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https://newrelic.com/blog/how-to-relic/dude-wheres-my-error +--- + +![A confused penguin trying to learn about errors and exceptions. Image generated with AI using Dalle3 via Bing Copilot](penguin-chalkboard.jpg) + +Depending on the language you’re used to developing in, you may have certain +ideas about what an error is, as well as what constitutes an exception and how +it should be handled. For example, Go doesn't have exceptions, partly to +discourage programmers from labeling too many ordinary errors as exceptional. On +the other hand, languages such as Java and Python provide built-in support for +throwing and catching exceptions. + +When different languages disagree about what errors or exceptions are and how to +handle them, what do you use when you need standardized telemetry and error +reporting across microservices written in those languages? OpenTelemetry is the +tool with which we'll address the following, and more: + +- How an error is visualized in a backend might not be where you think it’ll be, + or how you expect it to look. +- How span kind affects error reporting. +- Errors reported by spans versus logs. + +## Errors versus exceptions + +Before we get into how OTel deals with errors and exceptions, let’s establish +what they are, and how they differ from each other. While there are variations +on the definitions of these terms, we’ve landed on the following ones, which +we’ll be using in this article. Note that this is **not** official OTel +language; they are general industry definitions: + +An **error** is an unexpected issue in a program that hinders its execution. +Examples include syntax errors, such as a missing semicolon or incorrect +indentation, and runtime errors, resulting from errors in logic. + +An **exception** is a type of runtime error that disrupts the normal flow of a +program. Examples include dividing by zero or accessing an invalid memory +address. + +Some languages, such as Python and JavaScript, treat errors and exceptions as +synonyms; others, such as PHP and Java, do not. Understanding the distinction +between errors and exceptions is crucial for effective error handling, because +it empowers you to adopt more nuanced strategies for handling and recovering +from failures in your applications. + +## Handling errors in OTel + +So how does OTel deal with all these conceptual differences across languages? +This is where the [specification](/docs/specs/otel/) (or “spec” for short) comes +in. The spec provides a blueprint for developers working on various parts of the +project, and standardizes implementation across all languages. + +Since language APIs and SDKs are implementations of the spec, there are general +rules against implementing anything that isn’t covered in the spec. This +provides a guiding principle to help organize contributions to the project. In +practice, there are a few exceptions; for example, a language might prototype a +new feature as part of adding it to the spec, but the feature may be published +(usually as alpha or experimental) before the corresponding language is added. + +Another exception is when a language might decide to diverge from the spec. +Although it is generally not advised, sometimes there are strong +language-specific reasons to do something different. In this way, the spec +allows for some flexibility for each language to implement features as +idiomatically as possible. For example, most languages have implemented +`RecordException` (for example, +[Python](https://opentelemetry-python.readthedocs.io/en/latest/_modules/opentelemetry/sdk/trace.html#Span.record_exception)), +while Go has implemented +[`RecordError`](https://github.com/open-telemetry/opentelemetry-go/blob/main/sdk/trace/span.go), +which does the same thing. + +You can view this +[compliance matrix](https://github.com/open-telemetry/opentelemetry-specification/blob/main/spec-compliance-matrix.md) +of the spec across all languages, but you’ll get the most updated info by +checking the individual language repository. Now we can begin figuring out how +to handle errors in OTel, starting with how to report them: + +- Spans +- Logs + +### Errors in spans + +In OTel, spans are the building blocks of distributed traces, representing +individual units of work within a distributed system. Spans are related to each +other and to a trace through context. Put simply, context is the glue that turns +a pack of data into a unified trace. Context propagation allows us to pass +information across multiple systems, therefore tying them together. Traces can +tell us all sorts of things about our applications through metadata and span +events. + +![Graphic that shows the spans within a trace](OTel-spans.png) + +### Enhancing spans with metadata + +OTel enables you to enhance spans with metadata +([attributes](/docs/concepts/signals/traces/#attributes)) in the form of +key-value pairs. By attaching relevant information to spans, such as user IDs, +request parameters, or environment variables, you can gain deeper insights into +the circumstances surrounding an error and quickly identify its root cause. This +metadata-rich approach to error handling can significantly reduce the time and +effort required to diagnose and resolve issues, ultimately improving the +reliability and maintainability of your applications. + +Spans