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Modifications of VTL grammar

Usage of the VTL grammar​

Trevas on the VTL formal grammar expressed with EBNF. The reference is the version 2.0 upgrade published in July 2020 on the SDMX web site.

The grammar consists of two files ready to be processed by the Antlr parser generator:

  • VtlTokens.g4 contains the list of valid VTL terms.

  • Vtl.g4 contains the rules that produce valid VTL expressions.

Antlr uses these files to produce a lexer that creates a list of vocabulary symbols from an input character stream, and a parser that creates the grammatical structure corresponding to this list of symbols. Antlr can generate parsers usable in different target languages. Trevas uses the Java parser, which is exposed in the vtl-parser module.

Adaptations of the grammar​

In order to improve performance and functionalities, minor modifications were made to the VTL grammar used in Trevas.

Simplification of the grammatical tree​

As documented here and here, the expr and exprComp branches of the grammatical tree are nearly identical. In order to avoid implementing the same logic twice, the exprComp branch was commented out in the 498c1f8 commit. It was then noticed that this modification wrongly invalidated the COUNT() expression, and the corresponding rule was therefore reactivated in the grammar with the [54f86f2] (https://github.com/InseeFr/Trevas/commit/54f86f27d2e8fdd57df1439d74ed56d225064a7d) commit.

Addition of distance operators​

Distance operators like Levenshtein of Jaro-Winkler are commonly used in tests of character strings. In order to allow them in VTL expressions, the 036dc60 commit added to the grammar a distanceOperators section containing a LEVENSHTEIN rule, as well as the LEVENSHTEIN symbol in the lexer file.

Last updated on
- - +
Skip to main content

Modifications of VTL grammar

Usage of the VTL grammar​

Trevas on the VTL formal grammar expressed with EBNF. The reference is the version 2.0 upgrade published in July 2020 on the SDMX web site.

The grammar consists of two files ready to be processed by the Antlr parser generator:

  • VtlTokens.g4 contains the list of valid VTL terms.

  • Vtl.g4 contains the rules that produce valid VTL expressions.

Antlr uses these files to produce a lexer that creates a list of vocabulary symbols from an input character stream, and a parser that creates the grammatical structure corresponding to this list of symbols. Antlr can generate parsers usable in different target languages. Trevas uses the Java parser, which is exposed in the vtl-parser module.

Adaptations of the grammar​

In order to improve performance and functionalities, minor modifications were made to the VTL grammar used in Trevas.

Simplification of the grammatical tree​

As documented here and here, the expr and exprComp branches of the grammatical tree are nearly identical. In order to avoid implementing the same logic twice, the exprComp branch was commented out in the 498c1f8 commit. It was then noticed that this modification wrongly invalidated the COUNT() expression, and the corresponding rule was therefore reactivated in the grammar with the [54f86f2] (https://github.com/InseeFr/Trevas/commit/54f86f27d2e8fdd57df1439d74ed56d225064a7d) commit.

Addition of distance operators​

Distance operators like Levenshtein of Jaro-Winkler are commonly used in tests of character strings. In order to allow them in VTL expressions, the 036dc60 commit added to the grammar a distanceOperators section containing a LEVENSHTEIN rule, as well as the LEVENSHTEIN symbol in the lexer file.

