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Manual Python Tracing

Learn how to set up highlight.io tracing for your Python application.
1
Setup your frontend Highlight snippet with tracingOrigins.

Make sure that you followed the fullstack mapping guide.

H.init("<YOUR_PROJECT_ID>", { tracingOrigins: ['localhost', 'example.myapp.com/backend'], networkRecording: { enabled: true, recordHeadersAndBody: true, }, });
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2
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
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# or with pip pip install highlight-io
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3
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", )
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4
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()
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5
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()
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6
Verify your backend traces are being recorded.

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