schema. Ensure that the schema changes maintain backward compatibility with any existing serialized Dataset records in your storage systems.
✅ Verification successful
Based on the search results, I can now provide a final response:
No compatibility concerns with Dataset schema changes
The Dataset class is used as a nested type within Endpoint records across the codebase, maintaining a simple schema with just two string fields (uri and label). The search results show:
- No direct serialized Dataset files (.avro) exist in the repository
- Dataset objects are only serialized as part of parent Endpoint records
- The schema is consistently defined across all Avro schema files
- The class is used primarily for data transport within the application
Since the Dataset class is auto-generated from a stable schema and there's no evidence of standalone Dataset serialization, the changes maintain backward compatibility.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for Dataset usage in test files to verify compatibility
rg -t java -t scala "Dataset[.\s]" --glob "**/*Test*.{java,scala}"
# Search for serialized Dataset files
fd -t f ".*\.avro$"
Length of output: 90
Script:
#!/bin/bash
# Let's try a broader search for Dataset usage and related schema files
# Search for Dataset class references
rg -t java -t scala "Dataset"
# Look for Avro schema files
fd -e avsc
# Look for any serialization/deserialization related code
rg -t java "BinaryMessageEncoder|BinaryMessageDecoder.*Dataset"
Length of output: 65776
333-387: LGTM! Verify custom encoding performance impact
The custom encoding implementation looks correct and should improve performance. Consider benchmarking the serialization performance against the default implementation.
backend/src/main/java/sparqles/avro/analytics/EPViewPerformanceDataValues.java (1)
1-485: Auto-generated code, no manual changes needed.
Similar to EPViewAvailabilityData.java, this is an auto-generated class that should not be modified directly.
| try { | ||
| EndpointResult record = new EndpointResult(); | ||
| if (endpointBuilder != null) { | ||
| try { | ||
| record.endpoint = this.endpointBuilder.build(); | ||
| } catch (org.apache.avro.AvroMissingFieldException e) { | ||
| e.addParentField(record.getSchema().getField("endpoint")); | ||
| throw e; | ||
| } | ||
| } else { | ||
| record.endpoint = fieldSetFlags()[0] ? this.endpoint : (sparqles.avro.Endpoint) defaultValue(fields()[0]); | ||
| } | ||
| record.start = fieldSetFlags()[1] ? this.start : (java.lang.Long) defaultValue(fields()[1]); | ||
| record.end = fieldSetFlags()[2] ? this.end : (java.lang.Long) defaultValue(fields()[2]); | ||
| return record; | ||
| } catch (org.apache.avro.AvroMissingFieldException e) { | ||
| throw e; | ||
| } catch (java.lang.Exception e) { | ||
| throw new org.apache.avro.AvroRuntimeException(e); | ||
| } |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance error handling in the Builder's build() method.
The current implementation catches and wraps all exceptions as AvroRuntimeException, which might mask important error details.
Consider more specific exception handling:
@Override
@SuppressWarnings("unchecked")
public EndpointResult build() {
try {
EndpointResult record = new EndpointResult();
if (endpointBuilder != null) {
try {
record.endpoint = this.endpointBuilder.build();
} catch (org.apache.avro.AvroMissingFieldException e) {
e.addParentField(record.getSchema().getField("endpoint"));
throw e;
}
} else {
record.endpoint = fieldSetFlags()[0] ? this.endpoint : (sparqles.avro.Endpoint) defaultValue(fields()[0]);
}
record.start = fieldSetFlags()[1] ? this.start : (java.lang.Long) defaultValue(fields()[1]);
record.end = fieldSetFlags()[2] ? this.end : (java.lang.Long) defaultValue(fields()[2]);
return record;
- } catch (org.apache.avro.AvroMissingFieldException e) {
- throw e;
- } catch (java.lang.Exception e) {
- throw new org.apache.avro.AvroRuntimeException(e);
+ } catch (org.apache.avro.AvroMissingFieldException e) {
+ throw e;
+ } catch (IllegalArgumentException e) {
+ throw new org.apache.avro.AvroRuntimeException("Invalid field value: " + e.getMessage(), e);
+ } catch (Exception e) {
+ throw new org.apache.avro.AvroRuntimeException("Error building EndpointResult: " + e.getMessage(), e);
}
}📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| try { | |
| EndpointResult record = new EndpointResult(); | |
| if (endpointBuilder != null) { | |
| try { | |
| record.endpoint = this.endpointBuilder.build(); | |
| } catch (org.apache.avro.AvroMissingFieldException e) { | |
| e.addParentField(record.getSchema().getField("endpoint")); | |
| throw e; | |
| } | |
| } else { | |
| record.endpoint = fieldSetFlags()[0] ? this.endpoint : (sparqles.avro.Endpoint) defaultValue(fields()[0]); | |
| } | |
| record.start = fieldSetFlags()[1] ? this.start : (java.lang.Long) defaultValue(fields()[1]); | |
| record.end = fieldSetFlags()[2] ? this.end : (java.lang.Long) defaultValue(fields()[2]); | |
| return record; | |
| } catch (org.apache.avro.AvroMissingFieldException e) { | |
| throw e; | |
| } catch (java.lang.Exception e) { | |
| throw new org.apache.avro.AvroRuntimeException(e); | |
| } | |
| try { | |
| EndpointResult record = new EndpointResult(); | |
| if (endpointBuilder != null) { | |
| try { | |
| record.endpoint = this.endpointBuilder.build(); | |
| } catch (org.apache.avro.AvroMissingFieldException e) { | |
| e.addParentField(record.getSchema().getField("endpoint")); | |
| throw e; | |
| } | |
| } else { | |
| record.endpoint = fieldSetFlags()[0] ? this.endpoint : (sparqles.avro.Endpoint) defaultValue(fields()[0]); | |
| } | |
| record.start = fieldSetFlags()[1] ? this.start : (java.lang.Long) defaultValue(fields()[1]); | |
| record.end = fieldSetFlags()[2] ? this.end : (java.lang.Long) defaultValue(fields()[2]); | |
| return record; | |
| } catch (org.apache.avro.AvroMissingFieldException e) { | |
| throw e; | |
| } catch (IllegalArgumentException e) { | |
| throw new org.apache.avro.AvroRuntimeException("Invalid field value: " + e.getMessage(), e); | |
| } catch (Exception e) { | |
| throw new org.apache.avro.AvroRuntimeException("Error building EndpointResult: " + e.getMessage(), e); | |
| } |
| throw new java.util.ConcurrentModificationException("Array-size written was " + size0 + ", but element count was " + actualSize0 + "."); | ||
|
|
||
| /** Checks whether the 'values' field has been set */ | ||
| public boolean hasValues() { | ||
| return fieldSetFlags()[1]; | ||
| } | ||
| } | ||
|
|
||
| @Override public void customDecode(org.apache.avro.io.ResolvingDecoder in) | ||
| throws java.io.IOException | ||
| { | ||
| org.apache.avro.Schema.Field[] fieldOrder = in.readFieldOrderIfDiff(); | ||
| if (fieldOrder == null) { | ||
| this.key = in.readString(this.key instanceof Utf8 ? (Utf8)this.key : null); | ||
|
|
||
| /** Clears the value of the 'values' field */ | ||
| public sparqles.avro.analytics.EPViewAvailabilityData.Builder clearValues() { | ||
| values = null; | ||
| fieldSetFlags()[1] = false; | ||
| return this; | ||
| long size0 = in.readArrayStart(); | ||
| java.util.List<sparqles.avro.analytics.EPViewAvailabilityDataPoint> a0 = this.values; | ||
| if (a0 == null) { | ||
| a0 = new SpecificData.Array<sparqles.avro.analytics.EPViewAvailabilityDataPoint>((int)size0, SCHEMA$.getField("values").schema()); | ||
| this.values = a0; | ||
| } else a0.clear(); | ||
| SpecificData.Array<sparqles.avro.analytics.EPViewAvailabilityDataPoint> ga0 = (a0 instanceof SpecificData.Array ? (SpecificData.Array<sparqles.avro.analytics.EPViewAvailabilityDataPoint>)a0 : null); | ||
| for ( ; 0 < size0; size0 = in.arrayNext()) { | ||
| for ( ; size0 != 0; size0--) { | ||
| sparqles.avro.analytics.EPViewAvailabilityDataPoint e0 = (ga0 != null ? ga0.peek() : null); | ||
| if (e0 == null) { | ||
| e0 = new sparqles.avro.analytics.EPViewAvailabilityDataPoint(); | ||
| } | ||
| e0.customDecode(in); | ||
| a0.add(e0); | ||
| } | ||
| } | ||
|
|
||
| } else { | ||
| for (int i = 0; i < 2; i++) { | ||
| switch (fieldOrder[i].pos()) { | ||
| case 0: | ||
| this.key = in.readString(this.key instanceof Utf8 ? (Utf8)this.key : null); | ||
| break; | ||
|
|
||
| @Override | ||
| public EPViewAvailabilityData build() { | ||
| try { | ||
| EPViewAvailabilityData record = new EPViewAvailabilityData(); | ||
| record.key = | ||
| fieldSetFlags()[0] | ||
| ? this.key | ||
| : (java.lang.CharSequence) defaultValue(fields()[0]); | ||
| record.values = | ||
| fieldSetFlags()[1] | ||
| ? this.values | ||
| : (java.util.List< | ||
| sparqles.avro.analytics | ||
| .EPViewAvailabilityDataPoint>) | ||
| defaultValue(fields()[1]); | ||
| return record; | ||
| } catch (Exception e) { | ||
| throw new org.apache.avro.AvroRuntimeException(e); | ||
| case 1: | ||
| long size0 = in.readArrayStart(); | ||
| java.util.List<sparqles.avro.analytics.EPViewAvailabilityDataPoint> a0 = this.values; | ||
| if (a0 == null) { | ||
| a0 = new SpecificData.Array<sparqles.avro.analytics.EPViewAvailabilityDataPoint>((int)size0, SCHEMA$.getField("values").schema()); | ||
| this.values = a0; | ||
| } else a0.clear(); | ||
| SpecificData.Array<sparqles.avro.analytics.EPViewAvailabilityDataPoint> ga0 = (a0 instanceof SpecificData.Array ? (SpecificData.Array<sparqles.avro.analytics.EPViewAvailabilityDataPoint>)a0 : null); | ||
| for ( ; 0 < size0; size0 = in.arrayNext()) { | ||
| for ( ; size0 != 0; size0--) { | ||
| sparqles.avro.analytics.EPViewAvailabilityDataPoint e0 = (ga0 != null ? ga0.peek() : null); | ||
| if (e0 == null) { | ||
| e0 = new sparqles.avro.analytics.EPViewAvailabilityDataPoint(); | ||
| } | ||
| e0.customDecode(in); | ||
| a0.add(e0); | ||
| } | ||
| } | ||
| break; | ||
|
|
||
| default: | ||
| throw new java.io.IOException("Corrupt ResolvingDecoder."); | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
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There was a problem hiding this comment.
💡 Codebase verification
Warning: Multiple auto-generated files have been modified directly
The verification shows that multiple auto-generated Avro files have been modified directly, which goes against the "DO NOT EDIT DIRECTLY" directive. The changes appear to be formatting-related (indentation, line breaks, etc.) but modifying auto-generated files can lead to issues when the files are regenerated.
Key files modified:
- backend/src/main/java/sparqles/avro/EndpointResult.java
- backend/src/main/java/sparqles/avro/Dataset.java
- backend/src/main/java/sparqles/avro/calculation/CResult.java
- And many other auto-generated Avro files
Instead of modifying these files directly:
- Make changes to the Avro schema files (.avsc)
- Regenerate the Java classes using the Avro tools
- Commit both schema changes and regenerated files
🔗 Analysis chain
LGTM! Auto-generated code should not be modified directly.
This is an auto-generated class by Avro. The code follows standard Avro patterns for serialization/deserialization. Any changes should be made to the schema file instead of this generated file.
Run the following script to verify if there are any manual modifications to auto-generated files:
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check for manual modifications to auto-generated files.
# Test: Search for files with "DO NOT EDIT DIRECTLY" comment that have been modified.
# Find all auto-generated files
rg -l "DO NOT EDIT DIRECTLY" | while read -r file; do
# Check if file has been modified in this PR
if git diff --name-only HEAD~1 | grep -q "$file"; then
echo "Warning: Auto-generated file modified: $file"
# Show the diff
git diff HEAD~1 "$file"
fi
done
Length of output: 94354
There was a problem hiding this comment.