Last updated on
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l=2;l{r.r(t),r.d(t,{assets:()=>m,contentTitle:()=>o,default:()=>p,frontMatter:()=>i,metadata:()=>s,toc:()=>l});var a=r(87462),n=(r(67294),r(3905));const i={id:"index-grammar",title:"Modifications of VTL grammar",sidebar_label:"Modifications of VTL grammar",slug:"/administrator-guide/grammar",custom_edit_url:null},o=void 0,s={unversionedId:"administrator-guide/grammar/index-grammar",id:"administrator-guide/grammar/index-grammar",title:"Modifications of VTL grammar",description:"Usage of the VTL grammar",source:"@site/docs/administrator-guide/grammar/index-grammar.mdx",sourceDirName:"administrator-guide/grammar",slug:"/administrator-guide/grammar",permalink:"/Trevas/administrator-guide/grammar",draft:!1,editUrl:null,tags:[],version:"current",lastUpdatedAt:1719441741,formattedLastUpdatedAt:"Jun 26, 2024",frontMatter:{id:"index-grammar",title:"Modifications of VTL grammar",sidebar_label:"Modifications of VTL 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The reference is the version 2.0 upgrade published in July 2020 ",(0,n.kt)("a",{parentName:"p",href:"https://sdmx.org/wp-content/uploads/VTL-2.0-EBNF-Grammar-2020-07.zip"},"on the SDMX web site"),"."),(0,n.kt)("p",null,"The grammar consists of two files ready to be processed by the ",(0,n.kt)("a",{parentName:"p",href:"https://www.antlr.org/"},"Antlr")," parser generator:"),(0,n.kt)("ul",null,(0,n.kt)("li",{parentName:"ul"},(0,n.kt)("p",{parentName:"li"},(0,n.kt)("a",{parentName:"p",href:"https://github.com/InseeFr/Trevas/blob/master/vtl-parser/src/main/antlr4/fr/insee/vtl/parser/VtlTokens.g4"},"VtlTokens.g4")," contains the list of valid VTL terms.")),(0,n.kt)("li",{parentName:"ul"},(0,n.kt)("p",{parentName:"li"},(0,n.kt)("a",{parentName:"p",href:"https://github.com/InseeFr/Trevas/blob/master/vtl-parser/src/main/antlr4/fr/insee/vtl/parser/Vtl.g4"},"Vtl.g4")," contains the rules that produce valid VTL expressions."))),(0,n.kt)("p",null,"Antlr uses these files to produce a lexer that creates a list of vocabulary symbols from an input character stream, and a parser that creates the grammatical structure corresponding to this list of symbols. 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\\n\\t\\n
\\n\\nTrevas supports the above SDMX message elements. Only the VtlMappingSchemes attribute is optional.\\n\\nThe elements in box 1 are used to produce Trevas DataStructures, filling VTL components attributes name, role, type, nullable and valuedomain.\\n\\nThe elements in box 2 are used to generate the VTL code (rulesets & transformations).\\n\\n#### Tools available\\n\\n#### `buildStructureFromSDMX3` utility\\n\\n`TrevasSDMXUtils.buildStructureFromSDMX3` allows to obtain a Trevas DataStructure.\\n\\nProviding corresponding data, you can build a Trevas Dataset.\\n\\n```java\\nStructured.DataStructure structure = TrevasSDMXUtils.buildStructureFromSDMX3(\\"path/sdmx_file.xml\\", \\"STRUCT_ID\\");\\n\\nSparkDataset ds = new SparkDataset(\\n spark.read()\\n .option(\\"header\\", \\"true\\")\\n .option(\\"delimiter\\", \\";\\")\\n .option(\\"quote\\", \\"\\\\\\"\\")\\n .csv(\\"path\\"),\\n structure\\n);\\n```\\n\\n#### `SDMXVTLWorkflow` object\\n\\nThe `SDMXVTLWorkflow` constructor takes 3 arguments:\\n\\n- a `ScriptEngine` (Trevas or another)\\n- a `ReadableDataLocation` to handle an SDMX message\\n- a map of names / Datasets\\n\\n```java\\nSparkSession.builder()\\n .appName(\\"test\\")\\n .master(\\"local\\")\\n .getOrCreate();\\n\\nScriptEngineManager mgr = new ScriptEngineManager();\\nScriptEngine engine = mgr.getEngineByExtension(\\"vtl\\");\\nengine.put(VtlScriptEngine.PROCESSING_ENGINE_NAMES, \\"spark\\");\\n\\nReadableDataLocation rdl = new ReadableDataLocationTmp(\\"src/test/resources/DSD_BPE_CENSUS.xml\\");\\n\\nSDMXVTLWorkflow sdmxVtlWorkflow = new SDMXVTLWorkflow(engine, rdl, Map.of());\\n```\\n\\nThis object then allows you to activate the following 3 functions.\\n\\n#### SDMXVTLWorkflow `run` function - Preview mode\\n\\nThe `run` function can easily be called in a preview mode, without attached data.\\n\\n```java\\nScriptEngineManager mgr = new ScriptEngineManager();\\nScriptEngine engine = mgr.getEngineByExtension(\\"vtl\\");\\nengine.put(VtlScriptEngine.PROCESSING_ENGINE_NAMES, \\"spark\\");\\n\\nReadableDataLocation rdl = new ReadableDataLocationTmp(\\"src/test/resources/DSD_BPE_CENSUS.xml\\");\\n\\nSDMXVTLWorkflow sdmxVtlWorkflow = new SDMXVTLWorkflow(engine, rdl, Map.of());\\n\\n// instead of using TrevasSDMXUtils.buildStructureFromSDMX3 and data sources\\n// to build Trevas Datasets, sdmxVtlWorkflow.getEmptyDatasets()\\n// will handle SDMX message structures to produce Trevas Datasets\\n// with metadata defined in this message, and adding empty data\\nMap emptyDatasets = sdmxVtlWorkflow.getEmptyDatasets();\\nengine.getBindings(ScriptContext.ENGINE_SCOPE).putAll(emptyDatasets);\\n\\nMap result = sdmxVtlWorkflow.run();\\n```\\n\\nThe preview mode allows to check the conformity of the SDMX file and the metadata of the output datasets.\\n\\n#### SDMXVTLWorkflow `run` function\\n\\nOnce an `SDMXVTLWorkflow` is built, it is easy to run the VTL validations and transformations defined in the SDMX file.\\n\\n```java\\nStructured.DataStructure structure = TrevasSDMXUtils.buildStructureFromSDMX3(\\"path/sdmx_file.xml\\", \\"ds1\\");\\n\\nSparkDataset ds1 = new SparkDataset(\\n spark.read()\\n .option(\\"header\\", \\"true\\")\\n .option(\\"delimiter\\", \\";\\")\\n .option(\\"quote\\", \\"\\\\\\"\\")\\n .csv(\\"path/data.csv\\"),\\n structure\\n);\\n\\nScriptEngineManager mgr = new ScriptEngineManager();\\nScriptEngine engine = mgr.getEngineByExtension(\\"vtl\\");\\nengine.put(VtlScriptEngine.PROCESSING_ENGINE_NAMES, \\"spark\\");\\n\\nMap inputs = Map.of(\\"ds1\\", ds1);\\n\\nReadableDataLocation rdl = new ReadableDataLocationTmp(\\"path/sdmx_file.xml\\");\\n\\nSDMXVTLWorkflow sdmxVtlWorkflow = new SDMXVTLWorkflow(engine, rdl, inputs);\\n\\nMap bindings = sdmxVtlWorkflow.run();\\n```\\n\\nAs a result, one will receive all the dataset defined as persistent in the `TransformationSchemes` definition.\\n\\n#### SDMXVTLWorkflow `getTransformationsVTL` function\\n\\nGets the VTL code corresponding to the SDMX TransformationSchemes definition.\\n\\n```java\\nSDMXVTLWorkflow sdmxVtlWorkflow = new SDMXVTLWorkflow(engine, rdl, Map.of());\\nString vtl = sdmxVtlWorkflow.getTransformationsVTL();\\n```\\n\\n#### SDMXVTLWorkflow `getRulesetsVTL` function\\n\\nGets the VTL code corresponding to the SDMX TransformationSchemes definition.\\n\\n```java\\nSDMXVTLWorkflow sdmxVtlWorkflow = new SDMXVTLWorkflow(engine, rdl, Map.of());\\nString dprs = sdmxVtlWorkflow.getRulesetsVTL();\\n```"},{"id":"/trevas-temporal-operators","metadata":{"permalink":"/Trevas/blog/trevas-temporal-operators","source":"@site/blog/2024-06-21-temporal-operators.mdx","title":"Trevas - Temporal operators","description":"Temporal operators in Trevas","date":"2024-06-21T00:00:00.000Z","formattedDate":"June 21, 2024","tags":[{"label":"Trevas","permalink":"/Trevas/blog/tags/trevas"}],"readingTime":2.845,"hasTruncateMarker":false,"authors":[{"name":"Hadrien Kohl","link":"https://github.com/hadrienk","title":"Hadrien Kohl Consulting - Developer","image":"/img/profile_pic_hadrien_kohl.jpg","key":"hadrien"}],"frontMatter":{"slug":"/trevas-temporal-operators","title":"Trevas - Temporal operators","authors":["hadrien"],"tags":["Trevas"]},"prevItem":{"title":"Trevas - SDMX","permalink":"/Trevas/blog/trevas-sdmx"},"nextItem":{"title":"Trevas - Java 17","permalink":"/Trevas/blog/trevas-java-17"}},"content":"### Temporal operators in Trevas\\n\\nThe version 1.4.1 of Trevas introduces preliminary support for date and time types\\nand operators.\\n\\nThe specification describes temporal types such as `date`, `time_period`, `time`, and `duration`. However, Trevas authors find\\nthese descriptions unsatisfactory. This blog post outlines our implementation choices and how they differ from the spec.\\n\\nIn the specification, `time_period` (and the types `date`) is described as a compound type with a start and end (or a\\nstart and a duration). This complicates the implementation and brings little value to the language as one can simply\\noperate on a combination of dates or date and duration directly. For this reason, we defined an algebra between the\\ntemporal types and did not yet implement the `time_period`.\\n\\n| result (operators) | date | duration | number |\\n| ------------------ | ----------- | --------------- | ------------- |\\n| **date** | n/a | date (+, -) | n/a |\\n| **duration** | date (+, -) | duration (+, -) | duration (\\\\*) |\\n| **number** | n/a | duration (\\\\*) | n/a |\\n\\nThe `period_indicator` function relies on period-awareness for types that are not defined enough at the moment to\\nbe implemented.\\n\\n#### Java mapping\\n\\nThe VTL type `date` is represented internally as the\\ntypes [`java.time.Instant`](https://docs.oracle.com/en%2Fjava%2Fjavase%2F11%2Fdocs%2Fapi%2F%2F/java.base/java/time/Instant.html),\\n[`java.time.ZonedDateTime`](https://docs.oracle.com/en%2Fjava%2Fjavase%2F11%2Fdocs%2Fapi%2F%2F/java.base/java/time/ZonedDateTime.html)\\nand [`java.time.OffsetDateTime`](https://docs.oracle.com/en%2Fjava%2Fjavase%2F11%2Fdocs%2Fapi%2F%2F/java.base/java/time/OffsetDateTime.html#:~:text=OffsetDateTime%20is%20an%20immutable%20representation,be%20stored%20in%20an%20OffsetDateTime%20.)\\n\\nInstant represent a specific moment in time. Note that this type does not include timezone information and is therefore\\nnot usable with all the operators. One can use the types `ZonedDateTime` and `OffsetDateTime` when timezone or time\\nsaving is required.\\n\\nThe VTL type `duration` is represented internally as the\\ntype [`org.threeten.extra.PeriodDuration`](https://www.threeten.org/threeten-extra/apidocs/org.threeten.extra/org/threeten/extra/PeriodDuration.html)\\nfrom the [threeten extra](https://www.threeten.org/threeten-extra/) package.\\nIt represents a duration using both calendar units (years, months, days) and a temporal amount (hours, minutes, seconds\\nand nanoseconds).\\n\\n#### Function `flow_to_stock`\\n\\nThe flow_to_stock function converts a data set with flow interpretation into a stock interpretation. This transformation\\nis useful when you want to aggregate flow data (e.g., sales or production rates) into cumulative stock data (e.g., total\\ninventory).\\n\\n**Syntax:**\\n\\n`result := flow_to_stock(op)`\\n\\n**Parameters:**\\n\\n- `op` - The input data set with flow interpretation. The data set must have an identifier of type `time`, additional\\n identifiers, and at least one measure of type `number`.\\n\\n**Result:**\\n\\nThe function returns a data set with the same structure as the input, but with the values converted to stock\\ninterpretation.\\n\\n#### Function `stock_to_flow`\\n\\nThe `stock_to_flow` function converts a data set with stock interpretation into a flow interpretation. This\\ntransformation is useful when you want to derive flow data from cumulative stock data.\\n\\n**Syntax:**\\n\\n`result := stock_to_flow(op)`\\n\\n**Parameters:**\\n\\n- `op` - The input data set with stock interpretation. The data set must have an identifier of type `time`, additional\\n identifiers, and at least one measure of type `number`.\\n\\n**Result:**\\n\\nThe function returns a data set with the same structure as the input, but with the values converted to flow\\ninterpretation.\\n\\n#### Function `timeshift`\\n\\nThe `timeshift` function shifts the time component of a specified range of time in the data set. This is useful for\\nanalyzing data at different time offsets, such as comparing current values to past values.\\n\\n**Syntax:**\\n\\n`result := timeshift(op, shiftNumber)`\\n\\n**Parameters:**\\n\\n- `op` - The operand data set containing time series.\\n- `shiftNumber` - An integer representing the number of periods to shift. Positive values shift forward in time, while\\n negative values shift backward.\\n\\n**Result:**\\n\\nThe function returns a data set with the time identifiers shifted by the specified number of periods."},{"id":"/trevas-java-17","metadata":{"permalink":"/Trevas/blog/trevas-java-17","source":"@site/blog/2023-11-22-trevas-java-17.mdx","title":"Trevas - Java 17","description":"News","date":"2023-11-22T00:00:00.000Z","formattedDate":"November 22, 2023","tags":[{"label":"Trevas","permalink":"/Trevas/blog/tags/trevas"}],"readingTime":0.345,"hasTruncateMarker":false,"authors":[{"name":"Nicolas Laval","link":"https://github.com/NicoLaval","title":"Making Sense - Developer","image":"/img/profile_pic_nicolas_laval.jpg","key":"nicolas"}],"frontMatter":{"slug":"/trevas-java-17","title":"Trevas - Java 17","authors":["nicolas"],"tags":["Trevas"]},"prevItem":{"title":"Trevas - Temporal operators","permalink":"/Trevas/blog/trevas-temporal-operators"},"nextItem":{"title":"Trevas - Persistent assignments","permalink":"/Trevas/blog/trevas-persistent-assignments"}},"content":"### News\\n\\nTrevas 1.2.0 enables Java 17 support.\\n\\n### Java modules handling\\n\\nSpark does not support Java modules.\\n\\nJava 17 client apps, embedding Trevas in Spark mode have to configure `UNNAMED` modules for Spark.\\n\\n#### Maven\\n\\nAdd to your `pom.xml` file, in the `build > plugins` section:\\n\\n```xml\\n\\n org.apache.maven.plugins\\n maven-compiler-plugin\\n 3.11.0\\n \\n \\n --add-exports\\n java.base/sun.nio.ch=ALL-UNNAMED\\n \\n \\n\\n```\\n\\n#### Docker\\n\\n```shell\\nENTRYPOINT [\\"java\\", \\"--add-exports\\", \\"java.base/sun.nio.ch=ALL-UNNAMED\\", \\"mainClass\\"]\\n```"},{"id":"/trevas-persistent-assignments","metadata":{"permalink":"/Trevas/blog/trevas-persistent-assignments","source":"@site/blog/2023-11-22-trevas-persistent-assignment.mdx","title":"Trevas - Persistent assignments","description":"News","date":"2023-11-22T00:00:00.000Z","formattedDate":"November 22, 2023","tags":[{"label":"Trevas","permalink":"/Trevas/blog/tags/trevas"}],"readingTime":0.41,"hasTruncateMarker":false,"authors":[{"name":"Nicolas Laval","link":"https://github.com/NicoLaval","title":"Making Sense - Developer","image":"/img/profile_pic_nicolas_laval.jpg","key":"nicolas"}],"frontMatter":{"slug":"/trevas-persistent-assignments","title":"Trevas - Persistent assignments","authors":["nicolas"],"tags":["Trevas"]},"prevItem":{"title":"Trevas - Java 17","permalink":"/Trevas/blog/trevas-java-17"},"nextItem":{"title":"Trevas - check_hierarchy","permalink":"/Trevas/blog/trevas-check_hierarchy"}},"content":"### News\\n\\nTrevas 1.2.0 includes the persistent assignment support: `ds1 <- ds;`.