also have a [span kind](/docs/concepts/signals/traces/#span-kind) field, +which gives us some additional metadata that can help developers troubleshoot +errors. OTel defines several span kinds, each of which has unique implications +for error reporting: + +- **client**: For outgoing synchronous remote calls (for example, outgoing HTTP + request or DB call) +- **server**: For incoming synchronous remote calls (for example, incoming HTTP + request or remote procedure call) +- **internal**: For operations that do not cross process boundaries (for + example, instrumenting a function call) +- **producer**: For the creation of a job which may be asynchronously processed + later (for example, job inserted into a job queue) +- **consumer**: For the processing of a job created by a producer, which may + start long after the producer span has ended + +Span kind is determined automatically by the instrumentation libraries used. + +Spans can be further enhanced with +[span status](/docs/concepts/signals/traces/#span-status). By default, span +status is marked as `Unset` unless otherwise specified. You can mark a span +status as `Error` if the resulting span depicts an error, and `Ok` if the +resulting span is error-free. + +### Enhancing spans with span events + +A [span event](/docs/concepts/signals/traces/#span-events) is a structured log +message embedded within a span. Span events help enhance spans by providing +descriptive information about a span. +[Span events can also have attributes of their own](/docs/languages/ruby/instrumentation/#add-span-events). + +When a span status is set to `Error`, a span event is created automatically, +capturing the span’s resulting error message and stack trace as an event on that +span. You can further enhance this span error by adding attributes to it. + +Earlier, we mentioned a method called `RecordException`. Per +[the spec](/docs/specs/otel/trace/api/#record-exception) (emphasis our own), “To +facilitate recording an exception languages SHOULD provide a RecordException +method **if the language uses exceptions**… The signature of the method is to be +determined by each language and can be overloaded as appropriate.” + +Since Go doesn’t support the “conventional” concept of exceptions, it instead +supports +[`RecordError`](https://github.com/open-telemetry/opentelemetry-go/blob/main/sdk/trace/span.go#L445-L467), +which essentially does the same thing idiomatically. You have to make an +additional call to set its status to `Error` if that’s what it should be, as it +won’t automatically be set to that. Similarly, `RecordException` can be used to +record span events without setting the span’s status to `Error`, which means you +can use it to record any additional data about a span. + +By decoupling the span status from being automatically set to `Error` when a +span exception occurs, you can support the use case where you can have an +exception event with a status of `Ok` or `Unset`. This gives instrumentation +authors the most flexibility. + +### Errors in logs + +In OTel, a log is a structured, timestamped message emitted by a service or +other component. The recent addition of logs to OTel gives us yet another way of +reporting errors. Logs have traditionally had different severity levels for +representing the type of message being emitted such as `DEBUG`, `INFO`, +`WARNING`, `ERROR`, and `CRITICAL`. + +OTel allows for the correlation of logs to traces, in which a log message can be +associated to a span within a trace, via trace context correlation. Hence, +looking for a log message with a log level of `ERROR` or `CRITICAL` can yield +further information of what led to that error, by pulling up the correlated +trace. + +To record an error on a log, either `exception.type` or `exception.message` is +required, while `exception.stacktrace` is recommended. For more information, see +[Semantic Conventions for Exceptions in Logs](/docs/specs/semconv/exceptions/exceptions-logs/). + +## Logs or spans to capture errors? + +After all this, you might be wondering which signal to use to capture errors: +spans or logs? The answer is: "It depends!" Perhaps your team primarily uses +traces; perhaps it primarily uses logs. + +Spans can be great for capturing errors, because if the operation errors out, +marking a span as an error makes it stand out and therefore easier to spot. On +the other hand, if you’re not filtering or tail sampling your traces and your +system is producing thousands of spans per minute, you could miss errors that +aren’t occurring frequently, but that still need to be handled. + +What about using span events versus logs? Again, this depends. It may be +convenient to use span events, because when a span status is set to `Error`, a +span event with the exception message (and other metadata you may wish to +capture) is automatically created. + +Another consideration is your observability backend. Does your backend render +both logs and traces? How easily queryable or discoverable are your logs, spans, +and span events? Is logs and trace correlation supported? + +## Visualizing errors in different backends + +While OTel provides us with the raw telemetry data emitted by our systems, it +doesn’t provide data visualization or interpretation. This is done by an +observability backend. Because OTel is vendor-neutral, it means that the same +information emitted