From 324825e5d0fd7cdc6eb7688cb8bac5fefa9b8816 Mon Sep 17 00:00:00 2001 From: Jan-Niklas Tille Date: Tue, 16 Apr 2024 13:51:40 +0200 Subject: [PATCH] add intro for OTLP and Collector, basic spell checks --- .../content/intro/goals_of_otel/_index.md | 45 +++++ .../content/intro/how_we_got_here/index.md | 38 ++-- .../images/otel_collector_overview.drawio.png | Bin 0 -> 2436905 bytes .../images/otel_collector_overview.drawio.xml | 163 ++++++++++++++++++ .../content/intro/overview_of_otel/index.md | 84 ++++++--- .../problems_with_the_status_quo/index.md | 20 +-- 6 files changed, 298 insertions(+), 52 deletions(-) create mode 100644 tutorial/content/intro/goals_of_otel/_index.md create mode 100644 tutorial/content/intro/overview_of_otel/images/otel_collector_overview.drawio.png create mode 100644 tutorial/content/intro/overview_of_otel/images/otel_collector_overview.drawio.xml diff --git a/tutorial/content/intro/goals_of_otel/_index.md b/tutorial/content/intro/goals_of_otel/_index.md new file mode 100644 index 0000000..2104a7e --- /dev/null +++ b/tutorial/content/intro/goals_of_otel/_index.md @@ -0,0 +1,45 @@ +--- +title: "Why is OpenTelemetry promising?" +linktitle: "Goals of OpenTelemetry" +draft: false +weight: 30 +--- + + + + +At the time of writing, OpenTelmetry is the [second fastest-growing project](https://www.cncf.io/reports/cncf-annual-report-2023/#projects) within the CNCF. +OpenTelemetry receives so much attention because it promises to be a fundamental shift in the way we produce telemetry. +It's important to remember that observability is a fairly young discipline. +In the past, the rate of innovation and conflicts of interest prevented us from defining widely adopted standards for telemetry. +However, the timing and momentum of OpenTelemetry appear to have a realistic chance of pushing for standardization of common aspects of telemetry. + +#### Instrument once, use everywhere +A key promise of OpenTelemetry is that you *instrument code once and never again* and the ability *to use that instrumentation everywhere*. +OpenTelemetry recognizes that, should its efforts be successful, it will be a core dependency for many software projects. +Therefore, it follows strict processes to provide [*long-term stability guarantees*](https://opentelemetry.io/docs/specs/otel/versioning-and-stability/). +Once a signal is declared stable, the promise is that clients will never experience a breaking API change. + +#### Separate telemetry generation from analysis +Another core idea of OpenTelemetry is *separate the mechanisms that produce telemetry from the systems that analyzes it*. +Open and vendor-agnostic instrumentation marks a fundamental *change in the observability business*. +Instead of pouring resources into building proprietary instrumentation and keeping it up to date, vendors must differentiate themselves through feature-rich analysis platforms with great usability. +OpenTelemetry *fosters competition*, because users no longer stuck with the observability solution they chose during development. +After switching to OpenTelemetry, you can move platforms without having to re-instrument your entire system. + +#### Make software observable by default +With OpenTelemetry, open-source developers are able to add *native instrumentation to their projects without introducing vendor-specific code* that burdens their users. +The idea is to *make observability a first-class citizen during development*. +By having software ship with built-in instrumentation, we no longer need elaborate mechanisms to capture and integrate it after the fact. + +#### Improve how we use telemetry +Last (and definitely not least), OpenTelemetry tries to change how we think about and use telemetry. +Instead of having three separate silos for logs, metrics, and traces, OpenTelemetry follows a paradigm of linking telemetry signals together. +With context creating touch points between signals, the overall value and usability of telemetry increase drastically. +For instance, imagine the ability to jump from conspicuous statistics in a dashboard straight to the related logs. +Correlated telemetry data helps to reduce the cognitive load on humans operating complex systems. +Being able to take advantage of linked data will mark a new generation of observability tools. diff --git a/tutorial/content/intro/how_we_got_here/index.md b/tutorial/content/intro/how_we_got_here/index.md index 7b88178..bd2c2c5 100644 --- a/tutorial/content/intro/how_we_got_here/index.md +++ b/tutorial/content/intro/how_we_got_here/index.md @@ -17,7 +17,7 @@ First, there is the *workload*. These are the operations a system performs to fulfill its objectives. For instance, when a user sends a request, a distributed system often breaks it down into smaller tasks handled by different services. Second, there are *software abstractions* that make up the structure of the distributed system. -These includes elements such as load balancers, services, pods, containers and more. +This includes elements such as load balancers, services, pods, containers and more. Lastly, there are physical machines that provide computational *resources* (e.g. RAM, CPU, disk space, network) to carry out work. {{< figure src="images/workload_resource_analysis_gregg.png" width=400 caption="workload and resource analysis [[Gregg16]](https://www.brendangregg.com/Slides/ACMApplicative2016_SystemMethodology/#18)" >}} @@ -26,12 +26,12 @@ developers need highly detailed telemetry that they can use to pinpoint specific --> Depending on our background, we often have a certain bias when investigating the performance of / troubleshooting problems in a distributed system. Application developers typically concentrate on workload-related aspects, whereas operations teams tend to look at physical resources. -To truely understand a system, we must combine insights from multiple angles and figure out how they relate to one another. +To truly understand a system, we must combine insights from multiple angles and figure out how they relate to one another. However, before we can analyze something, we must first capture aspects of system behavior. -As you may know, we commonly do this through combination of *logs*, *metrics* and *traces*. +As you may know, we commonly do this through a combination of *logs*, *metrics* and *traces*. Although it seems normal today, things weren't always this way. But why should you be concerned about the past? -The reason is that OpenTelemetry tries to address problems that are the result of historical developments. . +The reason is that OpenTelemetry tries to address problems that are the result of historical developments. #### logs {{< figure src="images/logs.png" width=600 caption="Exemplary log files" >}} @@ -48,9 +48,9 @@ hard to agree on semantics / language A *log* is an append-only data structure that records events occurring in a system. A log entry consists of a timestamp that denotes when something happened and a message to describe details about the event. However, coming up with a standardized log format is no easy task. -One reason is that different types of software often convey different pieces of information. Logs of an HTTP web server are bound to look different from those of the kernel. +One reason is that different types of software often convey different pieces of information. The logs of an HTTP web server are bound to look different from those of the kernel. But even for similar software, people often have different opinions on what good logs should look like. -Apart from content, log formats also vary with their consumer. Initially, text-based formats catered to human readability. +Apart from content, log formats also vary with their consumers. Initially, text-based formats catered to human readability. However, as software systems became more complex, the volume of logs soon became unmanageable. To combat this, we started encoding events as key/value pairs to make them machine-readable. This is commonly known as structured logging. @@ -81,42 +81,42 @@ Instead of just looking at individual events—logs—tracing systems looked at {{< figure src="images/distributed_system.drawio.png" width=400 caption="Exemplary architecture of a distributed system" >}} As distributed systems grew in scale, it became clear that traditional logging systems often fell short when trying to debug complex problems. -The reason is that we often have to undertand the chain of events in a system. +The reason is that we often have to understand the chain of events in a system. On a single machine, stack traces allow us to track an exception back to a line of code. In a distributed environment, we don't have this luxury. Instead, we perform extensive filtering to locate log events of interest. -To understand the larger context, we must identify other related event events. +To understand the larger context, we must identify other related events. This often results in lots of manual labour (e.g. comparing timestamps) or requires extensive domain knowledge about the applications. Recognizing this problem, Google developed [Dapper](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/36356.pdf), which popularized the concept of distributed tracing. On a fundamental level, tracing is logging on steroids. The underlying idea is to add transactional context to logs. -By indexing this based on this information, it is possible infer causality and reconstruct the journey of requests in the system. +By indexing this based on this information, it is possible to infer causality and reconstruct the journey of requests in the system. #### three pillars of observability On the surface, logs, metrics, and traces share many similarities in their lifecycle and components. Everything starts with instrumentation that captures and emits data. -The data has to have certain structure, which is defined by a format. +The data has to have a certain structure, which is defined by a format. Then, we need a mechanism to collect and forward a piece of telemetry. -Often there is some kind of agent to further enrich, process and batch data before ingesting it in a backend. +Often, there is some kind of agent to further enrich, process and batch data before ingesting it in a backend. This typically involves a database to efficiently store, index and search large volumes of data. Finally, there is analysis frontend to make the data accessible to the end-user. However, in practice, we develop dedicated systems for each type of telemetry, and for good reason: -Each telemetry signal poses it's own unique technical challenge. -This is mainly due to the different nature of data. -The design of data models, interchange formats, transmission protocols, highly depends on whether you are dealing with un- or semi-structured textual information, compact numerical values inside a time series, or graph-like structures depicting causality between events. -Even for a single signal, there is no consensus on these kind of topics. +Each telemetry signal poses its own unique technical challenge. +This is mainly due to the different nature of the data. +The design of data models, interchange formats, and transmission protocols, highly depends on whether you are dealing with un- or semi-structured