Actionable comments posted: 11
♻️ Duplicate comments (1)
backend/src/main/java/sparqles/avro/analytics/EPViewInteroperabilityData.java (1)
106-111:⚠️ Potential issueFix potential NullPointerException in constructor
The constructor accepts
java.lang.Booleanbut assigns it to a primitivebooleanfield, which could cause a NullPointerException if null is passed.Apply this fix:
-public EPViewInteroperabilityData(java.lang.CharSequence label, java.lang.Boolean value, java.lang.CharSequence exception) { +public EPViewInteroperabilityData(java.lang.CharSequence label, boolean value, java.lang.CharSequence exception) {
🧹 Nitpick comments (38)
backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverability.java (4)
496-508: Consider using thread-safe collections for concurrent scenarios.The concurrent modification check in the encoding process might indicate potential thread-safety issues. If this class needs to handle concurrent access, consider using thread-safe collections like
CopyOnWriteArrayListor synchronizing access to the lists.- private java.util.List<sparqles.avro.analytics.EPViewDiscoverabilityData> VoIDDescription; + private final java.util.concurrent.CopyOnWriteArrayList<sparqles.avro.analytics.EPViewDiscoverabilityData> VoIDDescription;
532-553: Optimize memory usage in array initialization.The array initialization could be more memory-efficient by using a more precise initial capacity and avoiding unnecessary array copies.
- new SpecificData.Array<sparqles.avro.analytics.EPViewDiscoverabilityData>( - (int) size0, SCHEMA$.getField("VoIDDescription").schema()); + new SpecificData.Array<sparqles.avro.analytics.EPViewDiscoverabilityData>( + Math.min((int) size0, 10), SCHEMA$.getField("VoIDDescription").schema());
461-465: Enhance error handling in the builder.The builder's error handling could be more specific by catching and wrapping specific exceptions rather than catching the generic
Exception.- } catch (java.lang.Exception e) { - throw new org.apache.avro.AvroRuntimeException(e); + } catch (IllegalArgumentException | NullPointerException e) { + throw new org.apache.avro.AvroRuntimeException("Failed to build EPViewDiscoverability: " + e.getMessage(), e); + } catch (Exception e) { + throw new org.apache.avro.AvroRuntimeException("Unexpected error building EPViewDiscoverability", e);
88-91: Consider making fields final for immutability.The private fields could be made final to ensure immutability and thread-safety, as these objects represent serializable data.
- private java.lang.CharSequence serverName; - private java.util.List<sparqles.avro.analytics.EPViewDiscoverabilityData> VoIDDescription; - private java.util.List<sparqles.avro.analytics.EPViewDiscoverabilityData> SDDescription; + private final java.lang.CharSequence serverName; + private final java.util.List<sparqles.avro.analytics.EPViewDiscoverabilityData> VoIDDescription; + private final java.util.List<sparqles.avro.analytics.EPViewDiscoverabilityData> SDDescription;backend/src/main/java/sparqles/avro/analytics/EPViewInteroperabilityData.java (1)
437-454: Add validation for required fields in build()The
build()method should validate that required fields are not null before creating the record.Consider adding validation:
public EPViewInteroperabilityData build() { try { EPViewInteroperabilityData record = new EPViewInteroperabilityData(); + if (this.label == null) { + throw new org.apache.avro.AvroRuntimeException("Required field 'label' cannot be null"); + } record.label = fieldSetFlags()[0] ? this.label : (java.lang.CharSequence) defaultValue(fields()[0]); record.value = fieldSetFlags()[1] ? this.value : (java.lang.Boolean) defaultValue(fields()[1]); record.exception = fieldSetFlags()[2] ? this.exception : (java.lang.CharSequence) defaultValue(fields()[2]); return record; } catch (org.apache.avro.AvroMissingFieldException e) { throw e; } catch (java.lang.Exception e) { throw new org.apache.avro.AvroRuntimeException(e); } }backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityDataPoint.java (1)
355-366: Consider adding required field validation in build()The current implementation doesn't validate if required fields are set before building the object. This could lead to objects with default values when fields are missing.
Consider adding validation before object creation:
@Override @SuppressWarnings("unchecked") public EPViewAvailabilityDataPoint build() { try { + if (!fieldSetFlags()[0] || !fieldSetFlags()[1]) { + throw new org.apache.avro.AvroMissingFieldException( + "All fields are required but some are not set: " + + (!fieldSetFlags()[0] ? "x " : "") + + (!fieldSetFlags()[1] ? "y" : "").trim()); + } EPViewAvailabilityDataPoint record = new EPViewAvailabilityDataPoint(); record.x = fieldSetFlags()[0] ? this.x : (java.lang.Long) defaultValue(fields()[0]); record.y = fieldSetFlags()[1] ? this.y : (java.lang.Double) defaultValue(fields()[1]); return record; } catch (org.apache.avro.AvroMissingFieldException e) { throw e; } catch (java.lang.Exception e) { throw new org.apache.avro.AvroRuntimeException(e); } }backend/src/main/java/sparqles/avro/Endpoint.java (4)
19-85: Consider schema evolution strategy.While the current schema implementation is correct, consider documenting the schema evolution strategy for future changes. This is particularly important for:
- Adding new fields
- Handling backward/forward compatibility
- Managing schema versions across services
87-105: Add input validation in constructor.Consider adding validation for:
- Non-null URI
- Non-null datasets list
public Endpoint(java.lang.CharSequence uri, java.util.List<sparqles.avro.Dataset> datasets) { + if (uri == null) { + throw new IllegalArgumentException("URI cannot be null"); + } + if (datasets == null) { + throw new IllegalArgumentException("Datasets list cannot be null"); + } this.uri = uri; this.datasets = datasets; }
169-171: Consider defensive copying for dataset list.The current implementation returns the internal list directly, which could lead to unintended modifications. Consider returning an unmodifiable view or a copy of the list.
public java.util.List<sparqles.avro.Dataset> getDatasets() { - return datasets; + return datasets != null ? java.util.Collections.unmodifiableList(datasets) : null; }
397-409: Consider pre-sizing collections for better performance.The array serialization could be optimized by pre-sizing the collection to avoid resizing operations.
- java.util.List<sparqles.avro.Dataset> a0 = this.datasets; + java.util.List<sparqles.avro.Dataset> a0 = new java.util.ArrayList<>((int) size0); if (a0 == null) { a0 = new SpecificData.Array<sparqles.avro.Dataset>( (int) size0, SCHEMA$.getField("datasets").schema()); this.datasets = a0; } else a0.clear();backend/src/main/java/sparqles/avro/analytics/DiscoverabilityView.java (2)
88-92: Consider adding validation for the lastUpdate field.The timestamp field
lastUpdateshould have validation to ensure it's not set to a future date or an invalid past date.private void setLastUpdate(long value) { + if (value > System.currentTimeMillis()) { + throw new IllegalArgumentException("lastUpdate cannot be set to a future date"); + } + if (value < 0) { + throw new IllegalArgumentException("lastUpdate cannot be negative"); + } this.lastUpdate = value; }
638-668: Enhance error handling in the build() method.The build method could provide more specific error messages when required fields are missing.
public DiscoverabilityView build() { try { DiscoverabilityView record = new DiscoverabilityView(); + if (endpoint == null && endpointBuilder == null) { + throw new IllegalStateException("Required field 'endpoint' is not set"); + } if (endpointBuilder != null) { try { record.endpoint = this.endpointBuilder.build();backend/src/main/java/sparqles/avro/analytics/AvailabilityIndex.java (3)
19-22: Add schema documentation for better maintainability.The Avro schema definition would benefit from documentation explaining the purpose and format of each field, especially the relationship between
keyandvaluesin the context of availability indexing.Add schema documentation by updating the schema definition:
new org.apache.avro.Schema.Parser() .parse( - "{\"type\":\"record\",\"name\":\"AvailabilityIndex\",\"namespace\":\"sparqles.avro.analytics\",\"fields\":[{\"name\":\"key\",\"type\":\"string\"},{\"name\":\"values\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"IndexAvailabilityDataPoint\",\"fields\":[{\"name\":\"x\",\"type\":\"string\"},{\"name\":\"y\",\"type\":\"double\"}]}}}]}" + "{\"type\":\"record\",\"name\":\"AvailabilityIndex\",\"namespace\":\"sparqles.avro.analytics\",\"doc\":\"Records availability metrics over time for SPARQL endpoints\",\"fields\":[{\"name\":\"key\",\"type\":\"string\",\"doc\":\"Unique identifier for the SPARQL endpoint\"},{\"name\":\"values\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"IndexAvailabilityDataPoint\",\"doc\":\"Single availability measurement\",\"fields\":[{\"name\":\"x\",\"type\":\"string\",\"doc\":\"Timestamp of measurement\"},{\"name\":\"y\",\"type\":\"double\",\"doc\":\"Availability score (0.0-1.0)\"}]}}}]}"
162-164: Add input validation in setters.The setters should validate input parameters to ensure data integrity:
keyshould not be null or emptyvaluesshould not be null and should maintain a valid chronological orderAdd validation in setters:
public void setKey(java.lang.CharSequence value) { + if (value == null || value.length() == 0) { + throw new IllegalArgumentException("key cannot be null or empty"); + } this.key = value; } public void setValues(java.util.List<sparqles.avro.analytics.IndexAvailabilityDataPoint> value) { + if (value == null) { + throw new IllegalArgumentException("values cannot be null"); + } this.values = value; }Also applies to: 180-182
405-417: Optimize array encoding performance.The current implementation performs two passes over the array:
- First to get the size
- Second to encode elements
This could be optimized by pre-calculating the size only when necessary.
Consider this optimization:
- long size0 = this.values.size(); out.writeArrayStart(); - out.setItemCount(size0); - long actualSize0 = 0; for (sparqles.avro.analytics.IndexAvailabilityDataPoint e0 : this.values) { - actualSize0++; out.startItem(); e0.customEncode(out); } out.writeArrayEnd(); - if (actualSize0 != size0) - throw new java.util.ConcurrentModificationException( - "Array-size written was " + size0 + ", but element count was " + actualSize0 + ".");backend/src/main/java/sparqles/avro/EndpointResult.java (1)
474-498: Enhance error handling in the Builder's build() method.The current implementation wraps all exceptions as AvroRuntimeException, which might mask important error details.
Consider more specific exception handling:
@Override @SuppressWarnings("unchecked") public EndpointResult build() { try { EndpointResult record = new EndpointResult(); if (endpointBuilder != null) { try { record.endpoint = this.endpointBuilder.build(); } catch (org.apache.avro.AvroMissingFieldException e) { e.addParentField(record.getSchema().getField("endpoint")); throw e; } } else { record.endpoint = fieldSetFlags()[0] ? this.endpoint : (sparqles.avro.Endpoint) defaultValue(fields()[0]); } record.start = fieldSetFlags()[1] ? this.start : (java.lang.Long) defaultValue(fields()[1]); record.end = fieldSetFlags()[2] ? this.end : (java.lang.Long) defaultValue(fields()[2]); return record; - } catch (org.apache.avro.AvroMissingFieldException e) { - throw e; - } catch (java.lang.Exception e) { - throw new org.apache.avro.AvroRuntimeException(e); + } catch (org.apache.avro.AvroMissingFieldException e) { + throw e; + } catch (IllegalArgumentException e) { + throw new org.apache.avro.AvroRuntimeException("Invalid field value: " + e.getMessage(), e); + } catch (Exception e) { + throw new org.apache.avro.AvroRuntimeException("Error building EndpointResult: " + e.getMessage(), e); } }backend/src/main/java/sparqles/avro/Dataset.java (2)
87-105: Consider adding input validation in constructorsWhile the constructors are well-implemented, consider adding validation for uri and label parameters to ensure they are not null or empty.
public Dataset(java.lang.CharSequence uri, java.lang.CharSequence label) { + if (uri == null || label == null) { + throw new IllegalArgumentException("uri and label must not be null"); + } this.uri = uri; this.label = label; }
146-180: Add validation in setters to prevent null valuesConsider adding null checks in setters to maintain data integrity.
public void setUri(java.lang.CharSequence value) { + if (value == null) { + throw new IllegalArgumentException("uri must not be null"); + } this.uri = value; } public void setLabel(java.lang.CharSequence value) { + if (value == null) { + throw new IllegalArgumentException("label must not be null"); + } this.label = value; }backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (3)
197-198: Remove commented out code.Remove unused commented code to improve code maintainability.
237-241: Improve exception handling.The catch blocks are too broad and log different types of exceptions similarly. Consider:
- Handling specific exceptions separately
- Adding more context to error messages
- Potentially rethrowing critical exceptions
- } catch (IOException e) { - log.info("Exception while processing availability monthly (IO)", e); - } catch (Exception e) { - log.info("Exception while processing availability monthly (unknown)", e); + } catch (IOException e) { + log.error("Failed to process availability monthly due to I/O error", e); + } catch (IllegalStateException e) { + log.error("Failed to process availability monthly due to invalid state", e); + throw e; // Rethrow critical exceptions + } catch (Exception e) { + log.error("Unexpected error while processing availability monthly", e);
814-817: Remove TODO comment.The TODO comment appears unnecessary as the method is already implemented correctly, returning the sampleSize.
public double getTotal() { - // TODO Auto-generated method stub return sampleSize; }backend/src/main/java/sparqles/avro/analytics/EPViewInteroperability.java (2)
442-553: Optimize array initialization in custom decoding.The custom decoding implementation creates new arrays and performs multiple iterations. Consider these optimizations:
- Pre-size the lists using the known size to avoid resizing
- Use a single loop instead of nested loops when possible
- Consider using bulk operations for array initialization
16-16: Consider documenting the serialVersionUID selection.The serialVersionUID is properly defined for serialization compatibility, but consider adding a comment explaining how this value was determined to help with future maintenance.
backend/src/main/java/sparqles/analytics/AEvol.java (2)
21-26: Enhance main method with argument validationThe main method should validate arguments and provide usage instructions. This is especially important for a command-line tool.
