What Is AWS Lambda Tracing?

AWS Lambda tracing allows you to track and visualize the flow of requests through your serverless applications. This can be helpful for identifying performance bottlenecks and debugging issues.

With Lambda tracing, you can see detailed information about the execution of your functions, including the time each step took and the downstream calls that were made. This allows you to understand how your application is performing and make any necessary changes to improve its efficiency and reliability.

This is part of a series of articles about serverless debugging.

Why Is Lambda Tracing Important?

With application tracing, you can see how different parts of your application interact with each other, and you can understand how long each part takes to complete. AWS Lambda tracing helps you understand the flow of requests and responses through your application, and it can help you identify issues and performance bottlenecks.

There are several benefits to using lambda tracing:

Debugging: Application tracing can help you identify issues and bottlenecks in your application, and it can provide insights into how to fix them.
Performance optimization: Application tracing can help you understand the performance of your application, so you can optimize it for better performance and lower costs.
Monitoring: Application tracing can provide real-time visibility into the behavior and performance of your application, so you can monitor it for issues and identify potential problems before they become critical.
Security: Application tracing can help you identify types of security vulnerabilities and potential attacks on your application, and it can help provide insights into how to protect your application from these threats.

What Are the Challenges of Lambda Tracing?

There are a few challenges that you may encounter when using performing tracing for AWS Lambda functions:

Integration with other AWS services: AWS Lambda integrates with a variety of other AWS services, such as Amazon S3, Amazon DynamoDB, and Amazon API Gateway. Tracing requests and responses as they flow through these services can be challenging, as they may have different tracing implementations or may not support tracing at all.
Complex architectures: AWS Lambda is often used to build complex, microservice-based architectures, which can make tracing more challenging. With many different functions and services interacting with each other, it can be difficult to understand the flow of requests and responses through the entire system.
Distributed systems: AWS Lambda functions are distributed across many different servers, which can make it difficult to understand the relationships between different parts of your application. Tracing requests and responses as they flow through a distributed system can be challenging, as you may need to analyze multiple trace logs from different servers.
Performance: Tracing can add overhead to your application, as it requires additional processing and storage resources. This can affect the performance of your application, especially if you are tracing a large volume of requests and responses.

Key Elements of Distributed Tracing in AWS Lambda

There are several ways to trace AWS Lambda functions, and the right approach will depend on your specific requirements and use case.

Functional Requirements

Functional requirements for distributed tracing include:

The ability to trace requests and responses across multiple services and components of an application.
The ability to aggregate and visualize trace data in a way that makes it easy to understand and troubleshoot issues.
Support for different protocols and formats, such as HTTP and gRPC, to ensure that all components of an application can be traced.

Non-functional Requirements

Non-functional requirements for distributed tracing include:

Language agnostic: the ability to trace requests and responses across multiple languages and runtime environments, such as Java, Python, and Node.js. This is important for applications that use a variety of different languages and frameworks.
Platform agnostic: the ability to trace requests and responses across multiple platforms, such as AWS Lambda, Kubernetes, and EC2. This is important for applications that are deployed on different platforms.
Low overhead: the ability to trace requests and responses with minimal impact on the performance of the application. This is important for ensuring that the application remains responsive and performant while tracing is enabled.

6 Key Elements of a Distributed Tracing Solution

Here are several aspects to consider when performing distributed tracing of AWS Lambda functions:

Context propagation: The tracing context should be propagated between different parts of the application, including across different services and components. This allows the tracing data to be associated with the correct request and response.
Sampling: Sampling is the process of selectively tracing a subset of requests and responses, rather than tracing all of them. This can help reduce the overhead of tracing and improve the performance of the application.
Tracing library: A tracing library should be used to instrument the code of the Lambda function. This library should provide an easy-to-use API that allows developers to add tracing to their code with minimal effort.
Trace ID: Trace ID should be unique and consistent across different services and components of the application. This allows trace data to be correlated across different services and components, making it easier to troubleshoot issues.
Error handling: Tracing should include error handling and proper error reporting. This can help developers to identify and troubleshoot issues in the application.
Performance monitoring: Performance monitoring should be part of the tracing system, this can help developers to identify bottlenecks in the application and take action to improve the performance.

Instrumentation

It is also important to consider the type of instrumentation, which means adding code to an application to provide insight into its execution:

Programmatic instrumentation requires more effort from the developer and can add complexity to the codebase, but it can also provide more flexibility and control.
Automatic instrumentation, on the other hand, is generally easier to use and requires less developer effort.

Monitoring AWS Lambda with Dash0

Dash0 is an OpenTelemetry-native observability platform. The Dash0 Lambda Extension ships as a Lambda layer and captures traces, logs, and metrics from Python, Node.js, and Java functions without code changes. It also records the failures that standard OpenTelemetry instrumentation cannot see:

Timeouts and out-of-memory errors: The extension creates synthetic traces for invocations that AWS terminates before your handler can emit a span.
Cold starts: Every invocation records whether it was a cold start, and the initialization duration is available as a span attribute and as a metric.
Trigger context: Traces include the invocation ID, the event source, and trigger details for SQS, DynamoDB Streams, Kinesis, SNS, and EventBridge.
Log correlation: Logs are linked to the trace of the invocation that wrote them.

Functions that already use the OpenTelemetry SDK can send their telemetry to Dash0 directly. Because the data follows OpenTelemetry semantic conventions, Lambda spans connect to the rest of your traces.

Start a free trial of Dash0, or read how to set up AWS Lambda monitoring.