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AWS Lambda Instrumentation

For serverless apps based on Lambda and similar platforms, observability can be challenging. With no server to log into, clunky logging interfaces, and short, event-driven process runtimes, making sense of what’s going on is often difficult.

To help with this, Honeycomb has an extension that can run as a Lambda Layer, or inside a container alongside your lambda function. You can also instrument your application code with our NodeJS, Python, or Go SDKs.

Honeycomb Lambda Extension  🔗

The Honeycomb Lambda Extension is designed to run alongside your lambda function. It integrates with the Lambda Logs API and receives log messages from your lambda function, which are then sent to Honeycomb as events. Structured log messages sent to stdout or stderr from your lambda function will be sent to Honeycomb as events.

The extension can be run inside a container or added as a Lambda Layer.

How It Works  🔗

AWS Lambda Extensions allow you to extend the functionality of Lambda functions through configuration as Lambda Layers or run inside a container image.

Extensions run alongside the Lambda Runtime and can read data environment variables from the environment.

Once the Honeycomb Lambda Extension is configured, any structured logs emitted to stdout or stderr by your Lambda function will be parsed by the extension and sent to a dataset that you specify in Honeycomb.

Installing as a Lambda Layer  🔗

To start using the Honeycomb Lambda Extension, add the extension to your function as a Lambda Layer.

The extension can be added as a Lambda Layer with the AWS CLI tool:

$ aws lambda update-function-configuration --function-name YourFunctionName \
    --layers "arn:aws:lambda:<AWS_REGION>:702835727665:layer:honeycomb-lambda-extension:8"

Replace AWS_REGION with the appropriate value (for example, us-east-1).

You can also use infrastructure-as-code tools such as Terraform:

resource "aws_lambda_function" "extensions-demo-example-lambda" {
  function_name = "HoneycombExtensionExample"
  ...
  layers = ["arn:aws:lambda:<AWS_REGION>:702835727665:layer:honeycomb-lambda-extension:8"]
}

Additionally, you will need to add the following environment variables to your Lambda function configuration:

• LIBHONEY_DATASET - The Honeycomb dataset you would like events to be sent to.
• LIBHONEY_API_KEY - Your Honeycomb API Key (also called Write Key).

As well as the following optional environment variable:

• LIBHONEY_API_HOST - Mostly used for testing purposes, or to be compatible with proxies. Defaults to https://api.honeycomb.io/.
• LOGS_API_DISABLE_PLATFORM_MSGS - Optional. Set to “true” in order to disable “platform” messages from the logs API.
• HONEYCOMB_DEBUG - Optional. Set to “true” to enable debug statements and troubleshoot issues. Enabling this will subscribe to Libhoney’s response queue and log the success or failure of sending events to Honeycomb.

Installing in a Container Image  🔗

AWS Lambda functions can now be packaged and deployed as container images. This allows developers to leverage the flexibility and familiarity of container tooling, workflows and dependencies.

To run the Honeycomb Lambda Extension inside your container image, you must download the extension and place it in the /opt/extensions directory of your image.

FROM amazon/aws-lambda-ruby:2.7

ARG FUNCTION_DIR="/var/task"

RUN mkdir -p /opt/extensions
RUN yum install -y curl
RUN curl -L https://honeycomb.io/download/honeycomb-lambda-extension/v9.0.0/honeycomb-lambda-extension -o /opt/extensions/honeycomb-lambda-extension
RUN chmod +x /opt/extensions/honeycomb-lambda-extension

RUN mkdir -p ${FUNCTION_DIR}
COPY . ${FUNCTION_DIR}
ENV LIBHONEY_API_KEY <HONEYCOMB_API_KEY>
ENV LIBHONEY_DATASET <HONEYCOMB_DATASET>

CMD ["app.handler"]

AWS Lambda provides a number of base images you can use. You can also use a custom base image, and adjust the examples accordingly.

Performance Implications  🔗

The Honeycomb Lambda Extension runs independently of your Lambda Function and does not add to your Lambda Function’s execution time or add any additional latency. However, it does increase your Lambda Function’s size in proportion with the size of the Honeycomb Lambda Extension binary.

Examples  🔗

Below are some structured logging examples using some libraries we are familiar with at Honeycomb. Feel free to use your own!

Go  🔗

import (
  log "github.com/sirupsen/logrus"
)

log.SetFormatter(&log.JSONFormatter{})

func Handler(ctx context.Context) error {
  // Measure execution time
  startTime := time.Now()

  // ...

  // Get the Lambda context object
  lc, _ := lambdacontext.FromContext(ctx)

  log.WithFields(log.Fields{
    "function_name": lambdacontext.FunctionName,
    "function_version": lambdacontext.FunctionVersion,
    "request_id": lc.AwsRequestID,
    "duration_ms": time.Since(startTime).Milliseconds(),
    // other fields of interest
  }).Info("Hello World from Lambda!")
}

Python  🔗

import structlog
structlog.configure(processors=[structlog.processors.JSONRenderer()])
log = structlog.get_logger()

def handler(event, context):
    # measure execution time
    start_time = datetime.datetime.now()

    # ...

    log.msg(
        "Hello World from Lambda!",
        function_name=context.function_name,
        function_version=context.function_version,
        request_id=context.aws_request_id,
        duration_ms=(datetime.datetime.now() - start_time).total_seconds() * 1000,
        # other fields of interest
    )

JavaScript  🔗

var bunyan = require('bunyan');
var log = bunyan.createLogger();


module.exports.handler = (event, context, callback) => {
  // Measure execution time
  let startTime = Date.now();

  // ...

  log.info({
    functionName: context.functionName,
    functionVersion: context.functionVersion,
    requestId: context.awsRequestId,
    // Example fields - send anything that seems relevant!
    userId: event.UserId,
    userAction: event.UserAction,
    latencyMs: Date.now() - startTime,
  },
  'Hello World from Lambda!');
}