AWS Lambda

H.init("<YOUR_PROJECT_ID>", {
  tracingOrigins: ['localhost', 'example.myapp.com/backend'],
  networkRecording: {
    enabled: true,
    recordHeadersAndBody: true,
  },
});

Install the highlight-io python package.

Download the package from pypi and save it to your requirements. If you use a zip or s3 file upload to publish your function, you will want to make sure highlight-io is part of the build.

poetry add highlight-io
# or with pip
pip install highlight-io

Initialize the Highlight SDK.

Setup the SDK. Add the @observe_handler decorator to your lambdas.

import highlight_io
from highlight_io.integrations.aws import observe_handler

H = highlight_io.H("1", record_logs=True)


@observe_handler
def lambda_handler(event, context):
    return {
        "statusCode": 200,
        "body": f"Hello, {name}. This HTTP triggered function executed successfully.",
    }

Verify your installation.

Check that your installation is valid by throwing an error. Add an operation that raises an exception to your lambda handler. Setup an HTTP trigger and visit your lambda on the internet. You should see a DivideByZero error in the Highlight errors page within a few moments.

import highlight_io
from highlight_io.integrations.aws import observe_handler

H = highlight_io.H("1", record_logs=True)


@observe_handler
def lambda_handler(event, context):
    return {
        "body": f"Returning this is a bad idea: {5 / 0}.",
    }

Set up logging.

With the Python SDK, your logs are reported automatically from builtin logging methods. See the Python logging setup guide for more details.

Error Monitoring in Python Azure Functions