Format your python logs as JSON objects, perfect for easy ingestion into centralised logging systems.
Installation is pretty simple with pip:
> pip install jog
Depending on your system, you might need to use pip3 to install for Python 3 (ditto for any other pip commands):
> pip3 install jog
Once installed, import the JogFormatter and configure the logging system to use it. e.g.:
import logging
from jog import JogFormatter
log_handler = logging.StreamHandler()
log_format = '[%(asctime)s] %(name)s.%(levelname)s %(threadName)s %(module)s.%(funcName)s %(filename)s:%(lineno)s %(message)s'
log_handler.setFormatter(JogFormatter(log_format))
root_logger = logging.getLogger()
root_logger.setLevel(logging.INFO)
root_logger.addHandler(log_handler)
Then log as normal:
logging.info('foo')
logging.info('foo %s', 'bar')
logging.info('foo %(baz)s', {'baz': 'bar'})
logging.info('foo %(baz)s', {'baz': 'bar'}, extra={'ekki': 'ptang'})
Or log an arbitrary dict:
logging.info({'foo': 'bar'})
Here's an example of a simple log and its output:
logging.info('foo')
{
"@timestamp": "2017-05-30T11:25:51.168769+00:00",
"@version": 1,
"filename": "test.py",
"funcName": "<module>",
"levelname": "INFO",
"levelno": 20,
"lineno": 56,
"log": "[2017-05-30 23:25:51,168] __main__.INFO MainThread test.<module> test.py:56 foo",
"message": "foo",
"message_format": "foo",
"module": "test",
"name": "__main__",
"pathname": "test.py",
"process": 7476,
"processName": "MainProcess",
"relativeCreated": 7.751226425170898,
"thread": 139963963160320,
"threadName": "MainThread"
}
Note that the output has been pretty-printed for ease of examination - each log Json object is actually output on a single line.
The Json objects produced are primarily a conversion of LogRecords into dicts, which are then rendered as Json. See the official docs for details on fields included in a LogRecord. Note that the logging system will merge a extra dict into the LogRecord before it is passed to JogFormatter, so extra fields are treated as part of the LogRecord.
After the conversion of LogRecord to dict a number of transformations are applied.
A msg string is formatted using the args (if provided), and the result is put in the message field. The unformatted msg is put in message_format, and the args in message_args. An unstructured "traditional" log is also created using the fmt used to create the JogFormatter, and put in the log field:
logging.info('foo %s', 'bar')
{
...
"message": "foo bar",
"message_format": "foo %s",
"message_args": ["bar"],
"log": "[2017-05-30 22:03:48,217] __main__.INFO MainThread test.<module> test.py:45 foo bar",
...
}
If keyword formatting is used, the arguments dict is put in message_kwargs instead:
logging.info('foo %(baz)s', {'baz': 'bar'})
{
...
"message": "foo bar",
"message_format": "foo %(baz)s",
"message_kwargs": {"baz": "bar"},
"log": "[2017-05-30 22:03:48,217] __main__.INFO MainThread test.<module> test.py:45 foo bar",
...
}
A msg dict is merged straight into the log dict. Note that this will override any existing fields from the LogRecord (including fields from extra) with the same name.
logging.info({'foo': 'bar'})
{
...
"foo": "bar",
...
}
No formatting is done, so attempting to provide args along with a dict msg will result in a logging error.
logging.info({'foo': 'bar'}, 'baz') # results in a logging error
If a LogRecord contains a exc_info, the exception is rendered and put into the exc_text field.
try:
raise Exception('Foo!')
except Exception:
logging.exception('bar')
{
...
"exc_text": "Traceback (most recent call last):\n File \"test.py\", line 51, in <module>\n raise Exception('Foo!')\nException: Foo!",
...
}
JogFormatter takes a keyword argument fn, which is a function take takes a log dict (produced as described above) and returns the final dict to be rendered as Json, allowing arbitrary changes to be made before the log is written out.
By default, the provided jog.elk.format_log function is used. This function makes a number of changes to make the final Json suitable for ingestion into the ELK centralised logging system.
The @version field is set to 1, and the created timestamp field is removed, converted to ISO format, and put in @timestamp. asctime and msecs are removed as they are not needed with @timestamp set. exc_info is also removed, as no special formatting is done with it, and exc_text is available.
Finally, the dict is cleaned to avoid (statelessly detectable) mapping conflicts in Elasticsearch. Specifically, if a list or tuple's contents are not homogenously a basic Json type (None, str, int|float, bool), the (non None) contents are converted to strings.
log.info('foo %(baz)s', {'baz': ['bar', 0, None]})
{
...
"@version": 1,
"@timestamp": "2017-05-30T11:34:31.901264+00:00",
"message": "foo ['bar', 0, None]",
"message_kwargs": {"baz": ["bar", "0", null]},
...
}
The final log dict is rendered as Json using the standard json library. Unencodable objects are automatically converted to strings to avoid losing logs when they are logged by mistake.
To install directly from the git repo, run the following in the root project directory:
> pip install .
The library can be installed in "editable" mode, using pip's -e flag. This allows you to test out changes without having to re-install.
> pip install -e .
Send me a PR if you have a change you want to contribute!