Serverless Observability: The Ultimate Guide
Observability is an application state that gives you both the insight you need to understand what went wrong, and the tracing and tracking capabilities that help you understand why an error occurred.
In any application, high observability is a prerequisite for high availability. And to achieve observability in serverless applications, it’s important to get a complete picture — not just the snapshots of a single function call that most providers focus on.
This article explores the issue of observability and how a serverless environment complicates it. We then tour the native and third-party serverless observability tools and describe the journey toward ultimate observability.
We also invite you to download the more detailed serverless observability guide on which this article is based.
Defining Observability
The goal of observability is to give you full, end-to-end visibility into all of your application’s performance characteristics. True observability, therefore, goes beyond a snapshot of code to incorporate execution context, event systems and third-party integrated services into a cohesive picture of your application’s behavior.
Testing verifies the correctness of the system against “known” issues. Monitoring checks “known” metrics to evaluate the health of the system. Observability, on the other hand, is a state achieved through instrumentation of the application, so that developers have enough information to tackle the “unknowns.” Observability is essential for building a maintainable system.
Observability Challenges in Serverless Applications
Serverless applications are particularly challenging when it comes to observability. In a distributed microservices architecture, each individual service is sizable and complex enough to understand at a service-interaction level. Observability can be achieved by examining machine characteristics alongside coherent stack traces that clearly lay out the path of control flow.
In serverless applications, however, the event-driven functions are disparate, operate in isolation and are highly ephemeral. It is very difficult to analyze them for potential side effects (such as partially processed batches).
In short, any observability characteristics that would prove useful for a serverless application need to be built from scratch. Thus, it’s important to keep in mind the observability of your end state application at each phase of the development process.
Observability Using AWS Tools
Luckily, the serverless observability problem has not gone unnoticed by AWS, which provides two critical resources that contribute to your serverless application’s overall observability: AWS CloudWatch and AWS X-Ray.
These tools are your first line of defense when things go wrong, and it’s important to understand the role each plays in a serverless application.
AWS CloudWatch
AWS CloudWatch is the log and metric ingestion and visualization suite that is integrated with AWS Lambda by default. However, you need to take into account CloudWatch limits and costs — which do not scale well in serverless contexts. For example, the out-of-the-box function-level CloudWatch logs are problematic if the function that is failing is one in a chain of Lambda function requests that share a common origin. The workaround here is to implement CloudWatch configurable metric filters to aggregate the different function logs.
AWS X-Ray
Another native observability solution for AWS Lambda is AWS X-Ray, a distributed tracing system designed to help pinpoint failures in a serverless architecture. However, X-Ray and CloudWatch are not tightly integrated into a cohesive UI. Developers attempting to troubleshoot a serverless transaction need to continually switch between CloudWatch and X-Ray to get a full view of the state of the system.
So is there a better solution?
Observability with Open Source Software
Provider tools like CloudWatch and X-Ray get you close to a full observability picture, but they have their limitations. Imagine, for example, a serverless request that involves a third-party service. How do you incorporate the runtime and failure codes of that third-party service into your overall system map? Furthermore, X-Ray has distributed tracing support covering the AWS SQS and SNS, but applications built on DynamoDB, Kinesis and Firehose will need to build their own distributed tracing mechanisms.
Luckily, open-source observability and visualization tools have emerged to close these gaps, offering greater customizability and flexibility.
Observability Data Generation
The first step in building customized serverless observability is generating your system’s observable data. The original player in this space was the OpenTracing project, a set of provider-independent libraries and APIs that can be used to instrument your serverless applications. This has since grown to include telemetry and metrics in the OpenTelemetry project. These libraries are a critical part of building a custom tracing system for your AWS Lambda functions, providing tested reusable patterns and best practices.
Visualization Tools
Once you’ve generated your observability data, you need to present it cohesively. Raw log outputs are not particularly helpful when conceptualizing failures at the system level. Two of the leading open-source tracing visualization tools are Zipkin and Jaeger. These tools present generated traces and metrics in a readable format that shortens time-to-remediation.
Fully Automated Observability with Thundra
As your serverless applications become more complex, built-in observability tools usually become too limiting. One solution is to build a customized solution using open-source frameworks. However, these tools are often labor-intensive to implement and can be challenging to maintain as the application evolves. This is where you’ll want to turn to third-party tooling for the best tradeoff of value versus effort.
Thundra provides a comprehensive observability suite for serverless applications that runs out of the box, requiring minimal configuration to get all of your application’s serverless functions reporting correctly. Thundra’s drop-in libraries provide fully automated observability, integrating with the AWS Lambda execution layer to automatically instrument your functions at any point in their lifecycle. Thundra’s powerful visualizations also compile all of the data you need to track down failures and errant behavior in your serverless application.
You can further extend Thundra’s fully automated serverless observability solution by using the Thundra SDK to inject your own application-aware instrumentation logic.
The Journey Toward Ultimate Observability
Observability presents unique challenges to developers working in a serverless context. Built-in tools like CloudWatch and X-Ray provide a good start, but they have limitations that can be cost-prohibitive when you’re designing a fully observable system.
Open source tools let you build on top of these limitations, yielding a more complete and customizable picture of your application. Open source tools, however, tend to be labor-intensive when implemented at scale, and simple customizations can run into issues as they are spread throughout your organization.
Third-party tools like Thundra close the serverless observability gap completely, giving you an optimal mix between automated and manual observation techniques. By building on top of first-party solutions like CloudWatch to integrate with Lambda as it executes, Thundra gives you out-of-the-box capabilities that would require an entire development team to maintain in an open source-driven platform.
With Thundra, you can deploy a fully observable backend for your serverless application in minutes and start troubleshooting with confidence.
Visit Thundra’s website to download the full extensive guide on serverless observability.
Amazon Web Services is a sponsor of The New Stack.
Feature image via Pixabay.
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