can be visualized and interpreted by different backends +without re-instrumenting your application. + +### Jaeger + +Let’s take a look at what an OTel error looks like in +[Jaeger](https://www.jaegertracing.io/). The error data was generated by the +code in [this repository](https://github.com/avillela/otel-errors-talk). Here is +a trace view for the service +[py-otel-server](https://github.com/avillela/otel-errors-talk/blob/main/src/python/server.py). +As you can see below, the error spans show up as red dots: + +![List of traces in the Jaeger UI](jaeger-high-level-view.png) + +And if we drill down and zero in on the error span, we can click into `Logs`, +which is how span events are expressed in Jaeger, and view the information that +was captured on it: + +![Attributes and other metadata for an error span in Jaeger](jaeger-error.png) + +The span is clearly marked as error, and includes a span event with the +exception captured. Jaeger expresses the span event as a log, but does not +visualize logs outside of spans. + +### Proprietary backends + +If you’ve been using a proprietary agent to monitor your applications and have +recently migrated to OTel, you might notice that an OTel error may not be +expressed the way you expect in your observability backend, as compared to the +same error captured by the proprietary agent. This is most likely due to the +fact that OTel simply models errors differently than how vendors have been +modeling them. + +As a broad example, vendors might have their own notion of what constitutes a +logical unit of work in an application. You may be familiar with the term +`transaction`, which means something slightly different from vendor to vendor. +In OTel, this is represented by a trace. You’ve likely noticed differences in +your data visualization experiences as vendors make their own adjustments to +their platforms to accommodate OTLP as a first-class citizen data type. + +As a more specific example, OTel’s notion of span kinds may affect how your OTel +error is expressed in your backend. For instance, if you have a trace that has +one exception and it’s on an internal span with its status set to `Error`, you +should see the trace marked with an error, but it may not be counted toward your +overall app error rate. This is because the vendor might have an opinion that +only errors on entry point spans (server spans) and consumer spans should be +counted toward your error rate. + +If your backend supports trace and log correlation, you should be able to +navigate to the associated trace from the log, and vice versa. Furthermore, +while Jaeger visualizes span events as logs, some vendors might synthesize span +events as its own data type instead of as a log data type, which would impact +the way you query that data. + +## Conclusion + +We’ve just explored the challenges of handling errors and exceptions across +different programming languages within a microservices architecture, and +introduced OTel as a solution for standardized telemetry and error reporting. +The OTel specification serves as a blueprint for standardizing error handling +across various languages, providing guidelines for implementation, but allowing +for a degree of flexibility. + +You can record errors on spans by making use of your language SDK’s +`RecordException` or its equivalent, and enrich the span events further by +adding custom attributes. You can also record errors on logs by adding +`exception.type` or `exception.message`, and capture the stack trace by adding +`exception.stacktrace` to yield further information about what happened. + +Once that data is in your observability backend, if you have previously used +their proprietary monitoring agent, you might notice that there is a difference +in how OTel-instrumented errors are visualized versus how the agent-instrumented +errors were visualized. This is mainly because OTel models errors differently +than vendors might have previously done. + +By leveraging OTel's capabilities to record errors through logs and spans and to +enhance them with metadata, you can gain deeper insights into your applications' +behavior and more effectively troubleshoot issues. You'll be better equipped to +build and maintain resilient, reliable, and high-performing software +applications in today's dynamic and demanding environments. To learn more, see +[Error handling in OpenTelemetry](/docs/specs/otel/error-handling/). + +_A version of this article was +[originally posted](https://newrelic.com/blog/how-to-relic/dude-wheres-my-error) +on the New Relic blog._ diff --git a/content/en/blog/2024/otel-errors/jaeger-error.png b/content/en/blog/2024/otel-errors/jaeger-error.png new file mode 100644 index 0000000000000000000000000000000000000000..e2364f02b5ba00b7cdc4187f27625a4138d36017 GIT binary patch literal 430788 zcmeFYcUTkewl+*tdIv$OU;&gW9YR2=g^u(tA|kyBgaAqrkuK7tgLG-qk={a&6zN3> z5IQ8GB-D4qn;VFs0LA2HFJNSy>A~espdvk&u9(5B)c#{!sSqOF!8-bPs8DXa*AyT9-930PT$-U%*H1ojC2ok01^nv6roNhqy_b|K62j;am-_UbuvS`U z`bA(OBGA-1+Ee%gqvM;D&#^|+Y1Z@3wpvv2*5V)iRP5t)XdE7$J?c$5Rf^Pon8!s* z7C0W81p6RP*hnLbYQVRqVO3J8xJA|F8k#j0f=l!HczDZ#x0gIp@5J%5 zdbECzBJ1c~$HdR_u!LSj{|mX;S3DJ8PRd2EvE_mDEs}3$5{X=U5kX-6;!f8ljqI8& zER@f`hvC(a+r<8NMvaUwZqUHUl-n=J{J-Bar(-BN5E>d{O%tl+)9Vd?zU+@nL2-RL zu_^_XfdZwNwtpyeJ!KxJU5TRPOe43Xp(#w3oe-e8+r{Sv{)joZVYG4kB*kO05tB;l 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