textual information, compact numerical values inside a time series, or graph-like structures depicting causality between events. +Even for a single signal, there is no consensus on these kinds of topics. Furthermore, the way we work with and derive insights from telemetry varies dramatically. -A system might need to perform full-text search, inspect single events, analyze historical trends, visualize request flow, diagnose performance bottlenecks, and more. +A system might need to perform full-text searches, inspect single events, analyze historical trends, visualize request flow, diagnose performance bottlenecks, and more. These requirements manifest themselves in the design and optimizations of storage, access patterns, query capabilities and more. When addressing these technical challenges, [vertical integration](https://en.wikipedia.org/wiki/Vertical_integration) emerges as a pragmatic solution. -In practice, observability vendors narrow the scope of the problem to a single signal and provide instrumentation to generate *and* tools to analyse telemetry, as a single, fully-integrated, solution. +In practice, observability vendors narrow the scope of the problem to a single signal and provide instrumentation to generate *and* tools to analyze telemetry, as a single, fully integrated, solution. {{< figure src="images/three_pillars_of_observability.drawio.png" width=400 caption="The three pillars of observability, including metrics, traces and logs" >}} -Having dedicated systems for logs, metrics, and traces is why we commonly refer to them as the *the three pillars of observability*. +Having dedicated systems for logs, metrics, and traces is why we commonly refer to them as the *three pillars of observability*. 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file mode 100644 index 0000000..d67c0c0 --- /dev/null +++ b/tutorial/content/intro/overview_of_otel/images/otel_collector_overview.drawio.xml @@ -0,0 +1,163 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tutorial/content/intro/overview_of_otel/index.md b/tutorial/content/intro/overview_of_otel/index.md index c71b08e..cabc046 100644 --- a/tutorial/content/intro/overview_of_otel/index.md +++ b/tutorial/content/intro/overview_of_otel/index.md @@ -17,41 +17,41 @@ weight: 40 --> Only time will tell if OpenTelemetry can live up to its ambitious goals. -Chances are, you're eager to explore the project and try it out yourself to see what the fuss it about. +Chances are, you're eager to explore the project and try it out yourself to see what the fuss is about. However, newcomers often feel overwhelmed when getting into OpenTelemetry. -The reason is clear: OpenTelemetry is a vast endeavor that addresses a multitiude of problems by creating a comprehensive observability framework. +The reason is clear: OpenTelemetry is a vast endeavor that addresses a multitude of problems by creating a comprehensive observability framework. Before you dive into the labs, we want to give you a high-level overview of the structure and scope of the project. ### signal specification (language-agnostic) On a high level, OpenTelemetry is organized into *signals*, which mainly include *tracing*, *metrics*, *logging* and *baggage*. -Every signal is developed as a standalone component (but there are ways to connect data streams to another. +Every signal is developed as a standalone component (but there are ways to connect data streams to another). Signals are defined inside OpenTelemetry's *language-agnostic* [specification](https://opentelemetry.io/docs/specs/), which lies at the very heart of the project. -End-user probably won't come in direct contact with the specification, but it plays an crucial role for ensuring consistency and interoperability wihtin the OpenTelemetry ecosystem. +The end-user probably won't come into direct contact with the specification, but it plays a vital role in ensuring consistency and interoperability within the OpenTelemetry ecosystem. {{< figure src="images/otel_specification.drawio.png" width=600 caption="OpenTelemetry specification" >}} The specification consists of three parts. First, there are [*definitions of terms*](https://opentelemetry.io/docs/specs/otel/glossary/) that establish a common vocabulary and shared understanding to avoid confusion. -Second, it specificies the technical details of how each signals is designed. +Second, it specifies the technical details of how each signal is designed. This includes: - an *API specification* (see [traces](https://opentelemetry.io/docs/specs/otel/trace/api/), [metric](https://opentelemetry.io/docs/specs/otel/metrics/api/), and [logs](https://opentelemetry.io/docs/specs/otel/logs/)) - defines (conceptual) interfaces that implementations must adhere to - - ensures that implemenations are compatible with each other + - ensures that implementations are compatible with each other - includes the methods that can be used to generate, process, and export telemetry data - a *SDK specification* (see [trace](https://opentelemetry.io/docs/specs/otel/trace/sdk/),[metrics](https://opentelemetry.io/docs/specs/otel/metrics/sdk/), [logs](https://opentelemetry.io/docs/specs/otel/logs/sdk/)) - serves as a guide for developers - - defines requirements that an language-specific implementation of the API must meet to be compliant + - defines requirements that a language-specific implementation of the API must meet to be compliant - includes concepts around the configuration, processing, and exporting of telemetry data -Besides signal architecture, the specification covers aspect related to the telemetry data. +Besides signal architecture, the specification also covers aspects related to telemetry data. For example, OpenTelemetry defines [semantic conventions](https://opentelemetry.io/docs/specs/semconv/). By pushing for consistency in the naming and interpretation of common telemetry metadata, OpenTelemetry aims to reduce the need to normalize data coming from different sources. Finally, there is also the [OpenTelemetry Protocol (OTLP)](https://opentelemetry.io/docs/specs/otlp/), which we'll cover later. -### instrumentation to generate and emit telemetry (language-specific) +### vendor-agnostic instrumentation (language-specific) {{< figure src="images/otel_implementation.drawio.png" width=600 caption="generate and emit telemetry via the OTel API and SDK packages" >}} -To generate and emit telemetry from applications we use **language-*specific* implementations**, which adhere to OpenTelemetry's specification. +To generate and emit telemetry from applications, we use **language-*specific* implementations**, which adhere to OpenTelemetry's specification. OpenTelemetry supports a wide-range of popular [programming languages](https://opentelemetry.io/docs/instrumentation/#status-and-releases) at varying levels of matureity. The implementation of a signal consists of two parts: - *API* @@ -61,7 +61,7 @@ The implementation of a signal consists of two parts: - *SDK* - provider implement the OpenTelemetry API - contains the actual logic to generate, process and emit telemetry - - OpenTelemetry ships with official providers that serve as the reference implementation (commonly refered to as the SDK) + - OpenTelemetry ships with official providers that serve as the reference implementation (commonly referred to as the SDK) - possible to write your own @@ -69,10 +69,10 @@ The implementation of a signal consists of two parts: Generally speaking, we use the OpenTelemetry API to add instrumentation to our source code. In practice, instrumentation can be achieved in various ways, such as: - manual instrumentation - - requires modification of source code + - requires modification of the source code - allows fine-grained control over what and how telemetry gets generated - auto-instrumentation (if available) and library instrumentation to avoid code changes - - can be used to getting started quickly with observability + - can be used to get started quickly with observability - collects predefined metrics, traces and logs within a library or framework - added after the fact by injecting an agent (or already included inside the library or framework) - requires close to zero code changes @@ -80,17 +80,17 @@ In practice, instrumentation can be achieved in various ways, such as: We'll look at them later in more detail. -For now, let's focus on why OpenTelemetry decides to separate the API from the SDK. +For now, let's focus on why OpenTelemetry decided to separate the API from the SDK. On startup, the application registers a provider for every type of signal. After that, calls to the API are forwarded to the respective provider. If we don't explicitly register one, OpenTelemetry will use a fallback provider that translates API calls into no-ops. The primary reason is that it makes it easier to embed native instrumentation into open-source library code. -OpenTelemetry's API is designed to be lightwight and safe to depend on. -The signal's implementation provided by the SDK is signifanctly more complex and likely contains dependencies to other software. -Forcing these dependencies onto users could lead to conflicts with their particular software stack. +OpenTelemetry's API is designed to be lightweight and safe to depend on. +The signal's implementation provided by the SDK is significantly more complex and likely contains dependencies on other software. +Forcing these dependencies on users could lead to conflicts with their particular software stack. Registering a provider during the initial setup allows users to resolve dependency conflicts by choosing a different implementation. -Furthermore, it allows us to ship software with built-in observability, but without forcing the runtime cost of instrumentation onto users that don't need it. +Furthermore, it allows us to ship software with built-in observability without forcing the runtime cost of instrumentation onto users that don't need it. -### collect, process and export telemetry -### data transmission -To ensure that the collected telemetry data can be collected across different frameworks, libraries or programming languages a vendor-neutral protocol was set into place. The OpenTelemetry Protocol (OTLP) is an open-source protocol for collecting and transmitting telemetry data, to back-end systems for analysis and storage. It defines a standardized data model, encoding format, and transport mechanisms to enable interoperability between telemetry tools and services from different vendors. By standardizing the way telemetry data is collected and transported, OTLP simplifies the integration of telemetry tools and services, improves data consistency, and facilitates data analysis and visualization across multiple technologies and environments. - -OTLP offers three transport mechanisms for transmitting telemetry data: HTTP/1.1, HTTP/2, and gRPC. When using OTLP, the choice of transport mechanism depends on application requirements, considering factors such as performance, reliability, and security. OTLP data is often encoded using the Protocol Buffers (Protobuf) binary format, which is compact and efficient for network transmission and supports schema evolution, allowing for future changes to the data model without breaking