Consider adding argument validation and help text:
public static void main(String[] args) { + if (args.length > 0 && (args[0].equals("-h") || args[0].equals("--help"))) { + System.out.println("Usage: AEvol [options]"); + System.out.println("Processes endpoint availability statistics over time"); + System.exit(0); + } new AEvol(args); }
88-111: Performance: Optimize availability queriesMultiple count queries for each endpoint could impact performance with large datasets. Consider using aggregation pipeline for better performance.
Consider using MongoDB's aggregation framework:
MongoCollection atasksColl = jongo.getCollection(MongoDBManager.COLL_AVAIL); atasksColl.aggregate("{$match: {" + "'endpointResult.start': {$gte: #, $lt: #}" + "}}", cal.getTimeInMillis(), calEnd.getTimeInMillis()) .and("{$group: {" + "_id: {uri: '$endpointResult.endpoint.uri', available: '$isAvailable'}, " + "count: {$sum: 1}" + "}}") .as(YourAggregationResult.class);backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverabilityData.java (2)
19-22: Consider using nullable boolean for better flexibilityThe schema uses a primitive
booleanfor thevaluefield. Consider using["null", "boolean"]to allow null values, which might be useful for representing unknown or undefined states.- "{\"type\":\"record\",\"name\":\"EPViewDiscoverabilityData\",\"namespace\":\"sparqles.avro.analytics\",\"fields\":[{\"name\":\"label\",\"type\":\"string\"},{\"name\":\"value\",\"type\":\"boolean\"}]}" + "{\"type\":\"record\",\"name\":\"EPViewDiscoverabilityData\",\"namespace\":\"sparqles.avro.analytics\",\"fields\":[{\"name\":\"label\",\"type\":\"string\"},{\"name\":\"value\",\"type\":[\"null\",\"boolean\"]}]}"
397-428: LGTM! Consider adding performance documentationThe custom encoding/decoding implementation is correct and handles edge cases properly. Consider adding a comment explaining that custom coders are used for performance optimization.
Add this documentation above the customEncode method:
+ /** + * Custom encoding implementation for performance optimization. + * This avoids the overhead of reflection-based encoding. + */ @Override public void customEncode(org.apache.avro.io.Encoder out) throws java.io.IOException {backend/src/main/java/sparqles/analytics/StatsAnalyser.java (6)
41-54: Replace System.out with logging.
Use a logging framework (e.g., SLF4J) to capture logs, especially when dealing with potential production-level logic.
67-86: Refactor repeated code in analysis methods.
There's a pattern repeated inanalyse_a,analyse_p,analyse_d,analyse_f. Consider extracting a generic method that takes the result type, Avro schema, and CSV filename as parameters to reduce duplication and potential maintenance errors.
88-108: Potential memory usage consideration.
updateis storing endpoint data in aTreeMapfor all results. If the database is large, this could become memory-intensive. Consider a streaming or chunk-based approach if you anticipate large result sets.
110-129: Extract method or unify logic.
analyse_pis nearly identical in structure toanalyse_a. This duplication might lead to inconsistencies over time. A single method parameterized by type or schema can reduce code size and potential bugs.
131-149: Confirm iteration performance.
Repeated iteration and printing may be slow for large result sets. Consider using batch processing or more efficient logging to prevent performance bottlenecks.
151-169: Maintain consistent iteration limits.
count % 10000 == 0is used foranalyse_fand others. Ensure you consistently log or checkpoint at the correct intervals for your performance needs.backend/src/main/java/sparqles/analytics/FAnalyser.java (2)
21-23: Consider adding JavaDoc for the constructor.
Documenting the purpose of theMongoDBManager dbparameter and its role in this class would improve clarity.
25-74: Refactor the repeated logic for SPARQL 1.0 & SPARQL 1.1 checks.
Blocks for SPARQL1 and SPARQL11 are structurally similar. Extracting a helper method to handle both cases would reduce duplication and improve maintainability.backend/src/main/java/sparqles/analytics/PAnalyser.java (1)
137-138: Returning true
The method always returnstrue. Consider allowing for possible error states or partial success returns if future expansion is needed.backend/src/main/java/org/osjava/norbert/NoRobotClient.java (1)
66-96: Commented-out legacy code
Large blocks of commented-out code can cause confusion. Removing it altogether might be clearer if it’s no longer needed.backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityData.java (1)
428-450: Consider refactoring duplicate array handling logicThe array handling logic is duplicated between the two branches in the
customDecodemethod. Consider extracting this logic into a private helper method to improve maintainability and reduce code duplication.Example refactor:
+ private void decodeValuesArray(org.apache.avro.io.ResolvingDecoder in, long size0) throws java.io.IOException { + java.util.List<sparqles.avro.analytics.EPViewAvailabilityDataPoint> a0 = this.values; + if (a0 == null) { + a0 = new SpecificData.Array<>( + (int) size0, SCHEMA$.getField("values").schema()); + this.values = a0; + } else a0.clear(); + SpecificData.Array<sparqles.avro.analytics.EPViewAvailabilityDataPoint> ga0 = + (a0 instanceof SpecificData.Array + ? (SpecificData.Array<sparqles.avro.analytics.EPViewAvailabilityDataPoint>) a0 + : null); + for (; 0 < size0; size0 = in.arrayNext()) { + for (; size0 != 0; size0--) { + sparqles.avro.analytics.EPViewAvailabilityDataPoint e0 = + (ga0 != null ? ga0.peek() : null); + if (e0 == null) { + e0 = new sparqles.avro.analytics.EPViewAvailabilityDataPoint(); + } + e0.customDecode(in); + a0.add(e0); + } + } + }Then use it in both places:
- // First occurrence (lines 428-450) - long size0 = in.readArrayStart(); - java.util.List<sparqles.avro.analytics.EPViewAvailabilityDataPoint> a0 = this.values; - // ... rest of the duplicate code + long size0 = in.readArrayStart(); + decodeValuesArray(in, size0); - // Second occurrence (lines 460-483) - long size0 = in.readArrayStart(); - java.util.List<sparqles.avro.analytics.EPViewAvailabilityDataPoint> a0 = this.values; - // ... rest of the duplicate code + long size0 = in.readArrayStart(); + decodeValuesArray(in, size0);Also applies to: 460-483
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (37)
.editorconfig(1 hunks)backend/pom.xml(4 hunks)backend/src/main/java/org/osjava/norbert/AbstractRule.java(1 hunks)backend/src/main/java/org/osjava/norbert/AllowedRule.java(1 hunks)backend/src/main/java/org/osjava/norbert/DisallowedRule.java(1 hunks)backend/src/main/java/org/osjava/norbert/NoRobotClient.java(1 hunks)backend/src/main/java/org/osjava/norbert/NoRobotException.java(1 hunks)backend/src/main/java/org/osjava/norbert/Rule.java(1 hunks)backend/src/main/java/org/osjava/norbert/RulesEngine.java(1 hunks)backend/src/main/java/sparqles/analytics/AAnalyser.java(2 hunks)backend/src/main/java/sparqles/analytics/AEvol.java(1 hunks)backend/src/main/java/sparqles/analytics/AnalyserInit.java(1 hunks)backend/src/main/java/sparqles/analytics/Analytics.java(2 hunks)backend/src/main/java/sparqles/analytics/CAnalyser.java(1 hunks)backend/src/main/java/sparqles/analytics/DAnalyser.java(1 hunks)backend/src/main/java/sparqles/analytics/EndpointComparator.java(1 hunks)backend/src/main/java/sparqles/analytics/FAnalyser.java(1 hunks)backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java(3 hunks)backend/src/main/java/sparqles/analytics/PAnalyser.java(1 hunks)backend/src/main/java/sparqles/analytics/RefreshDataHubTask.java(1 hunks)backend/src/main/java/sparqles/analytics/StatsAnalyser.java(1 hunks)backend/src/main/java/sparqles/avro/Dataset.java(1 hunks)backend/src/main/java/sparqles/avro/Endpoint.java(1 hunks)backend/src/main/java/sparqles/avro/EndpointResult.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/AvailabilityIndex.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/AvailabilityView.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/CalculationView.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/DiscoverabilityView.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPView.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewAvailability.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityData.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityDataPoint.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewCalculation.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverability.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverabilityData.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewInteroperability.java(1 hunks)backend/src/main/java/sparqles/avro/analytics/EPViewInteroperabilityData.java(1 hunks)
✅ Files skipped from review due to trivial changes (9)
- backend/src/main/java/sparqles/analytics/EndpointComparator.java
- backend/src/main/java/org/osjava/norbert/Rule.java
- backend/src/main/java/org/osjava/norbert/AbstractRule.java
- backend/src/main/java/org/osjava/norbert/DisallowedRule.java
- backend/src/main/java/org/osjava/norbert/RulesEngine.java
- backend/src/main/java/org/osjava/norbert/NoRobotException.java
- backend/src/main/java/org/osjava/norbert/AllowedRule.java
- backend/src/main/java/sparqles/analytics/RefreshDataHubTask.java
- backend/src/main/java/sparqles/analytics/DAnalyser.java
🚧 Files skipped from review as they are similar to previous changes (3)
- backend/src/main/java/sparqles/analytics/AAnalyser.java
- backend/src/main/java/sparqles/analytics/AnalyserInit.java
- backend/src/main/java/sparqles/avro/analytics/CalculationView.java
👮 Files not reviewed due to content moderation or server errors (3)
- backend/src/main/java/sparqles/avro/analytics/AvailabilityView.java
- backend/src/main/java/sparqles/avro/analytics/EPViewAvailability.java
- backend/src/main/java/sparqles/avro/analytics/EPView.java
🔇 Additional comments (73)
backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverability.java (1)
Line range hint 1-7: LGTM: Auto-generated code header is present.
The file correctly includes the auto-generated code header warning.
backend/src/main/java/sparqles/avro/analytics/EPViewInteroperabilityData.java (1)
482-539: LGTM: Robust serialization implementation
The custom encoding and decoding implementation is well-structured with proper handling of:
- Null values for optional fields
- Field order optimization
- Utf8 string type checking
backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityDataPoint.java (3)
8-11: LGTM! Improved encapsulation and serialization handling
The changes enhance the class structure by:
- Making fields private for better encapsulation
- Adding thread-safe binary message handling
- Providing clear schema definition
Also applies to: 18-86, 88-89
91-106: LGTM! Well-structured constructors and accessors
The implementation provides:
- Clear documentation
- Proper encapsulation through getters/setters
- Both default and all-args constructors
Also applies to: 147-181
394-425: LGTM! Robust custom encoding/decoding implementation
The implementation provides:
- Efficient direct field encoding/decoding
- Proper handling of field order differences
- Strong error handling for corrupt data
backend/src/main/java/sparqles/avro/Endpoint.java (2)
8-16: LGTM! Enhanced serialization capabilities added.
The addition of binary message encoding/decoding support will improve serialization performance.
182-368: LGTM! Robust builder implementation.
The builder implementation follows best practices:
- Deep copying of fields
- Proper validation
- Clear field state tracking
backend/src/main/java/sparqles/avro/analytics/DiscoverabilityView.java (3)
8-13: LGTM: Appropriate imports for binary message encoding/decoding.
The imports are correctly structured to support the Avro binary serialization functionality.
671-687: LGTM: Proper implementation of Java serialization.
The implementation of writeExternal and readExternal correctly delegates to Avro's serialization mechanism.
694-705: Verify custom encoding implementation.
The custom encoding implementation needs to be verified for compatibility with other versions of the schema. Ensure that the encoding order matches the schema evolution rules.
✅ Verification successful
Custom encoding implementation matches schema definition
The custom encoding implementation in DiscoverabilityView.java correctly follows the field order defined in the Avro schema:
- The encoding order in
customEncode()matches exactly with the field order inDiscoverabilityView.avsc:- endpoint (custom encoded)
- VoID (boolean)
- SD (boolean)
- serverName (string)
- lastUpdate (long)
The implementation is consistent with the schema definition and follows proper encoding practices for Avro serialization.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for other versions of the DiscoverabilityView schema to verify compatibility
fd --type f --extension avsc | xargs grep -l "DiscoverabilityView"
# Search for any custom encoding implementations in test files
rg "customEncode.*DiscoverabilityView" --type java
Length of output: 166
Script:
#!/bin/bash
# Let's examine the Avro schema definition
cat backend/src/main/avro/DiscoverabilityView.avsc
# Let's also check for any custom decode implementations to verify matching order
rg "customDecode.*DiscoverabilityView" --type java -A 10
# Search for any test cases that might verify encoding/decoding
rg "test.*DiscoverabilityView" --type java
Length of output: 477
backend/pom.xml (2)
180-182: LGTM! Good configuration for code formatting
The changes improve code formatting configuration:
- Correctly excludes Avro-generated files from formatting
- Uses Google Java style which is widely adopted
Also applies to: 185-185
194-210: Consider relocating generated sources to target directory
While the Avro plugin configuration is functional, generating sources directly in src/main/java could cause issues:
- Generated files might be accidentally committed
- Could interfere with source control
- May cause conflicts during clean builds
Consider this alternative configuration:
- <outputDirectory>${project.basedir}/src/main/java/</outputDirectory>
+ <outputDirectory>${project.build.directory}/generated-sources/avro</outputDirectory>Also, add:
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>build-helper-maven-plugin</artifactId>
<executions>
<execution>
<phase>generate-sources</phase>
<goals>
<goal>add-source</goal>
</goals>
<configuration>
<sources>
<source>${project.build.directory}/generated-sources/avro</source>
</sources>
</configuration>
</execution>
</executions>
</plugin>.editorconfig (2)
10-10: LGTM! Consistent indentation across file types.
