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.

import highlight_io

# `instrument_logging=True` sets up logging instrumentation.
# if you do not want to send logs or are using `loguru`, pass `instrument_logging=False`
H = highlight_io.H(
	"<YOUR_PROJECT_ID>",
	instrument_logging=True,
	service_name="my-app",
	service_version="git-sha",
	environment="production",
)

Run your code with the H.trace() wrapper.

Wrap your code with H.trace(), and run your code.

import logging

def main():
    with H.trace(span_name="my_span"):
        logging.info('hello, world!', {'favorite_number': 7})
        return f"<h1>Hello world</h1>"

if __name__ == "__main__":
    main()

Use a decorator to trace your functions.

Use the highlight_io.trace() decorator to create spans for your functions.

import logging

@highlight_io.trace
def my_cool_method():
    logging.info("hello my_cool_method", {"customer": "unknown", "trace": "inside"})
    time.sleep(random.randint(0, 10) / 1000)
    logging.info("goodbye my_cool_method", {"customer": "unknown", "trace": "inside"})

def main():
    with H.trace(span_name="my_span"):
        logging.info('hello, world!', {'favorite_number': 7})
        my_cool_method()
        return f"<h1>Hello world</h1>"

if __name__ == "__main__":
    main()

Verify your backend traces are being recorded.

Visit the highlight traces portal and check that backend traces are coming in.

Tracing in Python AWS Lambda