\\n\\nIn Trevas, persistent datasets are represented as `PersistentDataset`.\\n\\n### Handle `PersistentDataset`\\n\\nTrevas datasets are represented as `Dataset`.\\n\\nAfter running the Trevas engine, you can use persistent datasets with something like:\\n\\n```\\nBindings engineBindings = engine.getContext().getBindings(ScriptContext.ENGINE_SCOPE);\\nengineBindings.forEach((k, v) -> {\\n if (v instanceof PersistentDataset) {\\n fr.insee.vtl.model.Dataset ds = ((PersistentDataset) v).getDelegate();\\n if (ds instanceof SparkDataset) {\\n Dataset sparkDs = ((SparkDataset) ds).getSparkDataset();\\n // Do what you want with sparkDs\\n }\\n }\\n});\\n```"},{"id":"/trevas-check_hierarchy","metadata":{"permalink":"/Trevas/blog/trevas-check_hierarchy","source":"@site/blog/2023-09-01-trevas-check_hierarchy.mdx","title":"Trevas - check_hierarchy","description":"News","date":"2023-09-01T00:00:00.000Z","formattedDate":"September 1, 2023","tags":[{"label":"Trevas","permalink":"/Trevas/blog/tags/trevas"}],"readingTime":1.815,"hasTruncateMarker":false,"authors":[{"name":"Nicolas Laval","link":"https://github.com/NicoLaval","title":"Making Sense - Developer","image":"/img/profile_pic_nicolas_laval.jpg","key":"nicolas"}],"frontMatter":{"slug":"/trevas-check_hierarchy","title":"Trevas - check_hierarchy","authors":["nicolas"],"tags":["Trevas"]},"prevItem":{"title":"Trevas - Persistent assignments","permalink":"/Trevas/blog/trevas-persistent-assignments"},"nextItem":{"title":"Trevas Batch 0.1.1","permalink":"/Trevas/blog/trevas-batch-0.1.1"}},"content":"### News\\n\\nTrevas 1.1.0 includes hierarchical validation via operators `define hierarchical ruleset` and `check_hierarchy`.\\n\\n### Example\\n\\n#### Input\\n\\n`ds1`:\\n\\n| id | Me |\\n| :-: | :-: |\\n| ABC | 12 |\\n| A | 1 |\\n| B | 10 |\\n| C | 1 |\\n| DEF | 100 |\\n| E | 99 |\\n| F | 1 |\\n| HIJ | 100 |\\n| H | 99 |\\n| I | 0 |\\n\\n#### VTL script\\n\\n```\\n// Ensure ds1 metadata definition is good\\nds1 := ds1[calc identifier id := id, Me := cast(Me, integer)];\\n\\n// Define hierarchical ruleset\\ndefine hierarchical ruleset hr (variable rule Me) is\\n My_Rule : ABC = A + B + C errorcode \\"ABC is not sum of A,B,C\\" errorlevel 1;\\n DEF = D + E + F errorcode \\"DEF is not sum of D,E,F\\";\\n HIJ : HIJ = H + I - J errorcode \\"HIJ is not H + I - J\\" errorlevel 10\\nend hierarchical ruleset;\\n\\n// Check hierarchy\\nds_all := check_hierarchy(ds1, hr rule id all);\\nds_all_measures := check_hierarchy(ds1, hr rule id always_null all_measures);\\nds_invalid := check_hierarchy(ds1, hr rule id always_zero invalid);\\n```\\n\\n#### Outputs\\n\\n- `ds_all`\\n\\n| id | ruleid | bool_var | errorcode | errorlevel | imbalance |\\n| :-: | :-----: | :------: | :-------: | :--------: | :-------: |\\n| ABC | My_Rule | true | null | null | 0 |\\n\\n- `ds_always_null_all_measures`\\n\\n| id | Me | ruleid | bool_var | errorcode | errorlevel | imbalance |\\n| :-: | :-: | :-----: | :------: | :-------: | :--------: | :-------: |\\n| ABC | 12 | My_Rule | true | null | null | 0 |\\n| DEF | 100 | hr_2 | null | null | null | null |\\n| HIJ | 100 | HIJ | null | null | null | null |\\n\\n- `ds_invalid`\\n\\n| id | Me | ruleid | errorcode | errorlevel | imbalance |\\n| :-: | :-: | :----: | :------------------: | :--------: | :-------: |\\n| HIJ | 100 | HIJ | HIJ is not H + I - J | 10 | 1 |"},{"id":"/trevas-batch-0.1.1","metadata":{"permalink":"/Trevas/blog/trevas-batch-0.1.1","source":"@site/blog/2023-07-02-trevas-batch-0.1.1.mdx","title":"Trevas Batch 0.1.1","description":"Trevas Batch 0.1.1 uses version 1.0.2 of Trevas.","date":"2023-07-02T00:00:00.000Z","formattedDate":"July 2, 2023","tags":[{"label":"Trevas Batch","permalink":"/Trevas/blog/tags/trevas-batch"}],"readingTime":0.46,"hasTruncateMarker":false,"authors":[{"name":"Nicolas Laval","link":"https://github.com/NicoLaval","title":"Making Sense - Developer","image":"/img/profile_pic_nicolas_laval.jpg","key":"nicolas"}],"frontMatter":{"slug":"/trevas-batch-0.1.1","title":"Trevas Batch 0.1.1","authors":["nicolas"],"tags":["Trevas Batch"]},"prevItem":{"title":"Trevas - check_hierarchy","permalink":"/Trevas/blog/trevas-check_hierarchy"},"nextItem":{"title":"Trevas Jupyter 0.3.2","permalink":"/Trevas/blog/trevas-jupyter-0.3.2"}},"content":"import useBaseUrl from \'@docusaurus/useBaseUrl\';\\nimport Link from \'@theme/Link\';\\n\\n[Trevas Batch](https://github.com/Making-Sense-Info/Trevas-Batch) `0.1.1` uses version `1.0.2` of [Trevas](https://github.com/InseeFr/Trevas).\\n\\nThis Java batch provides Trevas execution metrics in Spark mode.\\n\\nThe configuration file to fill in is described in the [README](https://github.com/Making-Sense-Info/Trevas-Batch/tree/main#readme) of the project.\\nLaunching the batch will produce a Markdown file as output.\\n\\n### Launch\\n\\n#### Local\\n\\n```java\\njava -jar trevas-batch-0.1.1.jar -Dconfig.path=\\"...\\" -Dreport.path=\\"...\\"\\n```\\n\\nThe java execution will be done in local Spark.\\n\\n#### Kubernetes\\n\\nDefault Kubernetes objects are defined in the [.kubernetes](https://github.com/Making-Sense-Info/Trevas-Batch/tree/main/.kubernetes) folder.\\n\\nFeed the `config-map.yml` file then launch the job in your cluster."},{"id":"/trevas-jupyter-0.3.2","metadata":{"permalink":"/Trevas/blog/trevas-jupyter-0.3.2","source":"@site/blog/2023-07-01-v1-trevas-jupyter-0.3.2.mdx","title":"Trevas Jupyter 0.3.2","description":"Trevas Jupyter 0.3.2 uses version 1.0.2 of Trevas.","date":"2023-07-01T00:00:00.000Z","formattedDate":"July 1, 2023","tags":[{"label":"Trevas Jupyter","permalink":"/Trevas/blog/tags/trevas-jupyter"}],"readingTime":0.59,"hasTruncateMarker":false,"authors":[{"name":"Nicolas Laval","link":"https://github.com/NicoLaval","title":"Making Sense - Developer","image":"/img/profile_pic_nicolas_laval.jpg","key":"nicolas"}],"frontMatter":{"slug":"/trevas-jupyter-0.3.2","title":"Trevas Jupyter 0.3.2","authors":["nicolas"],"tags":["Trevas Jupyter"]},"prevItem":{"title":"Trevas Batch 0.1.1","permalink":"/Trevas/blog/trevas-batch-0.1.1"},"nextItem":{"title":"Trevas Lab 0.3.3","permalink":"/Trevas/blog/trevas-lab-0.3.3"}},"content":"import useBaseUrl from \'@docusaurus/useBaseUrl\';\\nimport Link from \'@theme/Link\';\\n\\n[Trevas Jupyter](https://github.com/InseeFrLab/Trevas-Jupyter) `0.3.2` uses version `1.0.2` of [Trevas](https://github.com/InseeFr/Trevas).\\n\\n### News\\n\\nIn addition to the greatly increased since the publication of Trevas 1.x.x, Trevas Jupyter offers 1 new connector:\\n- SAS files (via the `loadSas` method)\\n\\n### Launch\\n\\n#### Manually adding the Trevas Kernel to an existing Jupyter instance\\n\\n- Trevas Jupyter compiler\\n- Copy the `kernel.json` file and the `bin` and `repo` folders to a new kernel folder.\\n- Edit the `kernel.json` file\\n- Launch Jupyter\\n\\n#### Docker\\n\\n```bash\\ndocker pull inseefrlab/trevas-jupyter:0.3.2\\ndocker run -p 8888:8888 inseefrlab/trevas-jupyter:0.3.2\\n```\\n\\n#### Helm\\n\\nThe Trevas Jupyter docker image can be instantiated via the `jupyter-pyspark` Helm contract from [InseeFrLab](https://github.com/InseeFrLab/helm-charts-interactive-services/tree/main)."},{"id":"/trevas-lab-0.3.3","metadata":{"permalink":"/Trevas/blog/trevas-lab-0.3.3","source":"@site/blog/2023-07-01-v1-trevas-lab-0.3.3.mdx","title":"Trevas Lab 0.3.3","description":"Trevas Lab 0.3.3 uses version 1.0.2 of Trevas.","date":"2023-07-01T00:00:00.000Z","formattedDate":"July 1, 2023","tags":[{"label":"Trevas Lab","permalink":"/Trevas/blog/tags/trevas-lab"}],"readingTime":0.335,"hasTruncateMarker":false,"authors":[{"name":"Nicolas Laval","link":"https://github.com/NicoLaval","title":"Making Sense - Developer","image":"/img/profile_pic_nicolas_laval.jpg","key":"nicolas"}],"frontMatter":{"slug":"/trevas-lab-0.3.3","title":"Trevas Lab 0.3.3","authors":["nicolas"],"tags":["Trevas Lab"]},"prevItem":{"title":"Trevas Jupyter 0.3.2","permalink":"/Trevas/blog/trevas-jupyter-0.3.2"}},"content":"import useBaseUrl from \'@docusaurus/useBaseUrl\';\\nimport Link from \'@theme/Link\';\\n\\n[Trevas Lab](https://github.com/InseeFrLab/Trevas-Lab) `0.3.3` uses version `1.0.2` of [Trevas](https://github.com/InseeFr/Trevas).\\n\\n### News\\n\\nIn addition to the greatly increased since the publication of Trevas 1.x.x, Trevas Lab offers 2 new connectors:\\n- SAS files\\n- JDBC MariaDB\\n\\n### Launch\\n\\n#### Kubernetes\\n\\nSample Kubernetes objects are available in the `.kubernetes` folders of [Trevas Lab](https://github.com/InseeFrLab/Trevas-Lab/tree/master/.kubernetes) and [Trevas Lab UI](https://github.com/InseeFrLab/Trevas-Lab-UI/tree/master/.kubernetes)."}]}')}}]); \ No newline at end of file +"use strict";(self.webpackChunktrevas_documentation=self.webpackChunktrevas_documentation||[]).push([[2046],{84643:e=>{e.exports=JSON.parse('{"blogPosts":[{"id":"/trevas-sdmx","metadata":{"permalink":"/Trevas/blog/trevas-sdmx","source":"@site/blog/2024-06-25-trevas-sdmx.mdx","title":"Trevas - SDMX","description":"News","date":"2024-06-25T00:00:00.000Z","formattedDate":"June 25, 2024","tags":[{"label":"Trevas","permalink":"/Trevas/blog/tags/trevas"},{"label":"SDMX","permalink":"/Trevas/blog/tags/sdmx"}],"readingTime":0.7,"hasTruncateMarker":false,"authors":[{"name":"Nicolas Laval","link":"https://github.com/NicoLaval","title":"Making Sense - Developer","image":"/img/profile_pic_nicolas_laval.jpg","key":"nicolas"}],"frontMatter":{"slug":"/trevas-sdmx","title":"Trevas - SDMX","authors":["nicolas"],"tags":["Trevas","SDMX"]},"nextItem":{"title":"Trevas - Temporal operators","permalink":"/Trevas/blog/trevas-temporal-operators"}},"content":"import useBaseUrl from \'@docusaurus/useBaseUrl\';\\nimport ThemedImage from \'@theme/ThemedImage\';\\nimport Link from \'@theme/Link\';\\n\\n### News\\n\\nTrevas 1.4.1 introduces the VTL SDMX module.\\n\\nThis module enables to consume SDMX metadata sources to instantiate Trevas DataStructures and Datasets.\\n\\nIt also allows to execute the VTL TransformationSchemes to obtain the resulting persistent datasets.\\n\\n#### Overview\\n\\n
\\n\\t\\n
\\n\\nTrevas supports the above SDMX message elements. Only the VtlMappingSchemes element is optional.\\n\\nThe elements in box 1 are used to produce Trevas DataStructures, filling VTL components attributes name, role, type, nullable and valuedomain.\\n\\nThe elements in box 2 are used to generate the VTL code (rulesets & transformations).\\n\\n#### Tools available\\n\\nSDMX Trevas tools are documented .\\n\\n#### Troubleshooting\\n\\nHave a look to ."},{"id":"/trevas-temporal-operators","metadata":{"permalink":"/Trevas/blog/trevas-temporal-operators","source":"@site/blog/2024-06-21-temporal-operators.mdx","title":"Trevas - Temporal operators","description":"Temporal operators in Trevas","date":"2024-06-21T00:00:00.000Z","formattedDate":"June 21, 2024","tags":[{"label":"Trevas","permalink":"/Trevas/blog/tags/trevas"}],"readingTime":2.845,"hasTruncateMarker":false,"authors":[{"name":"Hadrien Kohl","link":"https://github.com/hadrienk","title":"Hadrien Kohl Consulting - Developer","image":"/img/profile_pic_hadrien_kohl.jpg","key":"hadrien"}],"frontMatter":{"slug":"/trevas-temporal-operators","title":"Trevas - Temporal operators","authors":["hadrien"],"tags":["Trevas"]},"prevItem":{"title":"Trevas - SDMX","permalink":"/Trevas/blog/trevas-sdmx"},"nextItem":{"title":"Trevas - Java 17","permalink":"/Trevas/blog/trevas-java-17"}},"content":"### Temporal operators in Trevas\\n\\nThe version 1.4.1 of Trevas introduces preliminary support for date and time types\\nand operators.\\n\\nThe specification describes temporal types such as `date`, `time_period`, `time`, and `duration`. However, Trevas authors find\\nthese descriptions unsatisfactory. This blog post outlines our implementation choices and how they differ from the spec.\\n\\nIn the specification, `time_period` (and the types `date`) is described as a compound type with a start and end (or a\\nstart and a duration). This complicates the implementation and brings little value to the language as one can simply\\noperate on a combination of dates or date and duration directly. For this reason, we defined an algebra between the\\ntemporal types and did not yet implement the `time_period`.\\n\\n| result (operators) | date | duration | number |\\n| ------------------ | ----------- | --------------- | ------------- |\\n| **date** | n/a | date (+, -) | n/a |\\n| **duration** | date (+, -) | duration (+, -) | duration (\\\\*) |\\n| **number** | n/a | duration (\\\\*) | n/a |\\n\\nThe `period_indicator` function relies on period-awareness for types that are not defined enough at the moment to\\nbe implemented.\\n\\n#### Java mapping\\n\\nThe VTL type `date` is represented internally as the\\ntypes [`java.time.Instant`](https://docs.oracle.com/en%2Fjava%2Fjavase%2F11%2Fdocs%2Fapi%2F%2F/java.base/java/time/Instant.html),\\n[`java.time.ZonedDateTime`](https://docs.oracle.com/en%2Fjava%2Fjavase%2F11%2Fdocs%2Fapi%2F%2F/java.base/java/time/ZonedDateTime.html)\\nand [`java.time.OffsetDateTime`](https://docs.oracle.com/en%2Fjava%2Fjavase%2F11%2Fdocs%2Fapi%2F%2F/java.base/java/time/OffsetDateTime.html#:~:text=OffsetDateTime%20is%20an%20immutable%20representation,be%20stored%20in%20an%20OffsetDateTime%20.)\\n\\nInstant represent a specific moment in time. Note that this type does not include timezone information and is therefore\\nnot usable with all the operators. One can use the types `ZonedDateTime` and `OffsetDateTime` when timezone or time\\nsaving is required.\\n\\nThe VTL type `duration` is represented internally as the\\ntype [`org.threeten.extra.PeriodDuration`](https://www.threeten.org/threeten-extra/apidocs/org.threeten.extra/org/threeten/extra/PeriodDuration.html)\\nfrom the [threeten extra](https://www.threeten.org/threeten-extra/) package.\\nIt represents a duration using both calendar units (years, months, days) and a temporal amount (hours, minutes, seconds\\nand nanoseconds).\\n\\n#### Function `flow_to_stock`\\n\\nThe flow_to_stock function converts a data set with flow interpretation into a stock interpretation. This transformation\\nis useful when you want to aggregate flow data (e.g., sales or production rates) into cumulative stock data (e.g., total\\ninventory).