The change aligns Java indentation with the global 2-space setting, improving consistency across the codebase.
10-10: Verify the impact on existing Java files.
This change affects all Java files in the project. Consider:
- Running a formatter on the entire codebase
- Adding a note to the commit message about the formatting change
- Documenting the transition in contributing guidelines
Let's check the potential impact:
backend/src/main/java/sparqles/avro/EndpointResult.java (2)
8-84: LGTM: Binary message encoding/decoding implementation.
The implementation follows Avro's best practices for binary serialization/deserialization:
- Proper initialization of encoder/decoder with schema and model
- Thread-safe singleton instances
- Comprehensive error handling in fromByteBuffer
533-569: Verify field order handling in customDecode.
The implementation handles field order differences correctly, but ensure that all callers properly handle the case where field order differs.
✅ Verification successful
Field order handling is correctly implemented across all callers
The search results show that all classes using customDecode() follow the same consistent pattern for handling field order differences:
- They all check
fieldOrderusingreadFieldOrderIfDiff() - They handle both cases (null and non-null fieldOrder) appropriately
- When fieldOrder is present, they use the position information correctly in switch statements
- All implementations properly initialize nested objects before calling their
customDecode()
The implementation in EndpointResult.java follows the same pattern used throughout the codebase, ensuring consistent field order handling across all Avro serialization/deserialization operations.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for customDecode usage to verify field order handling
rg "customDecode\(" -A 10
Length of output: 94025
backend/src/main/java/sparqles/avro/Dataset.java (5)
8-26: LGTM: Proper class setup with serialization support
The class is properly set up with serialization support and a well-defined Avro schema.
107-144: LGTM: Proper implementation of core record operations
The get/put methods are correctly implemented with proper bounds checking and error handling.
182-365: LGTM: Well-implemented Builder pattern
The Builder implementation is thorough with proper validation, deep copying, and error handling.
28-85: Verify binary serialization usage across the codebase
The binary serialization infrastructure is well-implemented. However, we should verify its usage pattern across the codebase to ensure consistent error handling.
✅ Verification successful
Binary serialization implementation is consistent across the codebase
The search results show that all Avro-generated classes in the codebase follow the same pattern for binary serialization:
- All classes consistently use the same error handling by throwing
IOException - The implementation is uniform across all classes using the same encoder/decoder pattern
- The ByteBuffer serialization methods are consistently implemented with proper exception declarations
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for Dataset serialization usage to verify error handling
rg -A 3 "toByteBuffer|fromByteBuffer" --type java
Length of output: 38592
368-422: LGTM: Robust custom encoding/decoding implementation
The custom encoding/decoding implementation is robust with proper UTF-8 handling. Let's verify its usage in integration tests.
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (3)
51-51: Add null checks for MongoDB manager.
The _dbm field could be null if setDBManager is not called before using the class.
244-271: Improve coherence and RS value handling.
The method has several issues:
- Empty blocks for negative values (
if (coherence < 0) ;) - Magic numbers in range comparisons
- Duplicated range logic structure
143-242: Verify performance impact of MongoDB operations.
The recalculateAvailabilityMonthly method performs multiple MongoDB operations in loops. For large datasets, this could impact performance.
✅ Verification successful
MongoDB query performance concerns are mitigated by existing indexes
The performance concerns in recalculateAvailabilityMonthly are adequately addressed:
- The MongoDB queries use indexed fields:
- An index exists on
endpoint.urifield which is used in the main query - The queries are time-range bounded using
endpointResult.start
- An index exists on
- The method runs as a monthly aggregation task, not in real-time serving paths
- Both
AEvol.javaandIndexViewAnalytics.javause identical query patterns, suggesting this is an established pattern in the codebase
While the method does perform queries in a loop over endpoints, this is necessary for the monthly aggregation logic and the presence of proper indexes ensures acceptable performance.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check for potential performance bottlenecks in MongoDB queries
# Search for MongoDB collection operations in loops
rg -A 5 'MongoCollection.*count.*for'
# Search for string concatenated MongoDB queries
rg "{'.*' \+ .*}"
Length of output: 153
Script:
#!/bin/bash
# Search for MongoDB collection operations in loops with proper escaping
rg -A 5 "MongoCollection.*count.*for"
# Search for MongoDB queries with string concatenation using different pattern
rg "{'endpointResult\.endpoint\.uri': '.*\+.*'"
# Look for all MongoDB collection operations to understand the query patterns
rg "atasksColl\.count\("
Length of output: 903
Script:
#!/bin/bash
# Look at the implementation of AEvol.java to compare query patterns
cat backend/src/main/java/sparqles/analytics/AEvol.java
# Check if there are any existing indexes defined for these queries
rg -B 3 -A 3 "createIndex|ensureIndex"
# Look for any existing performance-related configurations
rg -l "mongodb.*performance|batch.*size|bulk.*write"
Length of output: 8227
backend/src/main/java/sparqles/avro/analytics/EPViewInteroperability.java (3)
387-403: LGTM: Proper implementation of external serialization.
The implementation correctly uses Avro's DatumWriter/DatumReader for external serialization, ensuring compatibility with Avro's serialization format.
27-85: LGTM: Well-structured encoder/decoder initialization.
The static initialization of encoders and decoders follows best practices:
- Proper use of SpecificData model
- Thread-safe singleton pattern for encoders/decoders
- Comprehensive error handling in serialization methods
412-424: Consider using thread-safe collections for concurrent access.
The custom encoding implementation throws ConcurrentModificationException when the array size changes during iteration. While this catches concurrent modifications, consider using thread-safe collections (e.g., CopyOnWriteArrayList) if concurrent access is expected.
backend/src/main/java/sparqles/analytics/AEvol.java (1)
91-108:
Security: Use proper query parameter binding
Direct string concatenation in MongoDB queries is vulnerable to injection attacks. Use proper parameter binding instead.
Replace string concatenation with proper query objects:
-atasksColl.count("{'endpointResult.endpoint.uri': '" + e.getUri() +
- "', 'isAvailable':true, 'endpointResult.start': {$gte : " +
- cal.getTimeInMillis() + ", $lt : " + calEnd.getTimeInMillis() + "}}}");
+atasksColl.count("{" +
+ "'endpointResult.endpoint.uri': #, " +
+ "'isAvailable': #, " +
+ "'endpointResult.start': {$gte: #, $lt: #}" +
+ "}", e.getUri(), true, cal.getTimeInMillis(), calEnd.getTimeInMillis());Likely invalid or redundant comment.
backend/src/main/java/sparqles/avro/analytics/EPViewDiscoverabilityData.java (3)
72-87: LGTM! Proper implementation of serialization methods
The serialization/deserialization methods are correctly implemented with proper exception handling.
153-182: LGTM! Well-implemented accessors
The getter and setter methods are properly implemented with appropriate visibility.
30-34: Ensure thread-safety of static encoder/decoder
The static ENCODER and DECODER instances are shared across all instances. While they appear to be thread-safe based on Avro's implementation, document this assumption for future maintainers.
✅ Verification successful
Let me try a different approach to verify the thread safety of these Avro classes.
Let me try one more search to specifically look for the Avro encoder/decoder implementation details.
Let me try one final search to check Avro's BinaryMessageEncoder/Decoder implementation.
Based on the analysis of the codebase, I can now provide a final response:
Document thread-safety of Avro encoders/decoders
The static ENCODER and DECODER instances are generated by Avro and follow an immutable pattern - they are final fields initialized once during class loading with constant schema and model parameters. The implementation suggests they are designed for concurrent use:
- All Avro-generated classes follow the same pattern of static final encoders/decoders
- The encoders/decoders are effectively immutable after initialization
- No synchronization is used or needed for read-only access to these static instances
- The pattern is consistently used across all Avro-generated classes in the codebase
Add a class-level documentation comment to clarify the thread-safety:
/**
* Avro-generated class for EPViewDiscoverabilityData.
* The static ENCODER and DECODER instances are thread-safe and can be safely shared
* across multiple threads as they are immutable after initialization.
*/
@org.apache.avro.specific.AvroGenerated
public class EPViewDiscoverabilityData extends ...🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Verify thread-safety documentation in Avro's source
rg -A 3 "thread.*safe" --glob "**/*.java"
Length of output: 43
Script:
#!/bin/bash
# Search for BinaryMessageEncoder/Decoder implementation details
ast-grep --pattern 'class BinaryMessageEncoder {
$$$
}'
# Also check decoder
ast-grep --pattern 'class BinaryMessageDecoder {
$$$
}'
# Look for any synchronization or thread safety related patterns
rg -i "synchronized|volatile|concurrent|atomic" --glob "**/*.java"
Length of output: 6123
Script:
#!/bin/bash
# Search for BinaryMessageEncoder/Decoder usage and implementation
rg -A 5 "BinaryMessageEncoder|BinaryMessageDecoder" --glob "**/*.java"
# Look for any documentation about thread safety in the codebase
rg "Thread.*safe|thread.*safe|@ThreadSafe|@NotThreadSafe" --glob "**/*.java"
Length of output: 72220
Script:
#!/bin/bash
# Look for any documentation about thread safety in the model/schema classes
rg -A 3 "MODEL\$|SCHEMA\$" --glob "**/*.java"
# Look for any usage patterns of these encoders/decoders
rg -A 3 "getEncoder\(\)|getDecoder\(\)" --glob "**/*.java"
Length of output: 35887
backend/src/main/java/sparqles/analytics/StatsAnalyser.java (4)
27-28: Consider clarifying the year format.
Using YYYY for week-based years can be correct if you want ISO week-based date calculations, but it may lead to unexpected transitions near the year's boundary. Verify whether yyyy (calendar year) or YYYY (week year) meets your intended logic.
31-39: Validate time zone assumptions.
The trim method resets hours, minutes, and seconds to zero, which forces midnight in the system's default time zone. If you need a specific time zone or want UTC semantics, consider specifying it explicitly.
56-65: Return a proper Index or document the null return.
The call() method returns null. Confirm that it aligns with the Task<Index> contract. If an Index is expected, create and return a valid instance or explain why null is acceptable.
216-219: SetDBManager usage verified.
Method usage is straightforward and consistent with expected injection. No further issues found.
backend/src/main/java/sparqles/analytics/FAnalyser.java (2)
19-19: No issues with logger instantiation.
The usage of LoggerFactory.getLogger is standard practice.
76-94: Revisit handling of multiple InteroperabilityView entries.
When views.size() > 1, the code logs a warning and proceeds with the first entry. If there is a legitimate scenario for multiple entries, confirm there's no data inconsistency. Otherwise, consider error handling or resolution strategies for selecting the correct record.
backend/src/main/java/sparqles/analytics/PAnalyser.java (19)
21-22: Use consistent log naming convention
Declaring the logger instance with the name log is consistent with other classes' patterns; no issues here.
27-30: Consider null checks for 'pres'
Although it's not strictly necessary if the system guarantees a non-null pres, you may want to add a quick null check or assert to prevent potential NullPointerExceptions when logging or accessing pres.
31-32: Check for null getEndpointResult()
When calling pres.getEndpointResult(), ensure the EndpointResult is guaranteed. Otherwise, consider handling the case when getEndpointResult() returns null.
33-35: Clarity on retrieving PerformanceView and EPView
The logic for retrieving or creating a PerformanceView (via getView) and retrieving EPView is appropriate. Just be sure they're both used consistently in the calling code to prevent data misalignment.
36-40: Initialize statistics for cold vs warm
Creating separate SummaryStatistics objects for cold/warm is a well-organized approach. No issues found.
41-55: Good use of descriptive naming for test sets
Explicitly labeling your performance data as “Cold ASK Tests,” etc., helps maintain clarity. No immediate concerns.
56-62: Aggregation into lists
Combining askCold/askWarm and joinCold/joinWarm into dedicated lists is straightforward. Clean approach.
63-65: Assign data back to EPView
Storing the ask/join lists within the eppview object is consistent with the existing structure. Looks good.
66-68: Long variable 'limit' initialization
Initializing limit to zero and later modifying it is fine, but note that it’s used only when LIMIT entries occur. No problem here, just ensure no negative data edge cases.
69-93: ASK block logic
The approach to gather cold/warm stats and populate EPViewPerformanceDataValues is well-structured. No issues found.
94-113: JOIN block logic
Similar to the ASK block, the structure for collecting cold/warm data is consistent. No issues here.
114-122: LIMIT block modifies 'limit'
Updating the threshold based on the maximum number of solutions is correct. This logic is good.
124-125: Dual threshold assignment
Ensuring both eppview and pview share the same threshold ensures synchronization. Looks appropriate.
127-131: Mean performance metrics
Assigning average cold/warm stats using the helper method checkForNAN is clear and robust against NaN. Good practice.
133-135: Database update
Persisting both pview and epview is necessary so the aggregated data remains consistent.
140-145: Handler for NaN
Using a sentinel value of -1 for NaN is acceptable. Make sure the downstream code interprets -1 consistently as “no data” or “invalid.”
154-157: Creation of new PerformanceView
Looks correct and consistent with the rest of the code.
159-160: Use of retrieved or newly inserted view
Selecting index 0 from the views list if not empty is coherent. No issues found.
162-163: Return final view
The method returns the single (or newly created) PerformanceView. This is standard.
backend/src/main/java/sparqles/avro/analytics/EPViewCalculation.java (1)
1-1437: Auto-generated Avro class
This file appears to be fully auto-generated. Such classes typically mirror the schema without manual logic. No major issues are apparent. Ensure you keep the schema in sync with your business logic.
backend/src/main/java/sparqles/analytics/CAnalyser.java (7)
14-15: Class definition
Introducing CAnalyser as Analytics<CResult> is a good pattern to parallel your other dialect-specific analyzers (like PAnalyser).
30-36: Conditional toggling of VoID / VoIDPart
Using boolean logic to toggle VoID vs. VoIDPart helps keep track of partial vs. full VoID usage. Just confirm that your front-end or other components interpret these flags correctly.
37-43: Conditional toggling of SD / SDPart
Similar logic for SD. Make sure the meaning of these booleans remains consistent if other modules parse them.
49-62: Write CResult data into EPViewCalculation
Populating the cepview fields directly from pres is straightforward. No issues found.
70-79: Warning for multiple results
Printing a warning if more than one CalculationView is found for a single endpoint is valid. This suggests potential concurrency or data duplication issues; consider investigating if these warnings appear frequently.
80-88: Creation of new CalculationView
Defaulting numeric fields to -1.0 (coherence, RS) is consistent with other code. Just ensure your code interprets -1.0 as a sentinel for “undefined.”
89-90: Insert after creation
Inserting the new CalculationView into the database upon creation is an acceptable approach.
backend/src/main/java/sparqles/analytics/Analytics.java (4)
13-23: Additional imports for new result types
The added Avro imports like EPViewCalculation, etc., expand Analytics to handle new types (CResult, etc.). This is consistent with the new classes introduced.
34-34: Renamed logger variable
Renaming to a lowercase log aligns with common Java conventions. No issues.
38-40: Simplified constructor
Accepting the MongoDBManager and storing it is straightforward.
42-103: Enhanced getEPView logic
Creating an EPViewCalculation with default -1 values is consistent with other analyzers. Just ensure your usage of -1 is well-documented as a sentinel.
backend/src/main/java/org/osjava/norbert/NoRobotClient.java (3)
46-50: Track baseUrl
Storing baseUrl in a field ensures future checks in isUrlAllowed can be validated. This is sensible.
97-101: New parse method
Providing a direct string-based parse method is more flexible than the old URL-based approach. Good addition.
189-202: Validate 'url' vs. 'baseUrl' substring
In isUrlAllowed, substring logic depends on this.baseUrl.toExternalForm().length() - 1. This may break if there's no trailing slash or other edge cases. Consider verifying edge conditions to avoid off-by-one errors.
backend/src/main/java/sparqles/avro/analytics/EPViewAvailabilityData.java (2)
Line range hint 1-490: Warning: Auto-generated file has been modified directly
This is an auto-generated Avro class file as indicated by the header comment "DO NOT EDIT DIRECTLY". Any changes should be made to the corresponding Avro schema file (.avsc) instead of modifying this file directly.
Let's verify the schema changes and their impact:
#!/bin/bash
# Description: Analyze schema changes and verify backward compatibility
# Find the corresponding schema file
fd -e avsc | while read -r schema_file; do
if grep -q "EPViewAvailabilityData" "$schema_file"; then
echo "Found schema file: $schema_file"
# Check if schema changes maintain backward compatibility
if [ -f "$schema_file" ]; then
# Extract and compare schema versions
echo "Schema definition:"
cat "$schema_file"
# Check for any breaking changes in the schema
rg -A 5 "BREAKING.*CHANGE" "$schema_file" || echo "No breaking changes found in schema comments"
fi
fi
done372-376: LGTM! Improved error handling
The error handling improvements are well implemented:
- Specific handling of
AvroMissingFieldExceptionin the build method - Clear error message for array size mismatch during encoding using
ConcurrentModificationException
Also applies to: 417-419
| this.serverName = value; | ||
| fieldSetFlags()[3] = true; | ||
| return this; | ||
| } | ||
| this.serverName = | ||
| in.readString(this.serverName instanceof Utf8 ? (Utf8) this.serverName : null); | ||
|
|
||
| /** Checks whether the 'serverName' field has been set */ | ||
| public boolean hasServerName() { | ||
| return fieldSetFlags()[3]; | ||
| } | ||
| this.lastUpdate = in.readLong(); | ||
|
|
||
| /** Clears the value of the 'serverName' field */ | ||
| public sparqles.avro.analytics.DiscoverabilityView.Builder clearServerName() { | ||
| serverName = null; | ||
| fieldSetFlags()[3] = false; | ||
| return this; | ||
| } | ||
| } else { | ||
| for (int i = 0; i < 5; i++) { | ||
| switch (fieldOrder[i].pos()) { | ||
| case 0: | ||
| if (this.endpoint == null) { | ||
| this.endpoint = new sparqles.avro.Endpoint(); | ||
| } | ||
| this.endpoint.customDecode(in); | ||
| break; | ||
|
|
||
| /** Gets the value of the 'lastUpdate' field */ | ||
| public java.lang.Long getLastUpdate() { | ||
| return lastUpdate; | ||
| } | ||
| case 1: | ||
| this.VoID = in.readBoolean(); | ||
| break; | ||
|
|
||
| /** Sets the value of the 'lastUpdate' field */ | ||
| public sparqles.avro.analytics.DiscoverabilityView.Builder setLastUpdate(long value) { | ||
| validate(fields()[4], value); | ||
| this.lastUpdate = value; | ||
| fieldSetFlags()[4] = true; | ||
| return this; | ||
| } | ||
| case 2: | ||
| this.SD = in.readBoolean(); | ||
| break; | ||
|
|
||
| /** Checks whether the 'lastUpdate' field has been set */ | ||
| public boolean hasLastUpdate() { | ||
| return fieldSetFlags()[4]; | ||
| } | ||
| case 3: | ||
| this.serverName = | ||
| in.readString(this.serverName instanceof Utf8 ? (Utf8) this.serverName : null); | ||
| break; | ||
|
|
||
| /** Clears the value of the 'lastUpdate' field */ | ||
| public sparqles.avro.analytics.DiscoverabilityView.Builder clearLastUpdate() { | ||
| fieldSetFlags()[4] = false; | ||
| return this; | ||
| } | ||
| case 4: | ||
| this.lastUpdate = in.readLong(); | ||
| break; | ||
|
|
||
| @Override | ||
| public DiscoverabilityView build() { | ||
| try { | ||
| DiscoverabilityView record = new DiscoverabilityView(); | ||
| record.endpoint = | ||
| fieldSetFlags()[0] | ||
| ? this.endpoint | ||
| : (sparqles.avro.Endpoint) defaultValue(fields()[0]); | ||
| record.VoID = | ||
| fieldSetFlags()[1] | ||
| ? this.VoID | ||
| : (java.lang.Boolean) defaultValue(fields()[1]); | ||
| record.SD = | ||
| fieldSetFlags()[2] | ||
| ? this.SD | ||
| : (java.lang.Boolean) defaultValue(fields()[2]); | ||
| record.serverName = | ||
| fieldSetFlags()[3] | ||
| ? this.serverName | ||
| : (java.lang.CharSequence) defaultValue(fields()[3]); | ||
| record.lastUpdate = | ||
| fieldSetFlags()[4] | ||
| ? this.lastUpdate | ||
| : (java.lang.Long) defaultValue(fields()[4]); | ||
| return record; | ||
| } catch (Exception e) { | ||
| throw new org.apache.avro.AvroRuntimeException(e); | ||
| } | ||
| default: | ||
| throw new java.io.IOException("Corrupt ResolvingDecoder."); | ||
| } | ||
| } | ||
| } | ||
| } |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider adding schema version validation in customDecode.
The custom decoding implementation should validate the schema version to ensure compatibility during deserialization.
@Override
public void customDecode(org.apache.avro.io.ResolvingDecoder in) throws java.io.IOException {
+ // Validate schema version compatibility
+ org.apache.avro.Schema writerSchema = in.getWriterSchema();
+ if (!writerSchema.equals(SCHEMA$) && !SchemaCompatibility.checkReaderWriterCompatibility(SCHEMA$, writerSchema).isCompatible()) {
+ throw new IOException("Incompatible schema version detected during decoding");
+ }
org.apache.avro.Schema.Field[] fieldOrder = in.readFieldOrderIfDiff();Committable suggestion skipped: line range outside the PR's diff.
| @@ -6,7 +6,7 @@ | |||
| <artifactId>sparqles</artifactId> | |||
| <version>0.0.1-SNAPSHOT</version> | |||
| <properties> | |||
| <jdkLevel>11</jdkLevel> | |||
| <jdkLevel>17</jdkLevel> | |||
There was a problem hiding this comment.
💡 Codebase verification
Critical: MongoDB and Jongo dependencies require updates for Java 17 compatibility
The verification reveals critical compatibility issues:
- MongoDB Java Driver 2.11.3 (released in 2014) is not compatible with Java 17. The minimum recommended version for Java 17 support is 4.x
- Jongo 1.1 depends on this old MongoDB driver and hasn't been updated since 2015
Before proceeding with the Java 17 upgrade, you must:
- Upgrade mongo-java-driver to version 4.x (preferably latest stable)
- Consider replacing Jongo with the native MongoDB Java driver as Jongo is no longer maintained
- Test the application thoroughly after these upgrades as they involve significant API changes
🔗 Analysis chain
Verify compatibility with Java 17 upgrade
The upgrade from Java 11 to 17 is significant. While the change is good for leveraging newer Java features, we should verify compatibility with key dependencies:
- MongoDB Java Driver (2.11.3) is quite old
- Jongo (1.1) hasn't been updated in years
Also applies to: 58-58
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check if any dependencies explicitly declare Java version compatibility
# Look for maven.compiler.target in dependency POMs
echo "Checking Java compatibility declarations in dependencies..."
mvn dependency:tree -Dverbose | grep -B1 "maven.compiler"
Length of output: 280
Script:
#!/bin/bash
# Let's check the dependencies directly from the POM file
echo "Checking dependencies in pom.xml..."
cat backend/pom.xml | grep -A5 "<dependency>"
# Also check for MongoDB driver version specifically
echo -e "\nChecking MongoDB driver version..."
cat backend/pom.xml | grep -A3 "mongo"
# Check for Jongo version
echo -e "\nChecking Jongo version..."
cat backend/pom.xml | grep -A3 "jongo"
Length of output: 3967
| long size0 = in.readArrayStart(); | ||
| java.util.List<sparqles.avro.analytics.IndexAvailabilityDataPoint> a0 = this.values; | ||
| if (a0 == null) { | ||
| a0 = | ||
| new SpecificData.Array<sparqles.avro.analytics.IndexAvailabilityDataPoint>( | ||
| (int) size0, SCHEMA$.getField("values").schema()); | ||
| this.values = a0; | ||
| } else a0.clear(); | ||
| SpecificData.Array<sparqles.avro.analytics.IndexAvailabilityDataPoint> ga0 = | ||
| (a0 instanceof SpecificData.Array | ||
| ? (SpecificData.Array<sparqles.avro.analytics.IndexAvailabilityDataPoint>) a0 | ||
| : null); | ||
| for (; 0 < size0; size0 = in.arrayNext()) { | ||
| for (; size0 != 0; size0--) { | ||
| sparqles.avro.analytics.IndexAvailabilityDataPoint e0 = (ga0 != null ? ga0.peek() : null); | ||
| if (e0 == null) { | ||
| e0 = new sparqles.avro.analytics.IndexAvailabilityDataPoint(); | ||
| } | ||
| e0.customDecode(in); | ||
| a0.add(e0); | ||
| } | ||
| } |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Reduce code duplication in array decoding.
The array decoding logic is duplicated between the main path and the fieldOrder path. This makes maintenance harder and increases the chance of bugs when updating one path but not the other.
Extract the common array decoding logic into a private method:
+ private void decodeValuesArray(org.apache.avro.io.ResolvingDecoder in) throws java.io.IOException {
+ long size0 = in.readArrayStart();
+ java.util.List<sparqles.avro.analytics.IndexAvailabilityDataPoint> a0 = this.values;
+ if (a0 == null) {
+ a0 = new SpecificData.Array<>(
+ (int) size0, SCHEMA$.getField("values").schema());
+ this.values = a0;
+ } else a0.clear();
+ SpecificData.Array<sparqles.avro.analytics.IndexAvailabilityDataPoint> ga0 =
+ (a0 instanceof SpecificData.Array
+ ? (SpecificData.Array<sparqles.avro.analytics.IndexAvailabilityDataPoint>) a0
+ : null);
+ for (; 0 < size0; size0 = in.arrayNext()) {
+ for (; size0 != 0; size0--) {
+ sparqles.avro.analytics.IndexAvailabilityDataPoint e0 = (ga0 != null ? ga0.peek() : null);
+ if (e0 == null) {
+ e0 = new sparqles.avro.analytics.IndexAvailabilityDataPoint();
+ }
+ e0.customDecode(in);
+ a0.add(e0);
+ }
+ }
+ }Then use this method in both paths:
if (fieldOrder == null) {
this.key = in.readString(this.key instanceof Utf8 ? (Utf8) this.key : null);
- // Current array decoding logic
+ decodeValuesArray(in);
} else {
for (int i = 0; i < 2; i++) {
switch (fieldOrder[i].pos()) {
case 0:
this.key = in.readString(this.key instanceof Utf8 ? (Utf8) this.key : null);
break;
case 1:
- // Current array decoding logic
+ decodeValuesArray(in);
break;
default:
throw new java.io.IOException("Corrupt ResolvingDecoder.");
}
}
}Also applies to: 457-479
| public EndpointResult(sparqles.avro.Endpoint endpoint, java.lang.Long start, java.lang.Long end) { | ||
| this.endpoint = endpoint; | ||
| this.start = start; | ||
| this.end = end; | ||
| } |
There was a problem hiding this comment.
💡 Codebase verification
Potential NullPointerException risk in timestamp handling
Based on the code analysis:
- The fields
startandendare declared as primitivelong - The constructor accepts
Longobjects but performs direct assignment without null checks - Usage examples show both direct long values and
System.currentTimeMillis(), but there's potential for null inputs - The schema appears to use nullable Long fields based on the
defaultValuehandling
To prevent NPEs, add null checks before assignment:
public EndpointResult(sparqles.avro.Endpoint endpoint, Long start, Long end) {
this.endpoint = endpoint;
this.start = start != null ? start : 0L;
this.end = end != null ? end : 0L;
}🔗 Analysis chain
Verify timestamp handling in constructor.
The constructor accepts Long objects for timestamps but stores them as primitive longs. Ensure that null checks are performed before auto-unboxing to prevent NullPointerException.
Let me gather more information about the field declarations and potential null checks.
Let me check one more thing about the field types and schema.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for EndpointResult constructor usage to verify null handling
rg -A 5 "new EndpointResult\("
Length of output: 2349
Script:
#!/bin/bash
# Check field declarations and potential null checks in EndpointResult
ast-grep --pattern 'class EndpointResult {
$$$
private $_ start;
$$$
}'
# Check if there are any null checks for start/end fields
rg -B 2 -A 2 "\.start\s*=\s*" backend/src/main/java/sparqles/avro/EndpointResult.java
rg -B 2 -A 2 "\.end\s*=\s*" backend/src/main/java/sparqles/avro/EndpointResult.java
Length of output: 2851
Script:
#!/bin/bash
# Check the schema definition and field types
rg "private\s+(final\s+)?long\s+start" backend/src/main/java/sparqles/avro/EndpointResult.java
rg "private\s+(final\s+)?long\s+end" backend/src/main/java/sparqles/avro/EndpointResult.java
rg "schema\(" -A 10 backend/src/main/java/sparqles/avro/EndpointResult.java
Length of output: 1865
| atasksColl.count( | ||
| "{'endpointResult.endpoint.uri': '" | ||
| + e.getUri() | ||
| + "', 'isAvailable':true, 'endpointResult.start': {$gte : " | ||
| + cal.getTimeInMillis() | ||
| + ", $lt : " | ||
| + calEnd.getTimeInMillis() | ||
| + "}}}"); |
There was a problem hiding this comment.
Fix potential SQL injection vulnerability in MongoDB query.
The MongoDB query is constructed using string concatenation, which could be vulnerable to injection attacks. Use proper query builders or parameterized queries instead.
-atasksColl.count(
- "{'endpointResult.endpoint.uri': '"
- + e.getUri()
- + "', 'isAvailable':true, 'endpointResult.start': {$gte : "
- + cal.getTimeInMillis()
- + ", $lt : "
- + calEnd.getTimeInMillis()
- + "}}}");
+atasksColl.count(
+ new Document("endpointResult.endpoint.uri", e.getUri())
+ .append("isAvailable", true)
+ .append("endpointResult.start",
+ new Document("$gte", cal.getTimeInMillis())
+ .append("$lt", calEnd.getTimeInMillis())));📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| atasksColl.count( | |
| "{'endpointResult.endpoint.uri': '" | |
| + e.getUri() | |
| + "', 'isAvailable':true, 'endpointResult.start': {$gte : " | |
| + cal.getTimeInMillis() | |
| + ", $lt : " | |
| + calEnd.getTimeInMillis() | |
| + "}}}"); | |
| atasksColl.count( | |
| new Document("endpointResult.endpoint.uri", e.getUri()) | |
| .append("isAvailable", true) | |
| .append("endpointResult.start", | |
| new Document("$gte", cal.getTimeInMillis()) | |
| .append("$lt", calEnd.getTimeInMillis()))); |
| try { | ||
| Gson gson = new Gson(); | ||
|
|
||
| SPARQLESProperties.init(new java.io.File("src/main/resources/sparqles_docker.properties")); |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Avoid hard-coded configuration paths
The properties file path is hard-coded, which reduces flexibility and makes testing difficult.
Consider accepting the properties file path as a parameter or environment variable:
-SPARQLESProperties.init(new java.io.File("src/main/resources/sparqles_docker.properties"));
+String propsPath = System.getenv("SPARQLES_PROPS") != null ?
+ System.getenv("SPARQLES_PROPS") : "src/main/resources/sparqles_docker.properties";
+SPARQLESProperties.init(new java.io.File(propsPath));📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| SPARQLESProperties.init(new java.io.File("src/main/resources/sparqles_docker.properties")); | |
| String propsPath = System.getenv("SPARQLES_PROPS") != null ? | |
| System.getenv("SPARQLES_PROPS") : "src/main/resources/sparqles_docker.properties"; | |
| SPARQLESProperties.init(new java.io.File(propsPath)); |
| } catch (IOException e) { | ||
| e.printStackTrace(); | ||
| } catch (Exception e) { | ||
| e.printStackTrace(); | ||
| } |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve error handling and logging
The current error handling is too generic and uses printStackTrace(). Consider using a proper logging framework and handling specific exceptions.
Implement proper logging and specific exception handling:
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
public class AEvol {
+ private static final Logger logger = LoggerFactory.getLogger(AEvol.class);
// ...
- } catch (IOException e) {
- e.printStackTrace();
- } catch (Exception e) {
- e.printStackTrace();
+ } catch (IOException e) {
+ logger.error("Failed to read configuration file", e);
+ throw new RuntimeException("Configuration error", e);
+ } catch (Exception e) {
+ logger.error("Unexpected error during availability processing", e);
+ throw e;
}📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| } catch (IOException e) { | |
| e.printStackTrace(); | |
| } catch (Exception e) { | |
| e.printStackTrace(); | |
| } | |
| import org.slf4j.Logger; | |
| import org.slf4j.LoggerFactory; | |
| public class AEvol { | |
| private static final Logger logger = LoggerFactory.getLogger(AEvol.class); | |
| // ... | |
| } catch (IOException e) { | |
| logger.error("Failed to read configuration file", e); | |
| throw new RuntimeException("Configuration error", e); | |
| } catch (Exception e) { | |
| logger.error("Unexpected error during availability processing", e); | |
| throw e; | |
| } |
| // read the list of endpoints | ||
| MongoDBManager dbm = new MongoDBManager(); | ||
| Collection<Endpoint> eps = dbm.get(Endpoint.class, Endpoint.SCHEMA$); | ||
|
|
||
| // check if there is any stat to run or if it is up to date | ||
| // open connection to mongodb aEvol collection | ||
| Jongo jongo = | ||
| new Jongo( | ||
| new MongoClient( | ||
| SPARQLESProperties.getDB_HOST() + ":" + SPARQLESProperties.getDB_PORT()) | ||
| .getDB(SPARQLESProperties.getDB_NAME())); | ||
| MongoCollection amonthsColl = jongo.getCollection(MongoDBManager.COLL_AMONTHS); | ||
| // get last month |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Implement proper resource management
MongoDB connections and resources should be properly managed using try-with-resources to ensure cleanup.
Consider wrapping the MongoDB operations in try-with-resources:
-MongoDBManager dbm = new MongoDBManager();
-Collection<Endpoint> eps = dbm.get(Endpoint.class, Endpoint.SCHEMA$);
-Jongo jongo = new Jongo(new MongoClient(...).getDB(...));
+try (MongoClient mongoClient = new MongoClient(
+ SPARQLESProperties.getDB_HOST() + ":" + SPARQLESProperties.getDB_PORT())) {
+ MongoDBManager dbm = new MongoDBManager();
+ Collection<Endpoint> eps = dbm.get(Endpoint.class, Endpoint.SCHEMA$);
+ Jongo jongo = new Jongo(mongoClient.getDB(SPARQLESProperties.getDB_NAME()));
+ // ... rest of the code
+} catch (Exception e) {
+ logger.error("Failed to connect to MongoDB", e);
+ throw e;
+}Committable suggestion skipped: line range outside the PR's diff.
Both sources can now be used to generate availability graphs (index - weekly, amonths - monthly) Signed-off-by: Andrew Berezovskyi <[email protected]>
Signed-off-by: Andrew Berezovskyi <[email protected]>
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Actionable comments posted: 0
♻️ Duplicate comments (1)
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (1)
211-218:⚠️ Potential issueFix potential SQL injection vulnerability in MongoDB query.
The MongoDB query is constructed using string concatenation, which could be vulnerable to injection attacks. Use proper query builders or parameterized queries instead.
🧹 Nitpick comments (3)
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (3)
Line range hint
1-51: Consider implementing builder pattern for complex object creation.The class handles complex object creation and manipulation. Consider implementing the Builder pattern to improve code maintainability and readability, especially for the creation of Index objects.
797-815: Extract bin thresholds as constants in SimpleHistogram.The SimpleHistogram class uses magic numbers for bin thresholds. Consider extracting these values as constants for better maintainability.
class SimpleHistogram { + private static final double LOW_THRESHOLD = 0.05; + private static final double MEDIUM_THRESHOLD = 0.9; + private static final double HIGH_THRESHOLD = 0.95; + private static final double MAX_THRESHOLD = 1.0; int sampleSize = 0; int[] bin = {0, 0, 0, 0}; public void add(Double d) { - if (d <= 0.05) bin[0]++; - else if (0.05 < d && d <= 0.9) bin[1]++; - else if (0.9 < d && d <= 0.95) bin[2]++; - else if (0.95 < d && d <= 1) bin[3]++; + if (d <= LOW_THRESHOLD) bin[0]++; + else if (d <= MEDIUM_THRESHOLD) bin[1]++; + else if (d <= HIGH_THRESHOLD) bin[2]++; + else if (d <= MAX_THRESHOLD) bin[3]++; sampleSize++; } }
817-832: Address TODO comment in Count class.The
getTotal()method has a TODO comment. Implement proper documentation and remove the TODO comment.Would you like me to help implement a proper documentation for this method?
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backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java(3 hunks)
🔇 Additional comments (1)
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (1)
245-272: 🛠️ Refactor suggestion
Refactor calculation analysis for better maintainability.
The method has several issues:
- Empty blocks for negative values (
if (coherence < 0) ;) - Magic numbers in range comparisons
- Duplicated range logic structure
Consider extracting the range logic into a separate method and using constants for the thresholds.
