Instrumenting Ruby code in AWS Lambda - AWS Lambda

Instrumenting Ruby code in AWS Lambda

Lambda integrates with AWS X-Ray to enable you to trace, debug, and optimize Lambda applications. You can use X-Ray to trace a request as it traverses resources in your application, from the frontend API to storage and database on the backend. By simply adding the X-Ray SDK library to your build configuration, you can record errors and latency for any call that your function makes to an AWS service.

The X-Ray service map shows the flow of requests through your application. The following example from the error processor sample application shows an application with two functions. The primary function processes events and sometimes returns errors. The second function processes errors that appear in the first's log group and uses the AWS SDK to call X-Ray, Amazon S3 and Amazon CloudWatch Logs.


          Service map showing nodes for Lambda functions, X-Ray, Amazon S3 and CloudWatch Logs

To trace requests that don't have a tracing header, enable active tracing in your function's configuration.

To enable active tracing

  1. Open the Lambda console Functions page.

  2. Choose a function.

  3. Under AWS X-Ray, choose Active tracing.

  4. Choose Save.

Pricing

X-Ray has a perpetual free tier. Beyond the free tier threshold, X-Ray charges for trace storage and retrieval. For details, see AWS X-Ray pricing.

Your function needs permission to upload trace data to X-Ray. When you enable active tracing in the Lambda console, Lambda adds the required permissions to your function's execution role. Otherwise, add the AWSXRayDaemonWriteAccess policy to the execution role.

X-Ray applies a sampling algorithm to ensure that tracing is efficient, while still providing a representative sample of the requests that your application serves. The default sampling rule is 1 request per second and 5 percent of additional requests.

When active tracing is enabled, Lambda records a trace for a subset of invocations. Lambda records two segments, which creates two nodes on the service map. The first node represents the Lambda service that receives the invocation request. The second node is recorded by the function's runtime.


      An X-Ray service map with a single function.

You can instrument your handler code to record metadata and trace downstream calls. To record detail about calls that your handler makes to other resources and services, use the X-Ray SDK for Ruby. To get the SDK, add the aws-xray-sdk package to your application's dependencies.

Example blank-ruby/function/Gemfile

# Gemfile source 'https://rubygems.org' gem 'aws-xray-sdk', '0.11.4' gem 'aws-sdk-lambda', '1.39.0' gem 'test-unit', '3.3.5'

To instrument AWS SDK clients, require the aws-xray-sdk/lambda module after creating a client in initialization code.

Example blank-ruby/function/lambda_function.rb – Tracing an AWS SDK client

# lambda_function.rb require 'logger' require 'json' require 'aws-sdk-lambda' $client = Aws::Lambda::Client.new() $client.get_account_settings() require 'aws-xray-sdk/lambda' def lambda_handler(event:, context:) logger = Logger.new($stdout) ...

The following example shows a trace with 2 segments. Both are named my-function, but one is type AWS::Lambda and the other is AWS::Lambda::Function. The function segment is expanded to show its subsegments.

The first segment represents the invocation request processed by the Lambda service. The second segment records the work done by your function. The function segment has 3 subsegments.

  • Initialization – Represents time spent loading your function and running initialization code. This subsegment only appears for the first event processed by each instance of your function.

  • Invocation – Represents the work done by your handler code. By instrumenting your code, you can extend this subsegment with additional subsegments.

  • Overhead – Represents the work done by the Lambda runtime to prepare to handle the next event.

You can also instrument HTTP clients, record SQL queries, and create custom subsegments with annotations and metadata. For more information, see The X-Ray SDK for Ruby in the AWS X-Ray Developer Guide.

Enabling active tracing with the Lambda API

To manage tracing configuration with the AWS CLI or AWS SDK, use the following API operations:

The following example AWS CLI command enables active tracing on a function named my-function.

$ aws lambda update-function-configuration --function-name my-function \ --tracing-config Mode=Active

Tracing mode is part of the version-specific configuration that is locked when you publish a version of your function. You can't change the tracing mode on a published version.

Enabling active tracing with AWS CloudFormation

To enable active tracing on an AWS::Lambda::Function resource in an AWS CloudFormation template, use the TracingConfig property.

Example function-inline.yml – Tracing configuration

Resources: function: Type: AWS::Lambda::Function Properties: TracingConfig: Mode: Active ...

For an AWS Serverless Application Model (AWS SAM) AWS::Serverless::Function resource, use the Tracing property.

Example template.yml – Tracing configuration

Resources: function: Type: AWS::Serverless::Function Properties: Tracing: Active ...

Storing runtime dependencies in a layer

If you use the X-Ray SDK to instrument AWS SDK clients your function code, your deployment package can become quite large. To avoid uploading runtime dependencies every time you update your functions code, package them in a Lambda layer.

The following example shows an AWS::Serverless::LayerVersion resource that stores X-Ray SDK for Ruby.

Example template.yml – Dependencies layer

Resources: function: Type: AWS::Serverless::Function Properties: CodeUri: function/. Tracing: Active Layers: - !Ref libs ... libs: Type: AWS::Serverless::LayerVersion Properties: LayerName: blank-ruby-lib Description: Dependencies for the blank-ruby sample app. ContentUri: lib/. CompatibleRuntimes: - ruby2.5

With this configuration, you only update library layer if you change your runtime dependencies. The function deployment package only contains your code. When you update your function code, upload time is much faster than if you include dependencies in the deployment package.

Creating a layer for dependencies requires build changes to generate the layer archive prior to deployment. For a working example, see the blank-ruby sample application.