\\n\\n**Syntax:**\\n\\n`result := flow_to_stock(op)`\\n\\n**Parameters:**\\n\\n- `op` - The input data set with flow interpretation. The data set must have an identifier of type `time`, additional\\n identifiers, and at least one measure of type `number`.\\n\\n**Result:**\\n\\nThe function returns a data set with the same structure as the input, but with the values converted to stock\\ninterpretation.\\n\\n#### Function `stock_to_flow`\\n\\nThe `stock_to_flow` function converts a data set with stock interpretation into a flow interpretation. This\\ntransformation is useful when you want to derive flow data from cumulative stock data.\\n\\n**Syntax:**\\n\\n`result := stock_to_flow(op)`\\n\\n**Parameters:**\\n\\n- `op` - The input data set with stock interpretation. The data set must have an identifier of type `time`, additional\\n identifiers, and at least one measure of type `number`.\\n\\n**Result:**\\n\\nThe function returns a data set with the same structure as the input, but with the values converted to flow\\ninterpretation.\\n\\n#### Function `timeshift`\\n\\nThe `timeshift` function shifts the time component of a specified range of time in the data set. This is useful for\\nanalyzing data at different time offsets, such as comparing current values to past values.\\n\\n**Syntax:**\\n\\n`result := timeshift(op, shiftNumber)`\\n\\n**Parameters:**\\n\\n- `op` - The operand data set containing time series.\\n- `shiftNumber` - An integer representing the number of periods to shift. Positive values shift forward in time, while\\n negative values shift backward.\\n\\n**Result:**\\n\\nThe function returns a data set with the time identifiers shifted by the specified number of periods."},{"id":"/trevas-java-17","metadata":{"permalink":"/Trevas/blog/trevas-java-17","source":"@site/blog/2023-11-22-trevas-java-17.mdx","title":"Trevas - Java 17","description":"News","date":"2023-11-22T00:00:00.000Z","formattedDate":"November 22, 2023","tags":[{"label":"Trevas","permalink":"/Trevas/blog/tags/trevas"}],"readingTime":0.345,"hasTruncateMarker":false,"authors":[{"name":"Nicolas Laval","link":"https://github.com/NicoLaval","title":"Making Sense - Developer","image":"/img/profile_pic_nicolas_laval.jpg","key":"nicolas"}],"frontMatter":{"slug":"/trevas-java-17","title":"Trevas - Java 17","authors":["nicolas"],"tags":["Trevas"]},"prevItem":{"title":"Trevas - Temporal operators","permalink":"/Trevas/blog/trevas-temporal-operators"},"nextItem":{"title":"Trevas - Persistent assignments","permalink":"/Trevas/blog/trevas-persistent-assignments"}},"content":"### News\\n\\nTrevas 1.2.0 enables Java 17 support.\\n\\n### Java modules handling\\n\\nSpark does not support Java modules.\\n\\nJava 17 client apps, embedding Trevas in Spark mode have to configure `UNNAMED` modules for Spark.\\n\\n#### Maven\\n\\nAdd to your `pom.xml` file, in the `build > plugins` section:\\n\\n```xml\\n\\n org.apache.maven.plugins\\n maven-compiler-plugin\\n 3.11.0\\n \\n \\n --add-exports\\n java.base/sun.nio.ch=ALL-UNNAMED\\n \\n \\n\\n```\\n\\n#### Docker\\n\\n```shell\\nENTRYPOINT [\\"java\\", \\"--add-exports\\", \\"java.base/sun.nio.ch=ALL-UNNAMED\\", \\"mainClass\\"]\\n```"},{"id":"/trevas-persistent-assignments","metadata":{"permalink":"/Trevas/blog/trevas-persistent-assignments","source":"@site/blog/2023-11-22-trevas-persistent-assignment.mdx","title":"Trevas - Persistent assignments","description":"News","date":"2023-11-22T00:00:00.000Z","formattedDate":"November 22, 2023","tags":[{"label":"Trevas","permalink":"/Trevas/blog/tags/trevas"}],"readingTime":0.41,"hasTruncateMarker":false,"authors":[{"name":"Nicolas Laval","link":"https://github.com/NicoLaval","title":"Making Sense - Developer","image":"/img/profile_pic_nicolas_laval.jpg","key":"nicolas"}],"frontMatter":{"slug":"/trevas-persistent-assignments","title":"Trevas - Persistent assignments","authors":["nicolas"],"tags":["Trevas"]},"prevItem":{"title":"Trevas - Java 17","permalink":"/Trevas/blog/trevas-java-17"},"nextItem":{"title":"Trevas - check_hierarchy","permalink":"/Trevas/blog/trevas-check_hierarchy"}},"content":"### News\\n\\nTrevas 1.2.0 includes the persistent assignment support: `ds1 <- ds;`.\\n\\nIn Trevas, persistent datasets are represented as `PersistentDataset`.\\n\\n### Handle `PersistentDataset`\\n\\nTrevas datasets are represented as `Dataset`.\\n\\nAfter running the Trevas engine, you can use persistent datasets with something like:\\n\\n```\\nBindings engineBindings = engine.getContext().getBindings(ScriptContext.ENGINE_SCOPE);\\nengineBindings.forEach((k, v) -> {\\n if (v instanceof PersistentDataset) {\\n fr.insee.vtl.model.Dataset ds = ((PersistentDataset) v).getDelegate();\\n if (ds instanceof SparkDataset) {\\n Dataset sparkDs = ((SparkDataset) ds).getSparkDataset();\\n // Do what you want with sparkDs\\n }\\n }\\n});\\n```"},{"id":"/trevas-check_hierarchy","metadata":{"permalink":"/Trevas/blog/trevas-check_hierarchy","source":"@site/blog/2023-09-01-trevas-check_hierarchy.mdx","title":"Trevas - check_hierarchy","description":"News","date":"2023-09-01T00:00:00.000Z","formattedDate":"September 1, 2023","tags":[{"label":"Trevas","permalink":"/Trevas/blog/tags/trevas"}],"readingTime":1.815,"hasTruncateMarker":false,"authors":[{"name":"Nicolas Laval","link":"https://github.com/NicoLaval","title":"Making Sense - Developer","image":"/img/profile_pic_nicolas_laval.jpg","key":"nicolas"}],"frontMatter":{"slug":"/trevas-check_hierarchy","title":"Trevas - check_hierarchy","authors":["nicolas"],"tags":["Trevas"]},"prevItem":{"title":"Trevas - Persistent assignments","permalink":"/Trevas/blog/trevas-persistent-assignments"},"nextItem":{"title":"Trevas Batch 0.1.1","permalink":"/Trevas/blog/trevas-batch-0.1.1"}},"content":"### News\\n\\nTrevas 1.1.0 includes hierarchical validation via operators `define hierarchical ruleset` and `check_hierarchy`.\\n\\n### Example\\n\\n#### Input\\n\\n`ds1`:\\n\\n| id | Me |\\n| :-: | :-: |\\n| ABC | 12 |\\n| A | 1 |\\n| B | 10 |\\n| C | 1 |\\n| DEF | 100 |\\n| E | 99 |\\n| F | 1 |\\n| HIJ | 100 |\\n| H | 99 |\\n| I | 0 |\\n\\n#### VTL script\\n\\n```\\n// Ensure ds1 metadata definition is good\\nds1 := ds1[calc identifier id := id, Me := cast(Me, integer)];\\n\\n// Define hierarchical ruleset\\ndefine hierarchical ruleset hr (variable rule Me) is\\n My_Rule : ABC = A + B + C errorcode \\"ABC is not sum of A,B,C\\" errorlevel 1;\\n DEF = D + E + F errorcode \\"DEF is not sum of D,E,F\\";\\n HIJ : HIJ = H + I - J errorcode \\"HIJ is not H + I - J\\" errorlevel 10\\nend hierarchical ruleset;\\n\\n// Check hierarchy\\nds_all := check_hierarchy(ds1, hr rule id all);\\nds_all_measures := check_hierarchy(ds1, hr rule id always_null all_measures);\\nds_invalid := check_hierarchy(ds1, hr rule id always_zero invalid);\\n```\\n\\n#### Outputs\\n\\n- `ds_all`\\n\\n| id | ruleid | bool_var | errorcode | errorlevel | imbalance |\\n| :-: | :-----: | :------: | :-------: | :--------: | :-------: |\\n| ABC | My_Rule | true | null | null | 0 |\\n\\n- `ds_always_null_all_measures`\\n\\n| id | Me | ruleid | bool_var | errorcode | errorlevel | imbalance |\\n| :-: | :-: | :-----: | :------: | :-------: | :--------: | :-------: |\\n| ABC | 12 | My_Rule | true | null | null | 0 |\\n| DEF | 100 | hr_2 | null | null | null | null |\\n| HIJ | 100 | HIJ | null | null | null | null |\\n\\n- `ds_invalid`\\n\\n| id | Me | ruleid | errorcode | errorlevel | imbalance |\\n| :-: | :-: | :----: | :------------------: | :--------: | :-------: |\\n| HIJ | 100 | HIJ | HIJ is not H + I - J | 10 | 1 |"},{"id":"/trevas-batch-0.1.1","metadata":{"permalink":"/Trevas/blog/trevas-batch-0.1.1","source":"@site/blog/2023-07-02-trevas-batch-0.1.1.mdx","title":"Trevas Batch 0.1.1","description":"Trevas Batch 0.1.1 uses version 1.0.2 of Trevas.","date":"2023-07-02T00:00:00.000Z","formattedDate":"July 2, 2023","tags":[{"label":"Trevas Batch","permalink":"/Trevas/blog/tags/trevas-batch"}],"readingTime":0.46,"hasTruncateMarker":false,"authors":[{"name":"Nicolas Laval","link":"https://github.com/NicoLaval","title":"Making Sense - Developer","image":"/img/profile_pic_nicolas_laval.jpg","key":"nicolas"}],"frontMatter":{"slug":"/trevas-batch-0.1.1","title":"Trevas Batch 0.1.1","authors":["nicolas"],"tags":["Trevas Batch"]},"prevItem":{"title":"Trevas - check_hierarchy","permalink":"/Trevas/blog/trevas-check_hierarchy"},"nextItem":{"title":"Trevas Jupyter 0.3.2","permalink":"/Trevas/blog/trevas-jupyter-0.3.2"}},"content":"import useBaseUrl from \'@docusaurus/useBaseUrl\';\\nimport Link from \'@theme/Link\';\\n\\n[Trevas Batch](https://github.com/Making-Sense-Info/Trevas-Batch) `0.1.1` uses version `1.0.2` of [Trevas](https://github.com/InseeFr/Trevas).\\n\\nThis Java batch provides Trevas execution metrics in Spark mode.\\n\\nThe configuration file to fill in is described in the [README](https://github.com/Making-Sense-Info/Trevas-Batch/tree/main#readme) of the project.\\nLaunching the batch will produce a Markdown file as output.\\n\\n### Launch\\n\\n#### Local\\n\\n```java\\njava -jar trevas-batch-0.1.1.jar -Dconfig.path=\\"...\\" -Dreport.path=\\"...\\"\\n```\\n\\nThe java execution will be done in local Spark.\\n\\n#### Kubernetes\\n\\nDefault Kubernetes objects are defined in the [.kubernetes](https://github.com/Making-Sense-Info/Trevas-Batch/tree/main/.kubernetes) folder.\\n\\nFeed the `config-map.yml` file then launch the job in your cluster."},{"id":"/trevas-jupyter-0.3.2","metadata":{"permalink":"/Trevas/blog/trevas-jupyter-0.3.2","source":"@site/blog/2023-07-01-v1-trevas-jupyter-0.3.2.mdx","title":"Trevas Jupyter 0.3.2","description":"Trevas Jupyter 0.3.2 uses version 1.0.2 of Trevas.","date":"2023-07-01T00:00:00.000Z","formattedDate":"July 1, 2023","tags":[{"label":"Trevas Jupyter","permalink":"/Trevas/blog/tags/trevas-jupyter"}],"readingTime":0.59,"hasTruncateMarker":false,"authors":[{"name":"Nicolas Laval","link":"https://github.com/NicoLaval","title":"Making Sense - Developer","image":"/img/profile_pic_nicolas_laval.jpg","key":"nicolas"}],"frontMatter":{"slug":"/trevas-jupyter-0.3.2","title":"Trevas Jupyter 0.3.2","authors":["nicolas"],"tags":["Trevas Jupyter"]},"prevItem":{"title":"Trevas Batch 0.1.1","permalink":"/Trevas/blog/trevas-batch-0.1.1"},"nextItem":{"title":"Trevas Lab 0.3.3","permalink":"/Trevas/blog/trevas-lab-0.3.3"}},"content":"import useBaseUrl from \'@docusaurus/useBaseUrl\';\\nimport Link from \'@theme/Link\';\\n\\n[Trevas Jupyter](https://github.com/InseeFrLab/Trevas-Jupyter) `0.3.2` uses version `1.0.2` of [Trevas](https://github.com/InseeFr/Trevas).\\n\\n### News\\n\\nIn addition to the greatly increased since the publication of Trevas 1.x.x, Trevas Jupyter offers 1 new connector:\\n- SAS files (via the `loadSas` method)\\n\\n### Launch\\n\\n#### Manually adding the Trevas Kernel to an existing Jupyter instance\\n\\n- Trevas Jupyter compiler\\n- Copy the `kernel.json` file and the `bin` and `repo` folders to a new kernel folder.\\n- Edit the `kernel.json` file\\n- Launch Jupyter\\n\\n#### Docker\\n\\n```bash\\ndocker pull inseefrlab/trevas-jupyter:0.3.2\\ndocker run -p 8888:8888 inseefrlab/trevas-jupyter:0.3.2\\n```\\n\\n#### Helm\\n\\nThe Trevas Jupyter docker image can be instantiated via the `jupyter-pyspark` Helm contract from [InseeFrLab](https://github.com/InseeFrLab/helm-charts-interactive-services/tree/main)."},{"id":"/trevas-lab-0.3.3","metadata":{"permalink":"/Trevas/blog/trevas-lab-0.3.3","source":"@site/blog/2023-07-01-v1-trevas-lab-0.3.3.mdx","title":"Trevas Lab 0.3.3","description":"Trevas Lab 0.3.3 uses version 1.0.2 of Trevas.","date":"2023-07-01T00:00:00.000Z","formattedDate":"July 1, 2023","tags":[{"label":"Trevas Lab","permalink":"/Trevas/blog/tags/trevas-lab"}],"readingTime":0.335,"hasTruncateMarker":false,"authors":[{"name":"Nicolas Laval","link":"https://github.com/NicoLaval","title":"Making Sense - Developer","image":"/img/profile_pic_nicolas_laval.jpg","key":"nicolas"}],"frontMatter":{"slug":"/trevas-lab-0.3.3","title":"Trevas Lab 0.3.3","authors":["nicolas"],"tags":["Trevas Lab"]},"prevItem":{"title":"Trevas Jupyter 0.3.2","permalink":"/Trevas/blog/trevas-jupyter-0.3.2"}},"content":"import useBaseUrl from \'@docusaurus/useBaseUrl\';\\nimport Link from \'@theme/Link\';\\n\\n[Trevas Lab](https://github.com/InseeFrLab/Trevas-Lab) `0.3.3` uses version `1.0.2` of [Trevas](https://github.com/InseeFr/Trevas).\\n\\n### News\\n\\nIn addition to the greatly increased since the publication of Trevas 1.x.x, Trevas Lab offers 2 new connectors:\\n- SAS files\\n- JDBC MariaDB\\n\\n### Launch\\n\\n#### Kubernetes\\n\\nSample Kubernetes objects are available in the `.kubernetes` folders of [Trevas Lab](https://github.com/InseeFrLab/Trevas-Lab/tree/master/.kubernetes) and [Trevas Lab UI](https://github.com/InseeFrLab/Trevas-Lab-UI/tree/master/.kubernetes)."