+ private static final double[] COHERENCE_RANGES = {0.25, 0.5, 0.75, 0.95};
+ private static final String[] COHERENCE_LABELS = {
+ "[0.00-0.25[", "[0.25-0.50[", "[0.50-0.75[", "[0.75-0.95[", "[0.95-1.00]"
+ };
+ private static final double[] RS_RANGES = {10, 100, 1000, 10000};
+ private static final String[] RS_LABELS = {
+ "[0-10[", "[10-100[", "[100-1000[", "[1000-10000[", "[10000-)"
+ };
- private void analyseCalculation(EPViewCalculation calculation, Count<String>[] calcStats) {
+ private void analyseCalculation(EPViewCalculation calculation, Count<String>[] calcStats) {
+ addToStats(calculation.getCoherence(), calcStats[1], COHERENCE_RANGES, COHERENCE_LABELS);
+ addToStats(calculation.getRS(), calcStats[2], RS_RANGES, RS_LABELS);
+
+ updateMetadataStats(calculation, calcStats[0]);
+ }
+ private void addToStats(double value, Count<String> stats, double[] ranges, String[] labels) {
+ if (value < 0) return;
+
+ for (int i = 0; i < ranges.length; i++) {
+ if (value < ranges[i]) {
+ stats.add(labels[i]);
+ return;
+ }
+ }
+ stats.add(labels[labels.length - 1]);
+ }
+ private void updateMetadataStats(EPViewCalculation calculation, Count<String> stats) {
+ if (!calculation.getVoID().toString().isEmpty()) stats.add("VoID");
+ if (calculation.getVoIDPart()) stats.add("VoIDPart");
+ if (!calculation.getSD().toString().isEmpty()) stats.add("SD");
+ if (calculation.getSDPart()) stats.add("SDPart");
+ if (calculation.getCoherence() >= 0) stats.add("Coherence");
+ if (calculation.getRS() >= 0) stats.add("RS");
+ stats.add("total");
+ }Likely invalid or redundant comment.
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♻️ Duplicate comments (1)
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (1)
211-218:⚠️ Potential issueFix potential MongoDB injection vulnerability.
The MongoDB query is constructed using string concatenation, which could be vulnerable to injection attacks.
Use the MongoDB Document builder API instead:
-atasksColl.count( - "{'endpointResult.endpoint.uri': '" - + e.getUri() - + "', 'isAvailable':true, 'endpointResult.start': {$gte : " - + cal.getTimeInMillis() - + ", $lt : " - + calEnd.getTimeInMillis() - + "}}}"); +atasksColl.count( + new Document("endpointResult.endpoint.uri", e.getUri()) + .append("isAvailable", true) + .append("endpointResult.start", + new Document("$gte", cal.getTimeInMillis()) + .append("$lt", calEnd.getTimeInMillis())));
🧹 Nitpick comments (2)
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (2)
274-351: Reduce code duplication in the calculation update logic.The method contains duplicated code for sorting coherence and RS values.
Extract the common comparator and data processing logic:
+ private static class IndexViewCalculationDataValuesComparator + implements Comparator<IndexViewCalculationDataValues> { + @Override + public int compare(IndexViewCalculationDataValues o1, IndexViewCalculationDataValues o2) { + int diff = Comparator.comparingDouble( + (IndexViewCalculationDataValues value) -> value.getValue()) + .compare(o1, o2); + return diff == 0 ? -1 : diff; + } + } + private List<IndexViewCalculationData> processCalculationData( + String label, Count<String> data) { + List<IndexViewCalculationData> result = new ArrayList<>(); + List<IndexViewCalculationDataValues> values = new ArrayList<>(); + + TreeSet<IndexViewCalculationDataValues> set = new TreeSet<>( + new IndexViewCalculationDataValuesComparator()); + + for (String k : data.keySet()) { + set.add(new IndexViewCalculationDataValues( + k, data.get(k) / (double) data.getTotal())); + } + + for (IndexViewCalculationDataValues d : set.descendingSet()) { + values.add(d); + } + result.add(new IndexViewCalculationData(label, values)); + return result; + } private void updateCalculation(Index idx, Count[] object) { IndexViewCalculation iv = idx.getCalculation(); - Count<String> coherence = object[1]; - List<IndexViewCalculationData> l1 = new ArrayList<>(); - // ... existing coherence processing + iv.setCoherences(processCalculationData("Coherence", object[1])); - Count<String> rs = object[2]; - List<IndexViewCalculationData> l2 = new ArrayList<>(); - // ... existing RS processing + iv.setRss(processCalculationData("RS", object[2])); // Update stats Count<String> stats = object[0]; // ... rest of the method }
828-830: Remove misleading TODO comment and improve method documentation.The
getTotal()method has a TODO comment but is actually implemented to returnsampleSize.- public double getTotal() { - // TODO Auto-generated method stub - return sampleSize; - } + /** + * Returns the total number of samples added to this count. + * @return the total sample size + */ + public double getTotal() { + return sampleSize; + }
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backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java(3 hunks)
🔇 Additional comments (2)
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (2)
51-51: Add null checks for MongoDB manager.
The _dbm field could be null if setDBManager is not called before using the class.
245-272: 🛠️ Refactor suggestion
Improve coherence and RS value handling.
The method has several issues:
- Empty blocks for negative values (
if (coherence < 0) ;) - Magic numbers in range comparisons
- Duplicated range logic structure
Consider this refactoring to improve maintainability:
+ private static final double[] COHERENCE_RANGES = {0.25, 0.5, 0.75, 0.95};
+ private static final String[] COHERENCE_LABELS = {
+ "[0.00-0.25[", "[0.25-0.50[", "[0.50-0.75[", "[0.75-0.95[", "[0.95-1.00]"
+ };
+ private static final double[] RS_RANGES = {10, 100, 1000, 10000};
+ private static final String[] RS_LABELS = {
+ "[0-10[", "[10-100[", "[100-1000[", "[1000-10000[", "[10000-)"
+ };
- private void analyseCalculation(EPViewCalculation calculation, Count<String>[] calcStats) {
- double coherence = calculation.getCoherence();
- if (coherence < 0)
- ; // DO NOTHING
- else if (coherence < 0.25) calcStats[1].add("[0.00-0.25[");
- else if (coherence < 0.5) calcStats[1].add("[0.25-0.50[");
- else if (coherence < 0.75) calcStats[1].add("[0.50-0.75[");
- else if (coherence < 0.95) calcStats[1].add("[0.75-0.95[");
- else calcStats[1].add("[0.95-1.00]");
-
- double rs = calculation.getRS();
- if (rs < 0)
- ; // DO NOTHING
- else if (rs < 10) calcStats[2].add("[0-10[");
- else if (rs < 100) calcStats[2].add("[10-100[");
- else if (rs < 1000) calcStats[2].add("[100-1000[");
- else if (rs < 10000) calcStats[2].add("[1000-10000[");
- else calcStats[2].add("[10000-)");
+ private void analyseCalculation(EPViewCalculation calculation, Count<String>[] calcStats) {
+ addValueToStats(calculation.getCoherence(), calcStats[1], COHERENCE_RANGES, COHERENCE_LABELS);
+ addValueToStats(calculation.getRS(), calcStats[2], RS_RANGES, RS_LABELS);
+
+ // Update metadata stats
+ updateMetadataStats(calculation, calcStats[0]);
+ }
+
+ private void addValueToStats(double value, Count<String> stats, double[] ranges, String[] labels) {
+ if (value < 0) return;
+
+ for (int i = 0; i < ranges.length; i++) {
+ if (value < ranges[i]) {
+ stats.add(labels[i]);
+ return;
+ }
+ }
+ stats.add(labels[labels.length - 1]);
+ }
+
+ private void updateMetadataStats(EPViewCalculation calculation, Count<String> stats) {
+ if (!calculation.getVoID().toString().isEmpty()) stats.add("VoID");
+ if (calculation.getVoIDPart()) stats.add("VoIDPart");
+ if (!calculation.getSD().toString().isEmpty()) stats.add("SD");
+ if (calculation.getSDPart()) stats.add("SDPart");
+ if (calculation.getCoherence() >= 0) stats.add("Coherence");
+ if (calculation.getRS() >= 0) stats.add("RS");
+ stats.add("total");
+ }Likely invalid or redundant comment.
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♻️ Duplicate comments (2)
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (2)
212-219:⚠️ Potential issueFix potential MongoDB injection vulnerability.
The MongoDB query is constructed using string concatenation, which could be vulnerable to injection attacks.
Apply this diff to use proper document builders:
-atasksColl.count( - "{'endpointResult.endpoint.uri': '" - + e.getUri() - + "', 'isAvailable':true, 'endpointResult.start': {$gte : " - + cal.getTimeInMillis() - + ", $lt : " - + calEnd.getTimeInMillis() - + "}}}"); +atasksColl.count( + new Document("endpointResult.endpoint.uri", e.getUri()) + .append("isAvailable", true) + .append("endpointResult.start", + new Document("$gte", cal.getTimeInMillis()) + .append("$lt", calEnd.getTimeInMillis())));
246-273: 🛠️ Refactor suggestionImprove code structure in analyseCalculation method.
The method has several issues:
- Empty if blocks with "DO NOTHING" comments
- Duplicated range checking logic
- Magic numbers in the comparisons
Consider extracting the range logic into a reusable method and using constants for the thresholds. Apply this refactoring:
+ private static final double[] COHERENCE_RANGES = {0.25, 0.5, 0.75, 0.95}; + private static final String[] COHERENCE_LABELS = { + "[0.00-0.25[", "[0.25-0.50[", "[0.50-0.75[", "[0.75-0.95[", "[0.95-1.00]" + }; + private static final double[] RS_RANGES = {10, 100, 1000, 10000}; + private static final String[] RS_LABELS = { + "[0-10[", "[10-100[", "[100-1000[", "[1000-10000[", "[10000-)" + }; - private void analyseCalculation(EPViewCalculation calculation, Count<String>[] calcStats) { - double coherence = calculation.getCoherence(); - if (coherence < 0) - ; // DO NOTHING - else if (coherence < 0.25) calcStats[1].add("[0.00-0.25["); - // ... rest of the method + private void analyseCalculation(EPViewCalculation calculation, Count<String>[] calcStats) { + addValueToStats(calculation.getCoherence(), calcStats[1], COHERENCE_RANGES, COHERENCE_LABELS); + addValueToStats(calculation.getRS(), calcStats[2], RS_RANGES, RS_LABELS); + + updateMetadataStats(calculation, calcStats[0]); + } + private void addValueToStats(double value, Count<String> stats, double[] ranges, String[] labels) { + if (value < 0) return; + + for (int i = 0; i < ranges.length; i++) { + if (value < ranges[i]) { + stats.add(labels[i]); + return; + } + } + stats.add(labels[labels.length - 1]); + } + private void updateMetadataStats(EPViewCalculation calculation, Count<String> stats) { + if (!calculation.getVoID().toString().isEmpty()) stats.add("VoID"); + if (calculation.getVoIDPart()) stats.add("VoIDPart"); + if (!calculation.getSD().toString().isEmpty()) stats.add("SD"); + if (calculation.getSDPart()) stats.add("SDPart"); + if (calculation.getCoherence() >= 0) stats.add("Coherence"); + if (calculation.getRS() >= 0) stats.add("RS"); + stats.add("total"); + }
🧹 Nitpick comments (1)
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (1)
833-836: Remove TODO comment and document the method.The
getTotal()method has a TODO comment but the implementation is complete. Remove the TODO comment and add proper documentation.Apply this diff:
- public double getTotal() { - // TODO Auto-generated method stub - return sampleSize; - } + /** + * Returns the total number of samples counted. + * @return the total count of samples + */ + public double getTotal() { + return sampleSize; + }
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backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java(3 hunks)
🔇 Additional comments (1)
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (1)
Line range hint 1-47: LGTM! Well-structured class with clear dependencies.
The class is well-organized with appropriate imports and implements the Task interface correctly.
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Actionable comments posted: 3
🧹 Nitpick comments (3)
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (3)
44-50: Consider using enums for operation constants.The integer constants for different operations could be better organized using enums to improve type safety and readability.
- final int askCold = 0, askWarm = 1, joinCold = 2, joinWarm = 3; - final int sparql1_solMods = 0, - sparql1_com = 1, - sparql1_graph = 2, - sparql11_agg = 3, - sparql11_filter = 4, - sparql11_other = 5; + private enum PerformanceType { + ASK_COLD, ASK_WARM, JOIN_COLD, JOIN_WARM + } + + private enum SparqlFeatureType { + SPARQL1_SOL_MODS, + SPARQL1_COM, + SPARQL1_GRAPH, + SPARQL11_AGG, + SPARQL11_FILTER, + SPARQL11_OTHER + }
698-716: Improve encapsulation in SimpleHistogram class.The SimpleHistogram class exposes its internal state through public fields.
Consider this refactoring:
class SimpleHistogram { - int sampleSize = 0; - int[] bin = {0, 0, 0, 0}; + private int sampleSize = 0; + private final int[] bin = {0, 0, 0, 0}; + + public int getSampleSize() { + return sampleSize; + } + + public int[] getBins() { + return bin.clone(); + }
718-733: Enhance Count class implementation.The Count class extends TreeMap but doesn't fully implement the contract.
Consider:
- Adding proper serialVersionUID for serialization
- Implementing equals and hashCode methods
- Adding proper documentation for the generic type T
- Making the class final or documenting for inheritance
+ private static final long serialVersionUID = 1L; + + @Override + public boolean equals(Object o) { + if (this == o) return true; + if (!(o instanceof Count)) return false; + if (!super.equals(o)) return false; + Count<?> count = (Count<?>) o; + return sampleSize == count.sampleSize; + } + + @Override + public int hashCode() { + return Objects.hash(super.hashCode(), sampleSize); + }
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backend/src/main/java/sparqles/analytics/AEvol.java(1 hunks)backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java(3 hunks)
🔇 Additional comments (8)
backend/src/main/java/sparqles/analytics/AEvol.java (6)
4-7: Remove unused URL-related imports
These imports are no longer used since the code has moved from URL-based to MongoDB-based endpoint data retrieval.
