Connecting Python Logs and Traces
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Connecting Python Logs and Traces

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Automatically Inject Trace and Span IDs

Enable injection with the environment variable DD_LOGS_INJECTION=true when using ddtrace-run. If you have configured your tracer with DD_ENV, DD_SERVICE, and DD_VERSION, then env, service, and version will also be added automatically. Learn more about unified service tagging.

Note: The standard library logging is supported for auto-injection. Any libraries, such as json_log_formatter, that extend the standard library module are also supported for auto-injection. ddtrace-run calls logging.basicConfig before executing your application. If the root logger has a handler configured, your application must modify the root logger and handler directly.

Manually Inject Trace and Span IDs

With Standard Library Logging

If you prefer to manually correlate your traces with your logs, patch your logging module by updating your log formatter to include the dd.trace_id and dd.span_id attributes from the log record.

Similarly, include dd.env, dd.service, and dd.version as attributes for your log record.

The configuration below is used by the automatic injection method and is supported by default in the Python Log Integration:

from ddtrace import patch_all; patch_all(logging=True)
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')
logging.basicConfig(format=FORMAT)
log = logging.getLogger(__name__)
log.level = logging.INFO

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

hello()

Without Standard Library Logging

If you are not using the standard library logging module, you can use the ddtrace.helpers.get_correlation_ids() to inject tracer information into your logs. 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.helpers import get_correlation_ids

import structlog

def tracer_injection(logger, log_method, event_dict):
    # get correlation ids from current tracer context
    trace_id, span_id = get_correlation_ids()

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

structlog.configure(
    processors=[
        tracer_injection,
        structlog.processors.JSONRenderer()
    ]
)
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, "span_id": 10130028953923355146, "env": "dev", "service": "hello", "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. More information can be found in the FAQ on this topic.

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