}]}')}}]); \ No newline at end of file diff --git a/assets/js/3e2612b7.30d02be2.js b/assets/js/3e2612b7.df5f687f.js similarity index 97% rename from assets/js/3e2612b7.30d02be2.js rename to assets/js/3e2612b7.df5f687f.js index 99334fe04..b0b0bfba2 100644 --- a/assets/js/3e2612b7.30d02be2.js +++ b/assets/js/3e2612b7.df5f687f.js @@ -1 +1 @@ -"use 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The input operand must be of boolean type. \n\n **Syntax** \n\n not op2 "}},{label:"between",kind:i,insertText:"between",range:e,documentation:{value:"The operator returns TRUE if op is greater than or equal to from and lower than or equal to to. \n\n **Syntax** \n\n between (op, from, to) "}},{label:"levenshtein",kind:i,insertText:"levenshtein",range:e,documentation:{value:"The operator returns the levenshtein distance between the two inputed strings. \n\n **Syntax** \n\n levenshtein (left, right) "}},{label:"random",kind:i,insertText:"random",range:e,documentation:{value:"The operator returns a random decimal number >= 0 and <1. \n\n **Syntax** \n\n random()"}},{label:"in",kind:o,insertText:"in",range:e,documentation:{value:"The in operator returns TRUE if op belongs to the collection, FALSE otherwise. \n\n **Syntax** \n\n op in collection "}},{label:"not_in",kind:o,insertText:"not_in",range:e,documentation:{value:"The not_in operator returns FALSE if op belongs to the collection, TRUE otherwise. \n\n **Syntax** \n\n op not_in collection "}},{label:"isnull",kind:i,insertText:"isnull",range:e,documentation:{value:"The operator returns TRUE if the value of the operand is NULL, FALSE otherwise. \n\n **Syntax** \n\n isnull(op) "}},{label:"ex",kind:o,insertText:"ex",range:e},{label:"union",kind:i,insertText:"union",range:e,documentation:{value:"The union operator implements the union of functions (i.e., Data Sets). \n\n **Syntax** \n\n union ( ds { , ds }*) "}},{label:"diff",kind:o,insertText:"diff",range:e},{label:"symdiff",kind:i,insertText:"symdiff",range:e,documentation:{value:"The operator implements the symmetric set difference between functions (i.e. Data Sets), interpreting the Data Points of the input Data Sets as the elements in the operand Sets. \n\n **Syntax** \n\n symdiff(ds1, ds) "}},{label:"intersect",kind:i,insertText:"intersect",range:e,documentation:{value:"The intersect operator implements the intersection of functions (i.e., Data Sets). \n\n **Syntax** \n\n intersect ( ds { , ds }*) "}},{label:"keys",kind:o,insertText:"KEYS",range:e},{label:"check",kind:i,insertText:"check",range:e,documentation:{value:"*Syntax*\n\n check ( op { errorcode errorcode } { errorlevel errorlevel } { imbalance imbalance } { output } ) \n\noutput ::= invalid | all "}},{label:"exists_in",kind:i,insertText:"exists_in",range:e,documentation:{value:"The operator returns TRUE if the value of the operand is NULL, FALSE otherwise. \n\n **Syntax** \n\n exists_in ( op1, op2 { , retain } ) \n\n *retain* ::= true | false | all"}},{label:"to",kind:o,insertText:"to",range:e},{label:"return",kind:o,insertText:"return",range:e},{label:"imbalance",kind:o,insertText:"imbalance",range:e,documentation:{value:"Imbalance is a numeric mono-measure Data Set with the same Identifiers of op. If not specified then imbalance is NULL"}},{label:"errorcode",kind:o,insertText:"errorcode",range:e},{label:"all",kind:o,insertText:"all",range:e},{label:"aggr",kind:o,insertText:"aggr",range:e,documentation:{value:"The operator aggr calculates aggregations of dependent Components (Measures or Attributes) on the basis of sub-expressions at Component level. \n\n **Syntax** \n\n op [ aggr aggrClause { groupingClause } ] "}},{label:"errorlevel",kind:o,insertText:"errorlevel",range:e},{label:"order",kind:o,insertText:"order",range:e},{label:"by",kind:o,insertText:"by",range:e},{label:"rank",kind:i,insertText:"rank",range:e,documentation:{value:"Rank (order number) of a Data Point in an ordered set of Data Points. \n\n **Syntax** \n\n rank( firstOperand { , additionalOperand }* { groupingClause } \n\n rank( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"asc",kind:o,insertText:"asc",range:e},{label:"desc",kind:o,insertText:"desc",range:e},{label:"min",kind:i,insertText:"min",range:e,documentation:{value:"Minimum value of a set of values . \n\n **Syntax** \n\n min( firstOperand { , additionalOperand }* { groupingClause } \n\n min( firstOperand { , additionalOperand }* over ( analyticClause ) "}},{label:"max",kind:i,insertText:"max",range:e,documentation:{value:"Maximum value of a set of values . \n\n **Syntax** \n\n max( firstOperand { , additionalOperand }* { groupingClause } \n\n max( firstOperand { , additionalOperand }* over ( analyticClause ) "}},{label:"first",kind:o,insertText:"first",range:e},{label:"last",kind:o,insertText:"last",range:e},{label:"indexof",kind:o,insertText:"indexof",range:e},{label:"abs",kind:i,insertText:"abs",range:e,documentation:{value:"The operator abs calculates the absolute value of a number. \n\n For example:\n\n abs(-3.141) gives 3.141 \n\n **Syntax** \n\n abs(op) "}},{label:"key",kind:o,insertText:"key",range:e},{label:"ln",kind:i,insertText:"ln",range:e,documentation:{value:"The operator ln calculates the natural logarithm of a number. \n\n **Syntax** \n\n ln(op) "}},{label:"log",kind:i,insertText:"log",range:e,documentation:{value:"The operator log calculates the logarithm of num base op. \n\n **Syntax** \n\n log(op , num) "}},{label:"trunc",kind:i,insertText:"trunc",range:e,documentation:{value:"The operator trunc truncates the operand to a number of positions at the right of the decimal point equal to the numDigit parameter. \n\n **Syntax** \n\n trunc ( op , numDigit ) "}},{label:"round",kind:i,insertText:"round",range:e,documentation:{value:"The operator round rounds the operand to a number of positions at the right of the decimal point equal to the numDigit parameter. \n\n **Syntax** \n\n round( op , numDigit ) "}},{label:"power",kind:i,insertText:"power",range:e,documentation:{value:"The operator power raises a number (the base) to another one (the exponent). \n\n **Syntax** \n\n power(base , exponent) "}},{label:"mod",kind:i,insertText:"mod",range:e,documentation:{value:"The operator mod returns the remainder of op1 divided by op2. It returns op1 if divisor op2 is 0. \n\n **Syntax** \n\n mod(op1, op2 ) "}},{label:"length",kind:i,insertText:"length",range:e,documentation:{value:"Returns the length of a string. \n\n **Syntax** \n\n len( op ) "}},{label:"trim",kind:i,insertText:"trim",range:e,documentation:{value:'Removes trailing and leading whitespace from a string. For example, trim("Hello ") gives "Hello". \n\n **Syntax** \n\n trim(operand) '}},{label:"upper",kind:i,insertText:"upper",range:e,documentation:{value:'Converts the character case of a string in upper case. For example, upper("Hello") gives "HELLO". \n\n **Syntax** \n\n upper(operand) '}},{label:"lower",kind:i,insertText:"lower",range:e,documentation:{value:'Converts the character case of a string in lower case. For example, lower("Hello") gives "hello". \n\n **Syntax** \n\n lower(operand) '}},{label:"substr",kind:i,insertText:"substr",range:e,documentation:{value:"The operator extracts a substring from op, which must be string type. The substring starts from the start character of the input string and has a number of characters equal to the length parameter. \n\n **Syntax** \n\n substr ( op, start, length ) "}},{label:"sum",kind:i,insertText:"sum",range:e,documentation:{value:"Sum of a set of numbers. \n\n **Syntax** \n\n sun( firstOperand { , additionalOperand }* { groupingClause } "}},{label:"avg",kind:i,insertText:"avg",range:e,documentation:{value:"Average value of a set of numbers. \n\n **Syntax** \n\n avg( firstOperand { , additionalOperand }* { groupingClause } \n\n avg( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"median",kind:i,insertText:"median",range:e,documentation:{value:"Median value of a set of values . \n\n **Syntax** \n\n median( firstOperand { , additionalOperand }* { groupingClause } \n\n median( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"count",kind:i,insertText:"count",range:e,documentation:{value:"Number of Data Points. \n\n **Syntax** \n\n count( firstOperand { , additionalOperand }* { groupingClause } \n\n count( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"identifier",kind:o,insertText:"identifier",range:e},{label:"measure",kind:o,insertText:"measure",range:e},{label:"attribute",kind:o,insertText:"attribute",range:e},{label:"filter",kind:o,insertText:"filter",range:e,documentation:{value:"The operator takes as input a Data Set (op) and a boolean Component expression (filterCondition) and filters the input Data Points according to the evaluation of the condition. \n\n **Syntax** \n\n op[ filter filterCondition ] "}},{label:"merge",kind:o,insertText:"merge",range:e},{label:"exp",kind:i,insertText:"exp",range:e,documentation:{value:"The operator exp returns e (base of the natural logarithm) raised to the op-th power. \n\n **Syntax** \n\n exp(op) "}},{label:"componentRole",kind:o,insertText:"componentRole",range:e},{label:"viral",kind:o,insertText:"viral",range:e},{label:"match_characters",kind:i,insertText:"match_characters",range:e,documentation:{value:"match_characters returns TRUE if op matches the regular expression regexp, FALSE otherwise. \n\n **Syntax** \n\n match_characters ( op , pattern ) "}},{label:"type",kind:o,insertText:"type",range:e},{label:"nvl",kind:i,insertText:"nvl",range:e,documentation:{value:"The operator nvl returns op2 when op1 is null, otherwise op1. \n\n **Syntax** \n\n nvl ( op1 , op2 ) "}},{label:"hierarchy",kind:i,insertText:"hierarchy",range:e,documentation:{value:"The hierarchy operator applies the rules of hr to op as specified in the parameters. \n\n **Syntax** \n\n hierarchy( op , hr { condition condComp { , condComp }* } { rule ruleComp } { mode } { input } { output } ) "}},{label:"invalid",kind:o,insertText:"invalid",range:e},{label:"valuedomain",kind:o,insertText:"valuedomain",range:e},{label:"variable",kind:o,insertText:"variable",range:e},{label:"data",kind:o,insertText:"data",range:e},{label:"structure",kind:o,insertText:"structure",range:e},{label:"dataset",kind:o,insertText:"dataset",range:e},{label:"operator",kind:o,insertText:"operator",range:e},{label:"define",kind:o,insertText:"define",range:e},{label:"datapoint",kind:o,insertText:"datapoint",range:e},{label:"hierarchical",kind:o,insertText:"hierarchical",range:e},{label:"ruleset",kind:o,insertText:"ruleset",range:e},{label:"rule",kind:o,insertText:"rule",range:e},{label:"end",kind:o,insertText:"end",range:e},{label:"alterDataset",kind:o,insertText:"alterDataset",range:e},{label:"ltrim",kind:i,insertText:"ltrim",range:e,documentation:{value:'Removes leading whitespace from a string. For example, trim(" Hello ") gives "Hello ". \n\n **Syntax** \n\n ltrim(operand) '}},{label:"rtrim",kind:i,insertText:"rtrim",range:e,documentation:{value:'Removes trailing whitespace from a string. For example, trim("Hello ") gives "Hello". \n\n **Syntax** \n\n rtrim(operand) '}},{label:"instr",kind:i,insertText:"instr",range:e,documentation:{value:"The operator returns the position in the input string of a specified string (pattern). \n\n **Syntax** \n\n instr ( op, pattern, start, occurrence ) "}},{label:"replace",kind:i,insertText:"replace",range:e,documentation:{value:"Replaces all the occurrences of a specified string-pattern (pattern1) with another one (pattern2). If pattern2 is omitted then all occurrences of pattern1 are removed \n\n **Syntax** \n\n replace (op , pattern1, pattern2 ) "}},{label:"ceil",kind:i,insertText:"ceil",range:e,documentation:{value:"The operator ceil returns the smallest integer greater than or equal to op. \n\n For example:\n\n ceil(3.14159) gives 4 \n\n **Syntax** \n\n ceil(op) "}},{label:"floor",kind:i,insertText:"floor",range:e,documentation:{value:"The operator floor returns the greatest integer which is smaller than or equal to op. \n\n For example:\n\n floor(3.14159) gives 3 \n\n **Syntax** \n\n floor(op) "}},{label:"sqrt",kind:i,insertText:"sqrt",range:e,documentation:{value:"The operator sqrt calculates the square root of a number. \n\n **Syntax** \n\n sqrt(op) "}},{label:"any",kind:o,insertText:"any",range:e},{label:"setdiff",kind:i,insertText:"setdiff",range:e,documentation:{value:"The operator implements the set difference of functions (i.e. Data Sets), interpreting the Data Points of the input Data Sets as the elements belonging to the operand sets, the minuend and the subtrahend, respectively. \n\n **Syntax** \n\n setdiff(ds1, ds2) "}},{label:"stddev_pop",kind:i,insertText:"stddev_pop",range:e,documentation:{value:"Population standard deviation of a set of numbers. \n\n **Syntax** \n\n stddev_pop( firstOperand { , additionalOperand }* { groupingClause } \n\n stddev_pop( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"stddev_samp",kind:i,insertText:"stddev_samp",range:e,documentation:{value:"Sample standard deviation of a set of numbers. \n\n **Syntax** \n\n stddev_samp( firstOperand { , additionalOperand }* { groupingClause } \n\n stddev_samp( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"var_pop",kind:i,insertText:"var_pop",range:e,documentation:{value:"Population variance of a set of numbers. \n\n **Syntax** \n\n var_pop( firstOperand { , additionalOperand }* { groupingClause } \n\n var_pop( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"var_samp",kind:i,insertText:"var_samp",range:e,documentation:{value:"Sample variance of a set of numbers. \n\n **Syntax** \n\n var_samp( firstOperand { , additionalOperand }* { groupingClause } \n\n var_samp( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"group",kind:o,insertText:"group",range:e},{label:"except",kind:o,insertText:"except",range:e},{label:"having",kind:o,insertText:"having",range:e},{label:"first_value",kind:i,insertText:"first_value",range:e,documentation:{value:"First value in an ordered set of values . \n\n **Syntax** \n\n first_value( firstOperand { , additionalOperand }* { groupingClause } \n\n first_value( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"last_value",kind:i,insertText:"last_value",range:e,documentation:{value:"Last value in an ordered set of values. \n\n **Syntax** \n\n last_value( firstOperand { , additionalOperand }* { groupingClause } \n\n last_value( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"lag",kind:i,insertText:"lag",range:e,documentation:{value:"In an ordered set of Data Points, it returns the value(s) taken from a Data Point at a given physical offset prior to the current Data Point. \n\n **Syntax** \n\n lag( firstOperand { , additionalOperand }* { groupingClause } \n\n lag( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"lead",kind:i,insertText:"lead",range:e,documentation:{value:"In an ordered set of Data Points, it returns the value(s) taken from a Data Point at a given physical offset beyond the current Data Point . \n\n **Syntax** \n\n lead( firstOperand { , additionalOperand }* { groupingClause } \n\n lead( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"ratio_to_report",kind:i,insertText:"ratio_to_report",range:e,documentation:{value:"In an ordered set of Data Points, it returns the value(s) taken from a Data Point at a given physical offset beyond the current Data Point . \n\n **Syntax** \n\n ratio_to_report( firstOperand { , additionalOperand }* { groupingClause } \n\n ratio_to_report( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"over",kind:o,insertText:"over",range:e},{label:"preceding",kind:o,insertText:"preceding",range:e},{label:"following",kind:o,insertText:"following",range:e},{label:"unbounded",kind:o,insertText:"unbounded",range:e},{label:"partition",kind:o,insertText:"partition",range:e},{label:"rows",kind:o,insertText:"rows",range:e},{label:"range",kind:o,insertText:"range",range:e},{label:"current",kind:o,insertText:"current",range:e},{label:"valid",kind:o,insertText:"valid",range:e},{label:"fill_time_series",kind:i,insertText:"fill_time_series",range:e,documentation:{value:"The operator fills the possibly missing Data Points of all the time series belonging to the operand op within the time limits automatically determined by applying the limit_method. \n\n **Syntax** \n\n fill_time_series( op { , limitsMethod}) \n\n *limitsMethod* ::= single | all"}},{label:"flow_to_stock",kind:i,insertText:"flow_to_stock",range:e,documentation:{value:"This operator takes in input a Data Set which are interpreted as flows and calculates the change of the corresponding stock since the beginning of each time series by summing the relevant flows. In other words, the operator perform the cumulative sum from the first Data Point of each time series to each other following Data Point of the same time series. \n\n **Syntax** \n\n flow_to_stock( op ) "}},{label:"stock_to_flow",kind:i,insertText:"stock_to_flow",range:e,documentation:{value:"This operator takes in input a Data Set of time series which is interpreted as stock data and, for each time series, calculates the corresponding flow data by subtracting from the measure values of each regular period the corresponding measure values of the previous one. \n\n **Syntax** \n\n stock_to_flow( op ) "}},{label:"timeshift",kind:i,insertText:"timeshift",range:e,documentation:{value:"This operator takes in input a Data Set of time series and, for each time series of the Data Set, shifts the reference time Identifier of a number of periods (of the time series) equal to the shift_number parameter. If shift_number is negative, the shift is in the past, otherwise in the future \n\n **Syntax** \n\n timeshift( op , shiftNumber) "}},{label:"measures",kind:o,insertText:"measures",range:e},{label:"no_measures",kind:o,insertText:"no_measures",range:e},{label:"condition",kind:o,insertText:"condition",range:e},{label:"boolean",kind:o,insertText:"boolean",range:e},{label:"date",kind:o,insertText:"date",range:e},{label:"time_period",kind:o,insertText:"time_period",range:e},{label:"number",kind:o,insertText:"number",range:e},{label:"string",kind:o,insertText:"string",range:e},{label:"integer",kind:o,insertText:"integer",range:e},{label:"float",kind:o,insertText:"float",range:e},{label:"list",kind:o,insertText:"list",range:e},{label:"record",kind:o,insertText:"record",range:e},{label:"restrict",kind:o,insertText:"restrict",range:e},{label:"maxLength",kind:o,insertText:"maxLength",range:e},{label:"regexp",kind:o,insertText:"regexp",range:e},{label:"is",kind:o,insertText:"is",range:e},{label:"when",kind:o,insertText:"when",range:e},{label:"from",kind:o,insertText:"from",range:e},{label:"aggregates",kind:o,insertText:"aggregates",range:e},{label:"points",kind:o,insertText:"points",range:e},{label:"point",kind:o,insertText:"point",range:e},{label:"total",kind:o,insertText:"total",range:e},{label:"partial",kind:o,insertText:"partial",range:e},{label:"always",kind:o,insertText:"always",range:e},{label:"inner_join",kind:i,insertText:"inner_join",range:e,documentation:{value:"Join operator, a relational join of the input operands is performed, according to SQL inner"}},{label:"left_join",kind:i,insertText:"left_join",range:e,documentation:{value:"Join operator, this is the left-most Data Set (left-outer sql)"}},{label:"cross_join",kind:i,insertText:"cross_join",range:e,documentation:{value:"Join operator, Cartesian product"}},{label:"full_join",kind:i,insertText:"full_join",range:e,documentation:{value:"Join operator, full outer (sql)"}},{label:"maps_from",kind:o,insertText:"maps_from",range:e},{label:"maps_to",kind:o,insertText:"maps_to",range:e},{label:"map_to",kind:o,insertText:"map_to",range:e},{label:"map_from",kind:o,insertText:"map_from",range:e},{label:"returns",kind:o,insertText:"returns",range:e},{label:"pivot",kind:o,insertText:"pivot",range:e,documentation:{value:"The operator transposes several Data Points of the operand Data Set into a single Data Point of the resulting Data Set. \n\n **Syntax** \n\n op [ pivot identifier , measure ] "}},{label:"customPivot",kind:o,insertText:"customPivot",range:e},{label:"unpivot",kind:o,insertText:"unpivot",range:e,documentation:{value:"The unpivot operator transposes a single Data Point of the operand Data Set into several Data Points of the result Data set. \n\n **Syntax** \n\n op [ unpivot identifier , measure ] "}},{label:"sub",kind:o,insertText:"sub",range:e,documentation:{value:"The operator returns a Data Set in a subspace of the one of the input Dataset. \n\n **Syntax** \n\n op [ sub identifier = value { , identifier = value }* ] "}},{label:"apply",kind:o,insertText:"apply",range:e},{label:"conditioned",kind:o,insertText:"conditioned",range:e},{label:"period_indicator",kind:i,insertText:"period_indicator ",range:e,documentation:{value:"period_indicator returns the period indicator of a time_period value. The period indicator is the part of the time_period value which denotes the duration of the time period (e.g. day, week, month \u2026). \n\n **Syntax** \n\n period_indicator(op) "}},{label:"single",kind:o,insertText:"single",range:e},{label:"duration",kind:o,insertText:"duration",range:e},{label:"time_agg",kind:i,insertText:"time_agg",range:e,documentation:{value:"The operator converts a time, date or time_period value from a smaller to a larger duration. \n\n **Syntax** \n\n time_agg(periodIndTo { ,periodIndFrom } { ,op } { ,first | last }) "}},{label:"unit",kind:o,insertText:"unit",range:e},{label:"value",kind:o,insertText:"value",range:e},{label:"valuedomains",kind:o,insertText:"valuedomains",range:e},{label:"variables",kind:o,insertText:"variables",range:e},{label:"input",kind:o,insertText:"input",range:e},{label:"output",kind:o,insertText:"output",range:e},{label:"cast",kind:i,insertText:"cast",range:e,documentation:{value:"This operator converts the scalar type of op to the scalar type specified by scalarType. It returns a copy of op converted to the specified scalarType. \n\n **Syntax** \n\n cast ( op , scalarType { , mask} ) "}},{label:"rule_priority",kind:o,insertText:"rule_priority",range:e},{label:"dataset_priority",kind:o,insertText:"dataset_priority",range:e},{label:"default",kind:o,insertText:"default",range:e},{label:"check_datapoint",kind:i,insertText:"check_datapoint",range:e,documentation:{value:"\n\n*Syntax*\n\n check_datapoint( op , dpr { components listComp } { output }) "}},{label:"check_hierarchy ",kind:i,insertText:"check_hierarchy ",range:e,documentation:{value:"The check_hierarchy operator applies the Rules of the Ruleset hr to check the Code Items Relations between the Code Items present in op \n\n **Syntax** \n\n check_hierarchy ( op , hr { condition condComp { , condComp }* } { rule ruleComp } { mode } { input } { output } ) 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else elseOperand "}},{label:"using",kind:o,insertText:"using",range:e},{label:"with",kind:o,insertText:"with",range:e},{label:"current_date",kind:i,insertText:"current_date()",range:e,documentation:{value:"The operator current_date returns the current time as a date type. \n\n **Syntax** \n\n current_date() "}},{label:"on",kind:o,insertText:"on",range:e},{label:"drop",kind:o,insertText:"drop",range:e,documentation:{value:"The operator takes as input a Data Set (op) and some Component names of such a Data Set (comp). \n\n **Syntax** \n\n op [drop comp { , comp }* ] "}},{label:"keep",kind:o,insertText:"keep",range:e,documentation:{value:"The operator takes as input a Data Set (op) and some Component names of such a Data Set (comp). \n\n **Syntax** \n\n op [ keep comp {, comp }* ] "}},{label:"calc",kind:o,insertText:"calc",range:e,documentation:{value:"The operator calculates new Identifier, Measure or Attribute Components on the basis of sub-expressions at Component level. ***\n\n*Example*\n\n DS_1 [ calc Me_3 := Me_1 + Me_2] "}},{label:"attrcalc",kind:o,insertText:"attrcalc",range:e},{label:"rename",kind:o,insertText:"rename",range:e,documentation:{value:"The operator assigns new names to one or more Components (Identifier, Measure or Attribute Components). \n\n **Syntax** \n\n op [ rename comp_from to comp_to { , comp_from to comp_to}* ] "}},{label:"as",kind:o,insertText:"as",range:e},{label:"and",kind:o,insertText:"and",range:e,documentation:{value:"The and operator returns TRUE if both operands are TRUE, otherwise FALSE. The two operands must be of boolean type. \n\n **Syntax** \n\n op1 and op2 "}},{label:"or",kind:o,insertText:"or",range:e,documentation:{value:"The or operator returns TRUE if at least one of the operands is TRUE, otherwise FALSE. The two operands must be of boolean type. \n\n **Syntax** \n\n op1 or op2 "}},{label:"xor",kind:o,insertText:"xor",range:e,documentation:{value:"The xor operator returns TRUE if only one of the operand is TRUE (but not both), FALSE otherwise. The two operands must be of boolean type. \n\n **Syntax** \n\n op1 xor op2 "}},{label:"not",kind:o,insertText:"not",range:e,documentation:{value:"The not operator returns TRUE if op is FALSE, otherwise TRUE. The input operand must be of boolean type. \n\n **Syntax** \n\n not op2 "}},{label:"between",kind:i,insertText:"between",range:e,documentation:{value:"The operator returns TRUE if op is greater than or equal to from and lower than or equal to to. \n\n **Syntax** \n\n between (op, from, to) "}},{label:"levenshtein",kind:i,insertText:"levenshtein",range:e,documentation:{value:"The operator returns the levenshtein distance between the two inputed strings. \n\n **Syntax** \n\n levenshtein (left, right) "}},{label:"random",kind:i,insertText:"random",range:e,documentation:{value:"The operator returns a random decimal number >= 0 and <1. \n\n **Syntax** \n\n random()"}},{label:"in",kind:o,insertText:"in",range:e,documentation:{value:"The in operator returns TRUE if op belongs to the collection, FALSE otherwise. \n\n **Syntax** \n\n op in collection "}},{label:"not_in",kind:o,insertText:"not_in",range:e,documentation:{value:"The not_in operator returns FALSE if op belongs to the collection, TRUE otherwise. \n\n **Syntax** \n\n op not_in collection "}},{label:"isnull",kind:i,insertText:"isnull",range:e,documentation:{value:"The operator returns TRUE if the value of the operand is NULL, FALSE otherwise. \n\n **Syntax** \n\n isnull(op) "}},{label:"ex",kind:o,insertText:"ex",range:e},{label:"union",kind:i,insertText:"union",range:e,documentation:{value:"The union operator implements the union of functions (i.e., Data Sets). \n\n **Syntax** \n\n union ( ds { , ds }*) "}},{label:"diff",kind:o,insertText:"diff",range:e},{label:"symdiff",kind:i,insertText:"symdiff",range:e,documentation:{value:"The operator implements the symmetric set difference between functions (i.e. Data Sets), interpreting the Data Points of the input Data Sets as the elements in the operand Sets. \n\n **Syntax** \n\n symdiff(ds1, ds) "}},{label:"intersect",kind:i,insertText:"intersect",range:e,documentation:{value:"The intersect operator implements the intersection of functions (i.e., Data Sets). \n\n **Syntax** \n\n intersect ( ds { , ds }*) "}},{label:"keys",kind:o,insertText:"KEYS",range:e},{label:"check",kind:i,insertText:"check",range:e,documentation:{value:"*Syntax*\n\n check ( op { errorcode errorcode } { errorlevel errorlevel } { imbalance imbalance } { output } ) \n\noutput ::= invalid | all "}},{label:"exists_in",kind:i,insertText:"exists_in",range:e,documentation:{value:"The operator returns TRUE if the value of the operand is NULL, FALSE otherwise. \n\n **Syntax** \n\n exists_in ( op1, op2 { , retain } ) \n\n *retain* ::= true | false | all"}},{label:"to",kind:o,insertText:"to",range:e},{label:"return",kind:o,insertText:"return",range:e},{label:"imbalance",kind:o,insertText:"imbalance",range:e,documentation:{value:"Imbalance is a numeric mono-measure Data Set with the same Identifiers of op. If not specified then imbalance is NULL"}},{label:"errorcode",kind:o,insertText:"errorcode",range:e},{label:"all",kind:o,insertText:"all",range:e},{label:"aggr",kind:o,insertText:"aggr",range:e,documentation:{value:"The operator aggr calculates aggregations of dependent Components (Measures or Attributes) on the basis of sub-expressions at Component level. \n\n **Syntax** \n\n op [ aggr aggrClause { groupingClause } ] "}},{label:"errorlevel",kind:o,insertText:"errorlevel",range:e},{label:"order",kind:o,insertText:"order",range:e},{label:"by",kind:o,insertText:"by",range:e},{label:"rank",kind:i,insertText:"rank",range:e,documentation:{value:"Rank (order number) of a Data Point in an ordered set of Data Points. \n\n **Syntax** \n\n rank( firstOperand { , additionalOperand }* { groupingClause } \n\n rank( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"asc",kind:o,insertText:"asc",range:e},{label:"desc",kind:o,insertText:"desc",range:e},{label:"min",kind:i,insertText:"min",range:e,documentation:{value:"Minimum value of a set of values . \n\n **Syntax** \n\n min( firstOperand { , additionalOperand }* { groupingClause } \n\n min( firstOperand { , additionalOperand }* over ( analyticClause ) "}},{label:"max",kind:i,insertText:"max",range:e,documentation:{value:"Maximum value of a set of values . \n\n **Syntax** \n\n max( firstOperand { , additionalOperand }* { groupingClause } \n\n max( firstOperand { , additionalOperand }* over ( analyticClause ) "}},{label:"first",kind:o,insertText:"first",range:e},{label:"last",kind:o,insertText:"last",range:e},{label:"indexof",kind:o,insertText:"indexof",range:e},{label:"abs",kind:i,insertText:"abs",range:e,documentation:{value:"The operator abs calculates the absolute value of a number. \n\n For example:\n\n abs(-3.141) gives 3.141 \n\n **Syntax** \n\n abs(op) "}},{label:"key",kind:o,insertText:"key",range:e},{label:"ln",kind:i,insertText:"ln",range:e,documentation:{value:"The operator ln calculates the natural logarithm of a number. \n\n **Syntax** \n\n ln(op) "}},{label:"log",kind:i,insertText:"log",range:e,documentation:{value:"The operator log calculates the logarithm of num base op. \n\n **Syntax** \n\n log(op , num) "}},{label:"trunc",kind:i,insertText:"trunc",range:e,documentation:{value:"The operator trunc truncates the operand to a number of positions at the right of the decimal point equal to the numDigit parameter. \n\n **Syntax** \n\n trunc ( op , numDigit ) "}},{label:"round",kind:i,insertText:"round",range:e,documentation:{value:"The operator round rounds the operand to a number of positions at the right of the decimal point equal to the numDigit parameter. \n\n **Syntax** \n\n round( op , numDigit ) "}},{label:"power",kind:i,insertText:"power",range:e,documentation:{value:"The operator power raises a number (the base) to another one (the exponent). \n\n **Syntax** \n\n power(base , exponent) "}},{label:"mod",kind:i,insertText:"mod",range:e,documentation:{value:"The operator mod returns the remainder of op1 divided by op2. It returns op1 if divisor op2 is 0. \n\n **Syntax** \n\n mod(op1, op2 ) "}},{label:"length",kind:i,insertText:"length",range:e,documentation:{value:"Returns the length of a string. \n\n **Syntax** \n\n len( op ) "}},{label:"trim",kind:i,insertText:"trim",range:e,documentation:{value:'Removes trailing and leading whitespace from a string. For example, trim("Hello ") gives "Hello". \n\n **Syntax** \n\n trim(operand) '}},{label:"upper",kind:i,insertText:"upper",range:e,documentation:{value:'Converts the character case of a string in upper case. For example, upper("Hello") gives "HELLO". \n\n **Syntax** \n\n upper(operand) '}},{label:"lower",kind:i,insertText:"lower",range:e,documentation:{value:'Converts the character case of a string in lower case. For example, lower("Hello") gives "hello". \n\n **Syntax** \n\n lower(operand) '}},{label:"substr",kind:i,insertText:"substr",range:e,documentation:{value:"The operator extracts a substring from op, which must be string type. The substring starts from the start character of the input string and has a number of characters equal to the length parameter. \n\n **Syntax** \n\n substr ( op, start, length ) "}},{label:"sum",kind:i,insertText:"sum",range:e,documentation:{value:"Sum of a set of numbers. \n\n **Syntax** \n\n sun( firstOperand { , additionalOperand }* { groupingClause } "}},{label:"avg",kind:i,insertText:"avg",range:e,documentation:{value:"Average value of a set of numbers. \n\n **Syntax** \n\n avg( firstOperand { , additionalOperand }* { groupingClause } \n\n avg( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"median",kind:i,insertText:"median",range:e,documentation:{value:"Median value of a set of values . \n\n **Syntax** \n\n median( firstOperand { , additionalOperand }* { groupingClause } \n\n median( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"count",kind:i,insertText:"count",range:e,documentation:{value:"Number of Data Points. \n\n **Syntax** \n\n count( firstOperand { , additionalOperand }* { groupingClause } \n\n count( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"identifier",kind:o,insertText:"identifier",range:e},{label:"measure",kind:o,insertText:"measure",range:e},{label:"attribute",kind:o,insertText:"attribute",range:e},{label:"filter",kind:o,insertText:"filter",range:e,documentation:{value:"The operator takes as input a Data Set (op) and a boolean Component expression (filterCondition) and filters the input Data Points according to the evaluation of the condition. \n\n **Syntax** \n\n op[ filter filterCondition ] "}},{label:"merge",kind:o,insertText:"merge",range:e},{label:"exp",kind:i,insertText:"exp",range:e,documentation:{value:"The operator exp returns e (base of the natural logarithm) raised to the op-th power. \n\n **Syntax** \n\n exp(op) "}},{label:"componentRole",kind:o,insertText:"componentRole",range:e},{label:"viral",kind:o,insertText:"viral",range:e},{label:"match_characters",kind:i,insertText:"match_characters",range:e,documentation:{value:"match_characters returns TRUE if op matches the regular expression regexp, FALSE otherwise. \n\n **Syntax** \n\n match_characters ( op , pattern ) "}},{label:"type",kind:o,insertText:"type",range:e},{label:"nvl",kind:i,insertText:"nvl",range:e,documentation:{value:"The operator nvl returns op2 when op1 is null, otherwise op1. \n\n **Syntax** \n\n nvl ( op1 , op2 ) "}},{label:"hierarchy",kind:i,insertText:"hierarchy",range:e,documentation:{value:"The hierarchy operator applies the rules of hr to op as specified in the parameters. \n\n **Syntax** \n\n hierarchy( op , hr { condition condComp { , condComp }* } { rule ruleComp } { mode } { input } { output } ) "}},{label:"invalid",kind:o,insertText:"invalid",range:e},{label:"valuedomain",kind:o,insertText:"valuedomain",range:e},{label:"variable",kind:o,insertText:"variable",range:e},{label:"data",kind:o,insertText:"data",range:e},{label:"structure",kind:o,insertText:"structure",range:e},{label:"dataset",kind:o,insertText:"dataset",range:e},{label:"operator",kind:o,insertText:"operator",range:e},{label:"define",kind:o,insertText:"define",range:e},{label:"datapoint",kind:o,insertText:"datapoint",range:e},{label:"hierarchical",kind:o,insertText:"hierarchical",range:e},{label:"ruleset",kind:o,insertText:"ruleset",range:e},{label:"rule",kind:o,insertText:"rule",range:e},{label:"end",kind:o,insertText:"end",range:e},{label:"alterDataset",kind:o,insertText:"alterDataset",range:e},{label:"ltrim",kind:i,insertText:"ltrim",range:e,documentation:{value:'Removes leading whitespace from a string. For example, trim(" Hello ") gives "Hello ". \n\n **Syntax** \n\n ltrim(operand) '}},{label:"rtrim",kind:i,insertText:"rtrim",range:e,documentation:{value:'Removes trailing whitespace from a string. For example, trim("Hello ") gives "Hello". \n\n **Syntax** \n\n rtrim(operand) '}},{label:"instr",kind:i,insertText:"instr",range:e,documentation:{value:"The operator returns the position in the input string of a specified string (pattern). \n\n **Syntax** \n\n instr ( op, pattern, start, occurrence ) "}},{label:"replace",kind:i,insertText:"replace",range:e,documentation:{value:"Replaces all the occurrences of a specified string-pattern (pattern1) with another one (pattern2). If pattern2 is omitted then all occurrences of pattern1 are removed \n\n **Syntax** \n\n replace (op , pattern1, pattern2 ) "}},{label:"ceil",kind:i,insertText:"ceil",range:e,documentation:{value:"The operator ceil returns the smallest integer greater than or equal to op. \n\n For example:\n\n ceil(3.14159) gives 4 \n\n **Syntax** \n\n ceil(op) "}},{label:"floor",kind:i,insertText:"floor",range:e,documentation:{value:"The operator floor returns the greatest integer which is smaller than or equal to op. \n\n For example:\n\n floor(3.14159) gives 3 \n\n **Syntax** \n\n floor(op) "}},{label:"sqrt",kind:i,insertText:"sqrt",range:e,documentation:{value:"The operator sqrt calculates the square root of a number. \n\n **Syntax** \n\n sqrt(op) "}},{label:"any",kind:o,insertText:"any",range:e},{label:"setdiff",kind:i,insertText:"setdiff",range:e,documentation:{value:"The operator implements the set difference of functions (i.e. Data Sets), interpreting the Data Points of the input Data Sets as the elements belonging to the operand sets, the minuend and the subtrahend, respectively. \n\n **Syntax** \n\n setdiff(ds1, ds2) "}},{label:"stddev_pop",kind:i,insertText:"stddev_pop",range:e,documentation:{value:"Population standard deviation of a set of numbers. \n\n **Syntax** \n\n stddev_pop( firstOperand { , additionalOperand }* { groupingClause } \n\n stddev_pop( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"stddev_samp",kind:i,insertText:"stddev_samp",range:e,documentation:{value:"Sample standard deviation of a set of numbers. \n\n **Syntax** \n\n stddev_samp( firstOperand { , additionalOperand }* { groupingClause } \n\n stddev_samp( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"var_pop",kind:i,insertText:"var_pop",range:e,documentation:{value:"Population variance of a set of numbers. \n\n **Syntax** \n\n var_pop( firstOperand { , additionalOperand }* { groupingClause } \n\n var_pop( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"var_samp",kind:i,insertText:"var_samp",range:e,documentation:{value:"Sample variance of a set of numbers. \n\n **Syntax** \n\n var_samp( firstOperand { , additionalOperand }* { groupingClause } \n\n var_samp( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"group",kind:o,insertText:"group",range:e},{label:"except",kind:o,insertText:"except",range:e},{label:"having",kind:o,insertText:"having",range:e},{label:"first_value",kind:i,insertText:"first_value",range:e,documentation:{value:"First value in an ordered set of values . \n\n **Syntax** \n\n first_value( firstOperand { , additionalOperand }* { groupingClause } \n\n first_value( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"last_value",kind:i,insertText:"last_value",range:e,documentation:{value:"Last value in an ordered set of values. \n\n **Syntax** \n\n last_value( firstOperand { , additionalOperand }* { groupingClause } \n\n last_value( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"lag",kind:i,insertText:"lag",range:e,documentation:{value:"In an ordered set of Data Points, it returns the value(s) taken from a Data Point at a given physical offset prior to the current Data Point. \n\n **Syntax** \n\n lag( firstOperand { , additionalOperand }* { groupingClause } \n\n lag( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"lead",kind:i,insertText:"lead",range:e,documentation:{value:"In an ordered set of Data Points, it returns the value(s) taken from a Data Point at a given physical offset beyond the current Data Point . \n\n **Syntax** \n\n lead( firstOperand { , additionalOperand }* { groupingClause } \n\n lead( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"ratio_to_report",kind:i,insertText:"ratio_to_report",range:e,documentation:{value:"In an ordered set of Data Points, it returns the value(s) taken from a Data Point at a given physical offset beyond the current Data Point . \n\n **Syntax** \n\n ratio_to_report( firstOperand { , additionalOperand }* { groupingClause } \n\n ratio_to_report( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"over",kind:o,insertText:"over",range:e},{label:"preceding",kind:o,insertText:"preceding",range:e},{label:"following",kind:o,insertText:"following",range:e},{label:"unbounded",kind:o,insertText:"unbounded",range:e},{label:"partition",kind:o,insertText:"partition",range:e},{label:"rows",kind:o,insertText:"rows",range:e},{label:"range",kind:o,insertText:"range",range:e},{label:"current",kind:o,insertText:"current",range:e},{label:"valid",kind:o,insertText:"valid",range:e},{label:"fill_time_series",kind:i,insertText:"fill_time_series",range:e,documentation:{value:"The operator fills the possibly missing Data Points of all the time series belonging to the operand op within the time limits automatically determined by applying the limit_method. \n\n **Syntax** \n\n 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else elseOperand "}},{label:"using",kind:o,insertText:"using",range:e},{label:"with",kind:o,insertText:"with",range:e},{label:"current_date",kind:i,insertText:"current_date()",range:e,documentation:{value:"The operator current_date returns the current time as a date type. \n\n **Syntax** \n\n current_date() "}},{label:"on",kind:o,insertText:"on",range:e},{label:"drop",kind:o,insertText:"drop",range:e,documentation:{value:"The operator takes as input a Data Set (op) and some Component names of such a Data Set (comp). \n\n **Syntax** \n\n op [drop comp { , comp }* ] "}},{label:"keep",kind:o,insertText:"keep",range:e,documentation:{value:"The operator takes as input a Data Set (op) and some Component names of such a Data Set (comp). \n\n **Syntax** \n\n op [ keep comp {, comp }* ] "}},{label:"calc",kind:o,insertText:"calc",range:e,documentation:{value:"The operator calculates new Identifier, Measure or Attribute Components on the basis of sub-expressions at Component level. ***\n\n*Example*\n\n DS_1 [ calc Me_3 := Me_1 + Me_2] "}},{label:"attrcalc",kind:o,insertText:"attrcalc",range:e},{label:"rename",kind:o,insertText:"rename",range:e,documentation:{value:"The operator assigns new names to one or more Components (Identifier, Measure or Attribute Components). \n\n **Syntax** \n\n op [ rename comp_from to comp_to { , comp_from to comp_to}* ] "}},{label:"as",kind:o,insertText:"as",range:e},{label:"and",kind:o,insertText:"and",range:e,documentation:{value:"The and operator returns TRUE if both operands are TRUE, otherwise FALSE. The two operands must be of boolean type. \n\n **Syntax** \n\n op1 and op2 "}},{label:"or",kind:o,insertText:"or",range:e,documentation:{value:"The or operator returns TRUE if at least one of the operands is TRUE, otherwise FALSE. The two operands must be of boolean type. \n\n **Syntax** \n\n op1 or op2 "}},{label:"xor",kind:o,insertText:"xor",range:e,documentation:{value:"The xor operator returns TRUE if only one of the operand is TRUE (but not both), FALSE otherwise. The two operands must be of boolean type. \n\n **Syntax** \n\n op1 xor op2 "}},{label:"not",kind:o,insertText:"not",range:e,documentation:{value:"The not operator returns TRUE if op is FALSE, otherwise TRUE. The input operand must be of boolean type. \n\n **Syntax** \n\n not op2 "}},{label:"between",kind:i,insertText:"between",range:e,documentation:{value:"The operator returns TRUE if op is greater than or equal to from and lower than or equal to to. \n\n **Syntax** \n\n between (op, from, to) "}},{label:"levenshtein",kind:i,insertText:"levenshtein",range:e,documentation:{value:"The operator returns the levenshtein distance between the two inputed strings. \n\n **Syntax** \n\n levenshtein (left, right) "}},{label:"random",kind:i,insertText:"random",range:e,documentation:{value:"The operator returns a random decimal number >= 0 and <1. \n\n **Syntax** \n\n random()"}},{label:"in",kind:o,insertText:"in",range:e,documentation:{value:"The in operator returns TRUE if op belongs to the collection, FALSE otherwise. \n\n **Syntax** \n\n op in collection "}},{label:"not_in",kind:o,insertText:"not_in",range:e,documentation:{value:"The not_in operator returns FALSE if op belongs to the collection, TRUE otherwise. \n\n **Syntax** \n\n op not_in collection "}},{label:"isnull",kind:i,insertText:"isnull",range:e,documentation:{value:"The operator returns TRUE if the value of the operand is NULL, FALSE otherwise. \n\n **Syntax** \n\n isnull(op) "}},{label:"ex",kind:o,insertText:"ex",range:e},{label:"union",kind:i,insertText:"union",range:e,documentation:{value:"The union operator implements the union of functions (i.e., Data Sets). \n\n **Syntax** \n\n union ( ds { , ds }*) "}},{label:"diff",kind:o,insertText:"diff",range:e},{label:"symdiff",kind:i,insertText:"symdiff",range:e,documentation:{value:"The operator implements the symmetric set difference between functions (i.e. Data Sets), interpreting the Data Points of the input Data Sets as the elements in the operand Sets. \n\n **Syntax** \n\n symdiff(ds1, ds) "}},{label:"intersect",kind:i,insertText:"intersect",range:e,documentation:{value:"The intersect operator implements the intersection of functions (i.e., Data Sets). \n\n **Syntax** \n\n intersect ( ds { , ds }*) "}},{label:"keys",kind:o,insertText:"KEYS",range:e},{label:"check",kind:i,insertText:"check",range:e,documentation:{value:"*Syntax*\n\n check ( op { errorcode errorcode } { errorlevel errorlevel } { imbalance imbalance } { output } ) \n\noutput ::= invalid | all "}},{label:"exists_in",kind:i,insertText:"exists_in",range:e,documentation:{value:"The operator returns TRUE if the value of the operand is NULL, FALSE otherwise. \n\n **Syntax** \n\n exists_in ( op1, op2 { , retain } ) \n\n *retain* ::= true | false | all"}},{label:"to",kind:o,insertText:"to",range:e},{label:"return",kind:o,insertText:"return",range:e},{label:"imbalance",kind:o,insertText:"imbalance",range:e,documentation:{value:"Imbalance is a numeric mono-measure Data Set with the same Identifiers of op. If not specified then imbalance is NULL"}},{label:"errorcode",kind:o,insertText:"errorcode",range:e},{label:"all",kind:o,insertText:"all",range:e},{label:"aggr",kind:o,insertText:"aggr",range:e,documentation:{value:"The operator aggr calculates aggregations of dependent Components (Measures or Attributes) on the basis of sub-expressions at Component level. \n\n **Syntax** \n\n op [ aggr aggrClause { groupingClause } ] "}},{label:"errorlevel",kind:o,insertText:"errorlevel",range:e},{label:"order",kind:o,insertText:"order",range:e},{label:"by",kind:o,insertText:"by",range:e},{label:"rank",kind:i,insertText:"rank",range:e,documentation:{value:"Rank (order number) of a Data Point in an ordered set of Data Points. \n\n **Syntax** \n\n rank( firstOperand { , additionalOperand }* { groupingClause } \n\n rank( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"asc",kind:o,insertText:"asc",range:e},{label:"desc",kind:o,insertText:"desc",range:e},{label:"min",kind:i,insertText:"min",range:e,documentation:{value:"Minimum value of a set of values . \n\n **Syntax** \n\n min( firstOperand { , additionalOperand }* { groupingClause } \n\n min( firstOperand { , additionalOperand }* over ( analyticClause ) "}},{label:"max",kind:i,insertText:"max",range:e,documentation:{value:"Maximum value of a set of values . \n\n **Syntax** \n\n max( firstOperand { , additionalOperand }* { groupingClause } \n\n max( firstOperand { , additionalOperand }* over ( analyticClause ) "}},{label:"first",kind:o,insertText:"first",range:e},{label:"last",kind:o,insertText:"last",range:e},{label:"indexof",kind:o,insertText:"indexof",range:e},{label:"abs",kind:i,insertText:"abs",range:e,documentation:{value:"The operator abs calculates the absolute value of a number. \n\n For example:\n\n abs(-3.141) gives 3.141 \n\n **Syntax** \n\n abs(op) "}},{label:"key",kind:o,insertText:"key",range:e},{label:"ln",kind:i,insertText:"ln",range:e,documentation:{value:"The operator ln calculates the natural logarithm of a number. \n\n **Syntax** \n\n ln(op) "}},{label:"log",kind:i,insertText:"log",range:e,documentation:{value:"The operator log calculates the logarithm of num base op. \n\n **Syntax** \n\n log(op , num) "}},{label:"trunc",kind:i,insertText:"trunc",range:e,documentation:{value:"The operator trunc truncates the operand to a number of positions at the right of the decimal point equal to the numDigit parameter. \n\n **Syntax** \n\n trunc ( op , numDigit ) "}},{label:"round",kind:i,insertText:"round",range:e,documentation:{value:"The operator round rounds the operand to a number of positions at the right of the decimal point equal to the numDigit parameter. \n\n **Syntax** \n\n round( op , numDigit ) "}},{label:"power",kind:i,insertText:"power",range:e,documentation:{value:"The operator power raises a number (the base) to another one (the exponent). \n\n **Syntax** \n\n power(base , exponent) "}},{label:"mod",kind:i,insertText:"mod",range:e,documentation:{value:"The operator mod returns the remainder of op1 divided by op2. It returns op1 if divisor op2 is 0. \n\n **Syntax** \n\n mod(op1, op2 ) "}},{label:"length",kind:i,insertText:"length",range:e,documentation:{value:"Returns the length of a string. \n\n **Syntax** \n\n len( op ) "}},{label:"trim",kind:i,insertText:"trim",range:e,documentation:{value:'Removes trailing and leading whitespace from a string. For example, trim("Hello ") gives "Hello". \n\n **Syntax** \n\n trim(operand) '}},{label:"upper",kind:i,insertText:"upper",range:e,documentation:{value:'Converts the character case of a string in upper case. For example, upper("Hello") gives "HELLO". \n\n **Syntax** \n\n upper(operand) '}},{label:"lower",kind:i,insertText:"lower",range:e,documentation:{value:'Converts the character case of a string in lower case. For example, lower("Hello") gives "hello". \n\n **Syntax** \n\n lower(operand) '}},{label:"substr",kind:i,insertText:"substr",range:e,documentation:{value:"The operator extracts a substring from op, which must be string type. The substring starts from the start character of the input string and has a number of characters equal to the length parameter. \n\n **Syntax** \n\n substr ( op, start, length ) "}},{label:"sum",kind:i,insertText:"sum",range:e,documentation:{value:"Sum of a set of numbers. \n\n **Syntax** \n\n sun( firstOperand { , additionalOperand }* { groupingClause } "}},{label:"avg",kind:i,insertText:"avg",range:e,documentation:{value:"Average value of a set of numbers. \n\n **Syntax** \n\n avg( firstOperand { , additionalOperand }* { groupingClause } \n\n avg( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"median",kind:i,insertText:"median",range:e,documentation:{value:"Median value of a set of values . \n\n **Syntax** \n\n median( firstOperand { , additionalOperand }* { groupingClause } \n\n median( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"count",kind:i,insertText:"count",range:e,documentation:{value:"Number of Data Points. \n\n **Syntax** \n\n count( firstOperand { , additionalOperand }* { groupingClause } \n\n count( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"identifier",kind:o,insertText:"identifier",range:e},{label:"measure",kind:o,insertText:"measure",range:e},{label:"attribute",kind:o,insertText:"attribute",range:e},{label:"filter",kind:o,insertText:"filter",range:e,documentation:{value:"The operator takes as input a Data Set (op) and a boolean Component expression (filterCondition) and filters the input Data Points according to the evaluation of the condition. \n\n **Syntax** \n\n op[ filter filterCondition ] "}},{label:"merge",kind:o,insertText:"merge",range:e},{label:"exp",kind:i,insertText:"exp",range:e,documentation:{value:"The operator exp returns e (base of the natural logarithm) raised to the op-th power. \n\n **Syntax** \n\n exp(op) "}},{label:"componentRole",kind:o,insertText:"componentRole",range:e},{label:"viral",kind:o,insertText:"viral",range:e},{label:"match_characters",kind:i,insertText:"match_characters",range:e,documentation:{value:"match_characters returns TRUE if op matches the regular expression regexp, FALSE otherwise. \n\n **Syntax** \n\n match_characters ( op , pattern ) "}},{label:"type",kind:o,insertText:"type",range:e},{label:"nvl",kind:i,insertText:"nvl",range:e,documentation:{value:"The operator nvl returns op2 when op1 is null, otherwise op1. \n\n **Syntax** \n\n nvl ( op1 , op2 ) "}},{label:"hierarchy",kind:i,insertText:"hierarchy",range:e,documentation:{value:"The hierarchy operator applies the rules of hr to op as specified in the parameters. \n\n **Syntax** \n\n hierarchy( op , hr { condition condComp { , condComp }* } { rule ruleComp } { mode } { input } { output } ) "}},{label:"invalid",kind:o,insertText:"invalid",range:e},{label:"valuedomain",kind:o,insertText:"valuedomain",range:e},{label:"variable",kind:o,insertText:"variable",range:e},{label:"data",kind:o,insertText:"data",range:e},{label:"structure",kind:o,insertText:"structure",range:e},{label:"dataset",kind:o,insertText:"dataset",range:e},{label:"operator",kind:o,insertText:"operator",range:e},{label:"define",kind:o,insertText:"define",range:e},{label:"datapoint",kind:o,insertText:"datapoint",range:e},{label:"hierarchical",kind:o,insertText:"hierarchical",range:e},{label:"ruleset",kind:o,insertText:"ruleset",range:e},{label:"rule",kind:o,insertText:"rule",range:e},{label:"end",kind:o,insertText:"end",range:e},{label:"alterDataset",kind:o,insertText:"alterDataset",range:e},{label:"ltrim",kind:i,insertText:"ltrim",range:e,documentation:{value:'Removes leading whitespace from a string. For example, trim(" Hello ") gives "Hello ". \n\n **Syntax** \n\n ltrim(operand) '}},{label:"rtrim",kind:i,insertText:"rtrim",range:e,documentation:{value:'Removes trailing whitespace from a string. For example, trim("Hello ") gives "Hello". \n\n **Syntax** \n\n rtrim(operand) '}},{label:"instr",kind:i,insertText:"instr",range:e,documentation:{value:"The operator returns the position in the input string of a specified string (pattern). \n\n **Syntax** \n\n instr ( op, pattern, start, occurrence ) "}},{label:"replace",kind:i,insertText:"replace",range:e,documentation:{value:"Replaces all the occurrences of a specified string-pattern (pattern1) with another one (pattern2). If pattern2 is omitted then all occurrences of pattern1 are removed \n\n **Syntax** \n\n replace (op , pattern1, pattern2 ) "}},{label:"ceil",kind:i,insertText:"ceil",range:e,documentation:{value:"The operator ceil returns the smallest integer greater than or equal to op. \n\n For example:\n\n ceil(3.14159) gives 4 \n\n **Syntax** \n\n ceil(op) "}},{label:"floor",kind:i,insertText:"floor",range:e,documentation:{value:"The operator floor returns the greatest integer which is smaller than or equal to op. \n\n For example:\n\n floor(3.14159) gives 3 \n\n **Syntax** \n\n floor(op) "}},{label:"sqrt",kind:i,insertText:"sqrt",range:e,documentation:{value:"The operator sqrt calculates the square root of a number. \n\n **Syntax** \n\n sqrt(op) "}},{label:"any",kind:o,insertText:"any",range:e},{label:"setdiff",kind:i,insertText:"setdiff",range:e,documentation:{value:"The operator implements the set difference of functions (i.e. Data Sets), interpreting the Data Points of the input Data Sets as the elements belonging to the operand sets, the minuend and the subtrahend, respectively. \n\n **Syntax** \n\n setdiff(ds1, ds2) "}},{label:"stddev_pop",kind:i,insertText:"stddev_pop",range:e,documentation:{value:"Population standard deviation of a set of numbers. \n\n **Syntax** \n\n stddev_pop( firstOperand { , additionalOperand }* { groupingClause } \n\n stddev_pop( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"stddev_samp",kind:i,insertText:"stddev_samp",range:e,documentation:{value:"Sample standard deviation of a set of numbers. \n\n **Syntax** \n\n stddev_samp( firstOperand { , additionalOperand }* { groupingClause } \n\n stddev_samp( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"var_pop",kind:i,insertText:"var_pop",range:e,documentation:{value:"Population variance of a set of numbers. \n\n **Syntax** \n\n var_pop( firstOperand { , additionalOperand }* { groupingClause } \n\n var_pop( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"var_samp",kind:i,insertText:"var_samp",range:e,documentation:{value:"Sample variance of a set of numbers. \n\n **Syntax** \n\n var_samp( firstOperand { , additionalOperand }* { groupingClause } \n\n var_samp( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"group",kind:o,insertText:"group",range:e},{label:"except",kind:o,insertText:"except",range:e},{label:"having",kind:o,insertText:"having",range:e},{label:"first_value",kind:i,insertText:"first_value",range:e,documentation:{value:"First value in an ordered set of values . \n\n **Syntax** \n\n first_value( firstOperand { , additionalOperand }* { groupingClause } \n\n first_value( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"last_value",kind:i,insertText:"last_value",range:e,documentation:{value:"Last value in an ordered set of values. \n\n **Syntax** \n\n last_value( firstOperand { , additionalOperand }* { groupingClause } \n\n last_value( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"lag",kind:i,insertText:"lag",range:e,documentation:{value:"In an ordered set of Data Points, it returns the value(s) taken from a Data Point at a given physical offset prior to the current Data Point. \n\n **Syntax** \n\n lag( firstOperand { , additionalOperand }* { groupingClause } \n\n lag( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"lead",kind:i,insertText:"lead",range:e,documentation:{value:"In an ordered set of Data Points, it returns the value(s) taken from a Data Point at a given physical offset beyond the current Data Point . \n\n **Syntax** \n\n lead( firstOperand { , additionalOperand }* { groupingClause } \n\n lead( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"ratio_to_report",kind:i,insertText:"ratio_to_report",range:e,documentation:{value:"In an ordered set of Data Points, it returns the value(s) taken from a Data Point at a given physical offset beyond the current Data Point . \n\n **Syntax** \n\n ratio_to_report( firstOperand { , additionalOperand }* { groupingClause } \n\n ratio_to_report( firstOperand { , additionalOperand }* over ( analyticClause )) "}},{label:"over",kind:o,insertText:"over",range:e},{label:"preceding",kind:o,insertText:"preceding",range:e},{label:"following",kind:o,insertText:"following",range:e},{label:"unbounded",kind:o,insertText:"unbounded",range:e},{label:"partition",kind:o,insertText:"partition",range:e},{label:"rows",kind:o,insertText:"rows",range:e},{label:"range",kind:o,insertText:"range",range:e},{label:"current",kind:o,insertText:"current",range:e},{label:"valid",kind:o,insertText:"valid",range:e},{label:"fill_time_series",kind:i,insertText:"fill_time_series",range:e,documentation:{value:"The operator fills the possibly missing Data Points of all the time series belonging to the operand op within the time limits automatically determined by applying the limit_method. \n\n **Syntax** \n\n 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