-import java.io.BufferedReader;
-import java.io.IOException;
-import java.io.InputStreamReader;
-import java.net.URL;14-22: LGTM: Proper logging setup
Good job implementing proper logging using SLF4J Logger with correct initialization pattern.
57-73: LGTM: Calendar manipulation logic
The calendar manipulation logic for handling the last month and setting up time boundaries is well-implemented and handles edge cases correctly.
49-54: 🛠️ Refactor suggestion
Implement proper resource management
MongoDB client resources should be properly managed using try-with-resources.
-Jongo jongo =
- new Jongo(
- new MongoClient(
- SPARQLESProperties.getDB_HOST() + ":" + SPARQLESProperties.getDB_PORT())
- .getDB(SPARQLESProperties.getDB_NAME()));
+try (MongoClient mongoClient = new MongoClient(
+ SPARQLESProperties.getDB_HOST() + ":" + SPARQLESProperties.getDB_PORT())) {
+ Jongo jongo = new Jongo(mongoClient.getDB(SPARQLESProperties.getDB_NAME()));
+ // ... rest of the code
+}Likely invalid or redundant comment.
100-108:
Fix potential MongoDB injection vulnerability
The direct concatenation of e.getUri() into the MongoDB query string is vulnerable to injection attacks.
-"{'endpointResult.endpoint.uri': '" + e.getUri() + "', 'isAvailable':true, 'endpointResult.start': {$gte : " + cal.getTimeInMillis() + ", $lt : " + calEnd.getTimeInMillis() + "}}}"
+String query = "{" +
+ "'endpointResult.endpoint.uri': #, " +
+ "'isAvailable': true, " +
+ "'endpointResult.start': {" +
+ "$gte: #, " +
+ "$lt: #" +
+ "}" +
+"}";
+atasksColl.count(query, e.getUri(), cal.getTimeInMillis(), calEnd.getTimeInMillis())Likely invalid or redundant comment.
31-41: 🛠️ Refactor suggestion
Improve configuration management and error handling
The constructor has two areas for improvement:
- The properties file path is hard-coded, reducing flexibility
- The error handling could be more specific
public AEvol(String[] args) {
- SPARQLESProperties.init(new java.io.File("src/main/resources/sparqles_docker.properties"));
+ String propsPath = System.getenv("SPARQLES_PROPS");
+ if (propsPath == null) {
+ propsPath = "src/main/resources/sparqles_docker.properties";
+ log.warn("SPARQLES_PROPS not set, using default: {}", propsPath);
+ }
+ try {
+ SPARQLESProperties.init(new java.io.File(propsPath));
+ } catch (IOException e) {
+ log.error("Failed to initialize properties from {}", propsPath, e);
+ throw new RuntimeException("Failed to initialize properties", e);
+ }
MongoDBManager dbm = new MongoDBManager();
try {
recalculateMonthly(dbm);
} catch (Exception e) {
- log.error("Error while recalculating monthly data", e);
+ log.error("Failed to recalculate monthly data", e);
+ throw new RuntimeException("Monthly data recalculation failed", e);
}
}Likely invalid or redundant comment.
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java (2)
43-43: Add null checks for MongoDB manager.
The _dbm field could be null if setDBManager is not called before using the class.
142-169: 🛠️ Refactor suggestion
Fix empty if blocks and refactor range checks.
The method contains empty if blocks and duplicated range check logic.
- Remove empty if blocks
- Use a more maintainable range check structure:
- if (coherence < 0)
- ; // DO NOTHING
- else if (coherence < 0.25) calcStats[1].add("[0.00-0.25[");
- else if (coherence < 0.5) calcStats[1].add("[0.25-0.50[");
+ if (coherence >= 0) {
+ if (coherence < 0.25) calcStats[1].add("[0.00-0.25[");
+ else if (coherence < 0.5) calcStats[1].add("[0.25-0.50[");Likely invalid or redundant comment.
| // String json = readUrl("https://sparqles.demo.openlinksw.com/api/endpoint/lis"); | ||
| // AvailEpFromList[] epArray = gson.fromJson(json, AvailEpFromList[].class); |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Remove unused method and commented code
The readUrl method and its related commented code are no longer used since the implementation has moved to MongoDB.
Remove:
- The commented code at lines 88-89
- The entire
readUrlmethod (lines 135-149)
Also applies to: 135-149
| return diff; | ||
| } | ||
| }); | ||
|
|
||
| for (String k : server.keySet()) { | ||
| set.add( | ||
| new IndexViewDiscoverabilityDataValues( | ||
| k, server.get(k) / (double) server.getTotal())); | ||
| } | ||
| // iterate over all epviews and analyse them | ||
| for (EPView epv : epviews) { | ||
| log.trace("EPView: {}", epv); | ||
| analyseAvailability(epv.getAvailability(), weekHist); | ||
|
|
||
| for (IndexViewDiscoverabilityDataValues d : set.descendingSet()) { | ||
| lv.add(d); | ||
| } | ||
| l.add(new IndexViewDiscoverabilityData("Server Names", lv)); | ||
| iv.setServerName(l); | ||
|
|
||
| Count<String> stats = object[0]; | ||
| int v = 0; | ||
| if (stats.containsKey("no")) { | ||
| v = stats.get("no"); | ||
| iv.setNoDescription(v / (double) stats.get("total")); | ||
| } else iv.setNoDescription(0D); | ||
|
|
||
| v = stats.getOrDefault("sd", 0); | ||
|
|
||
| Integer totalVal = stats.get("total"); | ||
| if (totalVal != null) { | ||
| iv.setSDDescription(v / (double) totalVal); | ||
|
|
||
| v = stats.getOrDefault("void", 0); | ||
| iv.setVoIDDescription(v / (double) totalVal); | ||
| } else { | ||
| log.error("Total value is missing"); | ||
| iv.setSDDescription(-1.0); | ||
| iv.setVoIDDescription(-1.0); | ||
| } | ||
| } | ||
| // analyse performance | ||
| analysePerformance(epv.getPerformance(), perfStats, thresholdStats); | ||
|
|
||
| private void updateInteroperability(Index idx, SimpleHistogram[] interStats) { | ||
| IndexViewInteroperability iv = idx.getInteroperability(); | ||
| // analyse interoperability | ||
| analyseInteroperability(epv.getInteroperability(), interStats); | ||
|
|
||
| List<IndexViewInterData> v = new ArrayList<IndexViewInterData>(); | ||
| iv.setData(v); | ||
| // analyse discoverability | ||
| analyseDiscoverability(epv.getDiscoverability(), discoStats); | ||
|
|
||
| v.add(updateSPARQL1(interStats)); | ||
| v.add(updateSPARQL11(interStats)); | ||
| // analyse calculation | ||
| analyseCalculation(epv.getCalculation(), calcStats); | ||
| } | ||
|
|
||
| private IndexViewInterData updateSPARQL11(SimpleHistogram[] interStats) { | ||
| IndexViewInterData ivd = new IndexViewInterData(); | ||
| ivd.setColor("#2ca02c"); | ||
| ivd.setKey("SPARQL 1.1"); | ||
|
|
||
| ArrayList<IndexViewInterDataValues> v = new ArrayList<IndexViewInterDataValues>(); | ||
| // sparql1 mod | ||
| double perc = | ||
| interStats[sparql11_agg].bin[3] / (double) interStats[sparql11_agg].sampleSize; | ||
| v.add(new IndexViewInterDataValues("Aggregate", perc)); | ||
|
|
||
| // sparql1 com | ||
| perc = interStats[sparql11_filter].bin[3] / (double) interStats[sparql11_filter].sampleSize; | ||
| v.add(new IndexViewInterDataValues("Filter", perc)); | ||
|
|
||
| // sparql1 graph | ||
| perc = interStats[sparql11_other].bin[3] / (double) interStats[sparql11_other].sampleSize; | ||
| v.add(new IndexViewInterDataValues("Other", perc)); | ||
|
|
||
| ivd.setData(v); | ||
| // update the index view | ||
| updateAvailabilityStats(idx, weekHist); | ||
|
|
||
| // update performance stats | ||
| updatePerformanceStats(idx, perfStats, thresholdStats); | ||
|
|
||
| // update interoperability stats | ||
| updateInteroperability(idx, interStats); | ||
|
|
||
| // update discoverability stats | ||
| updateDiscoverability(idx, discoStats); | ||
|
|
||
| // update calculation stats | ||
| updateCalculation(idx, calcStats); | ||
|
|
||
| log.info("Updated view {}", idx); | ||
| _dbm.update(idx); | ||
|
|
||
| return idx; | ||
| } |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider breaking down the call method into smaller, focused methods.
The method is handling too many responsibilities and could be split into smaller methods for better maintainability and testing.
Consider extracting the following operations into separate methods:
- Index retrieval and initialization
- Statistics preparation
- Analytics processing
- Index update
Additionally:
- Add error handling for null
_dbm - Consider using builder pattern for statistics initialization
backend/src/main/java/sparqles/analytics/IndexViewAnalytics.java
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🔭 Outside diff range comments (1)
backend/src/main/config/log4j.properties (1)
The commented loggers should be uncommented as they are actively used in the codebase
The commented logger configurations correspond to actual logger usage in the codebase:
sparqles.core.availabilityis used inATask.javasparqles.core.discoveryis used inDTask.javasparqles.core.performanceis used inPTask.javasparqles.utils.ExceptionHandleris used inExceptionHandler.javaAll these classes have proper logger initialization using SLF4J, and their corresponding appender configurations (
alog,dlog,plog,exlog) are fully defined in the properties file. Commenting out these loggers might lead to missing important debug information from these core components.🔗 Analysis chain
Line range hint
1-9: Verify the intention of commented logger configurationsSeveral logger configurations are commented out. Please clarify if these are:
- Intentionally disabled for this PR
- Development configurations that should be removed
- Configurations that should be enabled
This will help maintain a clean and clear configuration file.
Let me verify if these loggers are actively used in the code by checking their actual usage.
Let me try a different approach to check logger usage with more context.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Check if these loggers are used in the codebase rg -l "sparqles\.core\.(availability|discovery|features|performance)" --type javaLength of output: 960
Script:
#!/bin/bash # Check for actual logger usage in the code rg "getLogger\(.*sparqles\.core\.(availability|discovery|features|performance)" --type java # Also check for ExceptionHandler logger usage rg "getLogger\(.*sparqles\.utils\.ExceptionHandler" --type javaLength of output: 159
Script:
#!/bin/bash # Check for logger declarations and usage with more context rg "Logger|LOG" -A 2 -B 2 backend/src/main/java/sparqles/core/availability/ATask.java backend/src/main/java/sparqles/core/discovery/DTask.java backend/src/main/java/sparqles/core/performance/PTask.java backend/src/main/java/sparqles/utils/ExceptionHandler.java # Also check if the file appenders referenced in comments are used elsewhere rg "alog|dlog|flog|plog|exlog" backend/src/main/config/Length of output: 8462
🧹 Nitpick comments (2)
backend/src/main/config/log4j.properties (2)
73-74: Clean up commented pattern layoutsMultiple pattern layouts are commented out without clear indication of which should be used. This creates maintenance overhead and confusion. Consider:
- Removing unused patterns
- Documenting why specific patterns are kept as comments (if needed for reference)
Also applies to: 82-87
91-94: Consider adjusting HTML appender thresholdThe DEBUG threshold for HTML logs might be too verbose and could generate large log files. Consider using INFO level unless detailed HTML logs are specifically needed.
-log4j.appender.HTML.Threshold=DEBUG +log4j.appender.HTML.Threshold=INFO
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🔇 Additional comments (2)
backend/src/main/config/log4j.properties (2)
11-65: Reduce configuration duplication and standardize namingThe issues highlighted in the previous review are still present:
- Significant configuration duplication across appenders
- Inconsistent naming (e.g., features logger vs interoperability.log)
Please refer to the previous review comment for detailed recommendations.
96-103: Review logging levels for production environmentPlease verify if these logging levels are appropriate for production:
sparqles.core.availability.ATask=DEBUGmight generate excessive logs- Commented
FaultDiagnostic=TRACEconfiguration should be cleaned up if not neededIndexViewAnalytics=DEBUGmight impact performanceConsider using INFO level by default and enabling DEBUG only when needed through runtime configuration.
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
|
The migrated code:
Additional changes:
|
Mainly two things:
Summary by CodeRabbit
Release Notes
New Features
CResult,CalculationView,EPViewCalculation, andIndexViewCalculationto enhance analytics capabilities.CTask, allowing for additional endpoint processing and analytics.Improvements
CTaskto theScheduleclass to manage additional task types.Bug Fixes
Documentation
Tests