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Standard library logging

To correlate your traces with your logs, complete the following steps:

  1. Activate automatic instrumentation.
  2. Include required attributes from the log record.

Step 1 - Activate automatic instrumentation

Activate automatic instrumentation using one of the following options:

Option 1: Library Injection:

  1. Set the environment variable DD_LOGS_INJECTION=true in the application deployment/manifest file.
  2. Follow the instructions in Library Injection to set up tracing.

Option 2: ddtrace-run:

  1. Set the environment variable DD_LOGS_INJECTION=true in the environment where the application is running.
  2. Import ddtrace into the application.
  3. Run the application with ddtrace-run (for example, ddtrace-run python appname.py).

Option 3: patch:

  1. Import ddtrace into the application.
  2. Add ddtrace.patch(logging=True) to the start of the application code.

Step 2 - Include required attributes

Update your log format to include the required attributes from the log record.

Include the dd.env, dd.service, dd.version, dd.trace_id and dd.span_id attributes for your log record in the format string.

Here is an example using logging.basicConfig to configure the log injection:

import logging
from ddtrace import tracer

FORMAT = ('%(asctime)s %(levelname)s [%(name)s] [%(filename)s:%(lineno)d] '
          '[dd.service=%(dd.service)s dd.env=%(dd.env)s dd.version=%(dd.version)s dd.trace_id=%(dd.trace_id)s dd.span_id=%(dd.span_id)s] '
          '- %(message)s')
log = logging.getLogger(__name__)
log.level = logging.INFO

def hello():
    log.info('Hello, World!')


To learn more about logs injection, read the ddtrace documentation.

No standard library logging

If you are not using the standard library logging module, you can use the following code snippet to inject tracer information into your logs:

from ddtrace import tracer

span = tracer.current_span()
correlation_ids = (str((1 << 64) - 1 & span.trace_id), span.span_id) if span else (None, None)

As an illustration of this approach, the following example defines a function as a processor in structlog to add tracer fields to the log output:

import ddtrace
from ddtrace import tracer

import structlog

def tracer_injection(logger, log_method, event_dict):
    # get correlation ids from current tracer context
    span = tracer.current_span()
    trace_id, span_id = (str((1 << 64) - 1 & span.trace_id), span.span_id) if span else (None, None)

    # add ids to structlog event dictionary
    event_dict['dd.trace_id'] = str(trace_id or 0)
    event_dict['dd.span_id'] = str(span_id or 0)

    # add the env, service, and version configured for the tracer
    event_dict['dd.env'] = ddtrace.config.env or ""
    event_dict['dd.service'] = ddtrace.config.service or ""
    event_dict['dd.version'] = ddtrace.config.version or ""

    return event_dict

log = structlog.get_logger()

Once the logger is configured, executing a traced function that logs an event yields the injected tracer information:

>>> traced_func()
{"event": "In tracer context", "dd.trace_id": 9982398928418628468, "dd.span_id": 10130028953923355146, "dd.env": "dev", "dd.service": "hello", "dd.version": "abc123"}

Note: If you are not using a Datadog Log Integration to parse your logs, custom log parsing rules need to ensure that dd.trace_id and dd.span_id are being parsed as strings and remapped using the Trace Remapper. For more information, see Correlated Logs Not Showing Up in the Trace ID Panel.

See the Python logging documentation to ensure that the Python Log Integration is properly configured so that your Python logs are automatically parsed.

Further Reading