Logging helpers.
Project description
logkit-py
Logging helpers.
Installation
poetry add outcome-logkit
Usage
logkit
is a wrapper around structlog that configures it with the following:
- Sets log level based on
APP_ENV
environment variable - Automatically outputs Stackdriver-compliant JSON to stdout when running in a GCP environment (AppEngine, CloudRun, GKE, etc.)
- Intercepts all messages sent to the standard library loggers and processes them transparently
- Configures structlog to provide async-safe context values
Initialization
logkit
needs to be initialized before being used. This initialization configures structlog
and sets up the intercept for the standard logging library.
Note It's important to do this as early as possible in the program to ensure that no other imports start logging messages before the intercept has been configured. You can use # isort:skip
to ensure isort
doesn't reorder the import.
# Important that this happens before any other imports
from outcome.logkit import init_logging # isort:skip
init_logging() # isort:skip
Log Level
You can provide a level
parameter to init_logging
to define the default log-level. You can use the built-in log levels from the logging
module (e.g. logging.INFO
). If you don't provide a level, it will automatically be set based on the env.is_prod()
method from the outcome-utils package.
import logging
init_logging(level=logging.INFO)
Custom Processors
You can provide an array of your own structlog processors to init_logging
. They will be merged into the processors provided by logkit
.
init_logging(processors=[my_custom_processor])
Logging
To log with logkit
, you can either use the standard library logging, or use the structlog interface. Both can be used to pass structured data to the log entries. Using the structlog interface is marginally faster, since all the messages sent to the standard logging library are sent to structlog anyway.
import logging
from outcome.logkit import get_logger
# Using the standard library
logger = logging.getLogger(__name__)
logger.info('my_message', user_id='1')
# Using the structlog interface
structured_logger = get_logger(__name__)
structured_logger.info('my_message', user_id='1')
Async-safe context vars
You can set "global" variables that are async safe using outcome.logkit.context
.
import logging
from outcome.logkit import get_logger, context
context.add(user_id='1')
structured_logger = get_logger(__name__)
structured_logger.info('my_message') # user_id=1 will be added to this log event
context.remove('user_id')
Testing
If you want to capture logs during your tests, you can use configure_structlog
and log_output
fixtures.
@pytest.mark.usefixtures('configure_structlog')
def test_log_output(log_ouput):
assert log_output.entries == []
# do something
assert log_output.entries == [...]
You can also define the captured log level
or add custom processors
thanks to these handy fixtures:
@pytest.fixture
def log_level():
return logging.DEBUG
@pytest.fixture
def log_processors(log_output):
my_custom_processor = foo
return [my_custom_processor, log_output]
@pytest.mark.usefixtures('configure_structlog')
def test_log_output(log_ouput):
assert log_output.entries == []
# do something
assert log_output.entries == [...]
Development
Remember to run ./bootstrap.sh
when you clone the repository.
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