Logs in context for the Python agent connects your logs and APM data in New Relic, giving full context to high-level events, as well as providing high value data to specific log lines. Read on to learn how to configure logs in context and enrich your log data.
Compatibility and requirements
To use log monitoring with the Python agent, ensure your configuration meets the following requirements:
Configure logs in context with log management
To configure New Relic logs in context with Python:
- Enable log management with a compatible log forwarding plugin.
- Install or update the Python agent.
- Configure the Python logging framework to use the NewRelicContextFormatter.
- Check for logging data.
Enable log management
Install or update the Python agent
Enabling logs in Python is as simple as instantiating a log formatter and adding it to your log handler. This example uses a
StreamHandler which by default writes logs to
sys.stderr, but any handler can be used. See the Python docs for information about configuring log handlers.
# Import the logging module and the New Relic log formatter import logging from newrelic.agent import NewRelicContextFormatter # Instantiate a new log handler handler = logging.StreamHandler() # Instantiate the log formatter and add it to the log handler formatter = NewRelicContextFormatter() handler.setFormatter(formatter) # Get the root logger and add the handler to it root_logger = logging.getLogger() root_logger.addHandler(handler)
Check for logging data
To verify that you have configured the extension correctly, run your application and verify that the logging you have configured contains the following:
- Is properly-formatted JSON lines
Now that you've set up APM logs in context, here are some potential next steps:
- Explore your data using the Logs UI.
- Troubleshoot errors with distributed tracing, stack traces, application logs, and more.
- Query your data and create custom dashboards or alerts.