compatibility. Data can also be encoded in the JSON file format which allows for a human-readable format with the disadvantage of higher network traffic and larger file sizes. The protocol is described in the OpenTelemetry Protocol Specification. \ No newline at end of file +### telemetry processor (stand-alone component) +{{< figure src="images/otel_collector_overview.drawio.png" width=650 caption="processing and fowarding telemetry to backends" >}} + +So far, we have seen that OpenTelemetry provides tooling for vendor-agnostic instrumentation to application and library developers. +This alone marks a significant milestone, but OpenTelemetry's framework goes much further. +After generating and emitting telemetry, operators are responsible for managing and ingesting it into the respective backends. +This includes tasks such as: +- gathering data from various sources +- parsing and converting it for downstream processing +- enrichment with additional metadata +- filtering out irrelevant data to reduce noise and storage requirements +- normalization and applying transformations +- buffering for resilience and performance +- routing to steer subsets of telemetry to different destinations +- forwarding to backends + +To build and configure such telemetry pipelines, operations teams often deploy additional infrastructure. +A popular example is the [fluentbit](https://fluentbit.io/) telemetry agents. +Similarly, OpenTelemetry provides a standalone component with these capabilities: the OpenTelemetry [Collector](https://opentelemetry.io/docs/collector/). + +### wire protocol + +Completing the package of standardization, generation and management, OpenTelemetry also defines how to transport telemetry between producers, agents, and backends. +The [OpenTelemetry Protocol (OTLP)](https://opentelemetry.io/docs/specs/otel/protocol/) is an open-source and vendor-neutral wire format that defines: +- how data is encoded in memory +- a protocol to transport that data across the network + +As a result, OTLP is used throughout the observability stack. +Emitting telemetry in OLTP means that instrumented applications and third-party services are compatible with countless observability solutions. +The collector supports receiving telemetry from and exporting to a wide array of formats (e.g. Prometheus Metrics, Zipkin traces, etc.). +However, using OTLP is generally preferred, because the Collector also uses it internally to represent and process telemetry. +Thereby, we avoid the cost of having to convert between formats and increase consistency. +This is because the native format closely aligns with the ideas proposed by the framework (having attributes follow semantic conventions, cross-signal correlation, etc.). +Similarly, most observability backends support OTLP right out-of-the-box. +Given the rapid adoption of OpenTelemetry, integrating with OTLP automatically gives you access to a wide audience of potential users. +Moreover, an open and vendor-neutral telemetry protocol means less work for developers of observability tools. +Before, you had to develop countless adapters to be able to ingest data arriving in various proprietary formats. +In other words, OTLP is a significant push for interoperability between tools and services in the observability ecosystem. + +OTLP offers three transport mechanisms for transmitting telemetry data: HTTP/1.1, HTTP/2, and gRPC. +When using OTLP, the choice of transport mechanism depends on application requirements, considering factors such as performance, reliability, and security. +OTLP data is often encoded using the Protocol Buffers (Protobuf) binary format, which is compact and efficient for network transmission and supports schema evolution, allowing for future changes to the data model without breaking compatibility. +Data can also be encoded in the JSON file format, which allows for a human-readable format with the disadvantage of higher network traffic and larger file sizes. \ No newline at end of file diff --git a/tutorial/content/intro/problems_with_the_status_quo/index.md b/tutorial/content/intro/problems_with_the_status_quo/index.md index d450e26..5b2516e 100644 --- a/tutorial/content/intro/problems_with_the_status_quo/index.md +++ b/tutorial/content/intro/problems_with_the_status_quo/index.md @@ -7,7 +7,7 @@ weight: 20 With loads of open-source and commercial observability solutions on the market, you might (rightly) ask yourself: - Why is there so much hype around OpenTelemetry? -- If there are plenty mature solutions for generating, collecting, storing, and analysing logs, metrics and traces, why should I care? +- If there are plenty of mature solutions for generating, collecting, storing, and analyzing logs, metrics and traces, why should I care? - What's wrong with the current state of observability? - Oh, great ... is this yet another attempt at standardization? @@ -35,7 +35,7 @@ After discovering log events of interest, we often want to know about the larger Unfortunately, traditional logging systems lack the mechanisms to reconstruct the chain of events in that particular transaction. Traditional logging systems often fail to capture the full context of an operation, making it difficult to correlate events across different services or components. They frequently lack the ability to preserve critical metadata, such as trace IDs or span IDs, which are essential for linking related events together. This limitation results in fragmented views of the system's behavior, where the story of a single operation is spread across multiple logs without a clear narrative. Furthermore, the absence of standardized query languages or interfaces adds to the difficulty of searching and analyzing logs effectively, as operators must rely on custom scripts or manual filtering to uncover patterns and anomalies. -Switching the perspective from someone building an observability solution to someone using it reveals an inherent disconnect. +Switching perspectives from someone building an observability solution to someone using it reveals an inherent disconnect. The real world isn't made up of logging, metrics, or tracing problems. Instead, we have to move back and forth between different types of telemetry to build up a mental model and reason about the behavior of a system. Since observability tools are silos of disconnected data, figuring out how pieces of information relate to one another causes a significant cognitive load for the operator. @@ -47,7 +47,7 @@ Since observability tools are silos of disconnected data, figuring out how piece - report the same thing in different ways --> -Another factor that makes root cause analysis hard is that telemetry data often suffers from a lack of consistency. This leads to difficulties in correlating events across different services or components, as there is no standardized way to identify related events, such as through trace IDs or span IDs. Additionally, there is no straightforward method to integrate multiple solution-specific logging libraries into a coherent system, resulting in fragmented and disjointed views of the system's behavior. +Another factor that makes root-cause analysis hard is that telemetry data often suffers from a lack of consistency. This leads to difficulties in correlating events across different services or components, as there is no standardized way to identify related events, such as through trace IDs or span IDs. Additionally, there is no straightforward method to integrate multiple solution-specific logging libraries into a coherent system, resulting in fragmented and disjointed views of the system's behavior. #### no built-in instrumentation in open-source software Let's look at this from the perspective of open-source software developers. @@ -56,9 +56,9 @@ With a majority of work being performed outside the business logic of the applic The people with the most knowledge of what is important when operating a piece of software are the developers and maintainers themselves. However, there is currently no good way to communicate through native instrumentation. One option would be to pick the instrumentation of an observability solution. -However, this would add additional dependencies to the project and force users to integrate it into their system. +However, this would add additional dependencies to the project and force users to integrate it into their systems. While running multiple logging and metrics systems is impractical but technically possible, tracing is outright impossible as it requires everyone to agree on a standard for trace context propagation to work. -A common strategy for solving problems in computer science is by adding a layer of indirection. +A common strategy for solving problems in computer science is to add a layer of indirection. Instead of embedding vendor-specific instrumentation, open-source developers often provide observability hooks. This allows users to write adapters that connect the open-source component to their observability system. While this approach provides greater flexibility, it also has its fair share of problems. @@ -88,12 +88,12 @@ This up-front cost of re-instrumentation makes migration difficult, which is a f The last part of the equation is the observability vendors themselves. At first glance, vendors appear to be the only ones profiting from the current situation. In the past, high-quality instrumentation was a great way to differentiate yourself from the competition. -Moreover, since developing integrations for loads of pre-existing software is expensive, the observability market had a relatively high barrier to entry. +Moreover, since developing integrations for loads of pre-existing software is expensive, the observability market has a relatively high barrier to entry. With customers shying away from expensive re-instrumentation, established vendors faced less competition and pressure to innovate. However, they are also experiencing major pain points. The rate at which software is being developed has increased exponentially over the last decade. -Today's heterogeneous software landscape made it infeasible to maintain instrumentation for every library, framework, and component. +Today's heterogeneous software landscape makes it impossible to maintain instrumentation for every library, framework, and component. As soon as you start struggling with supplying instrumentation, customers will start refusing to adopt your product. -As a result, solutions compete on who can build the best n-to-n format converter instead of investing these resources into creating great analysis tools. -Another downside is converting data that was generated by foreign sources often leads to a degradation in the quality of telemetry. -Once data is no longer well-defined, it becomes harder to analyze. +As a result, solutions compete over who can build the best n-to-n format converter instead of investing these resources into creating great analysis tools. +Another downside is that converting data that was generated by foreign sources often leads to a degradation in the quality of telemetry. +Once data is no longer well-defined, it becomes harder to analyze. \ No newline at end of file

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