Correlating Python Logs and Traces
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Injection
Standard library logging
To correlate your traces with your logs, complete the following steps:
- Activate automatic instrumentation.
- 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:
- Set the environment variable
DD_LOGS_INJECTION=true
in the application deployment/manifest
file. - Follow the instructions in Library Injection to set up tracing.
Option 2: ddtrace-run
:
- Set the environment variable
DD_LOGS_INJECTION=true
in the environment where the application is running. - Import ddtrace into the application.
- Run the application with
ddtrace-run
(for example, ddtrace-run python appname.py
).
Option 3: patch
:
- Import ddtrace into the application.
- 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')
logging.basicConfig(format=FORMAT)
log = logging.getLogger(__name__)
log.level = logging.INFO
@tracer.wrap()
def hello():
log.info('Hello, World!')
hello()
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
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, "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
Documentation, liens et articles supplémentaires utiles: