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Description:

loggingjson 0.5.0

JSON formatter for logging

This module provides a JSON formatter for the python logging module that will format to JSON formatted string.
Using this formatter allows to have the proper format for logging to Splunk or ElasticSearch, but it can also be used for logging to stdout as a string is issued.

Features

Custom fields
dict logging
str logging

Configuration

Using dictConfig

Features
Adding additional fields and values
You can add fields to every message that is being logged.
To do so, specify the fields parameter to the logging_json.JSONFormatter instance.
It must be a dictionary where keys are the keys to be appended to the resulting JSON dictionary (if not already present) and the values can be one of the following:

An attribute of the logging record (non-exhaustive list can be found on the python logging documentation).
If not found on the record, the value will be linked to the key.

Logging exceptions, a specific case
If an exception is logged, the exception key will be appended to the resulting JSON dictionary.
This dictionary will contain 3 keys:

type: The name of the exception class (useful when the message is blank).
message: The str representation of the exception (usually the provided error message).
stack: The stack trace, formatted as a string.

You can rename the exception field key by setting the exception_field_name parameter with a new name for the key.
It is also possible to disable this behaviour by setting the exception_field_name parameter to None or an empty string
Logging with a dictionary
This formatter allows you to log dictionary as in the following:
import logging

logging.info({"key": "value", "other key": "other value"})

The resulting JSON dictionary will be the one you provided (with the additional fields).
Logging with anything else (such as a string)
Anything not logged using a dictionary will be handled by the standard formatter, and it can result in one of the 2 output:

A JSON dictionary, if additional fields are set or if extra parameter is used while logging, with the message available in the message key of the resulting JSON dictionary.
Default message key name can be changed by message_field_name parameter of the logging_json.JSONFormatter instance.
The formatted record, if no additional fields are set.

This handles the usual string logging as in the following:
import logging

logging.info("This is my message")

Changing asctime representation
You can override the default representation of asctime (2003-07-08 16:49:45,896) based on two different scenarii:
Without milliseconds
Set datefmt parameter.
Setting datefmt to %Y-%m-%dT%H:%M:%S would result in 2003-07-08T16:49:45.
With milliseconds
Set default_time_format to something else than %Y-%m-%d %H:%M:%S to change the representation part without milliseconds.
Set default_msec_format to something else than %s,%03d to change the representation milliseconds.
Note that %s in default_msec_format is going to be replaced by the representation without milliseconds.
Setting default_time_format to %Y-%m-%dT%H:%M:%S and default_msec_format to %s.%03d would result in 2003-07-08T16:49:45.896.
Configuration
You can create a formatter instance yourself as in the following, or you can use a logging configuration.
import logging_json

formatter = logging_json.JSONFormatter(fields={
"level_name": "levelname",
"thread_name": "threadName",
"process_name": "processName"
})

Using logging.config.dictConfig
You can configure your logging as advertise by python, by using the logging.config.dictConfig function.
dict configuration
import logging.config

logging.config.dictConfig({
"version": 1,
"formatters": {
"json": {
'()': 'logging_json.JSONFormatter',
'fields':{
"level_name": "levelname",
"thread_name": "threadName",
"process_name": "processName"
}
}
},
"handlers": {
"standard_output": {
'class': 'logging.StreamHandler',
'formatter': 'json',
'stream': 'ext://sys.stdout'
},
},
"loggers": {
"my_app": {"level": "DEBUG"}
},
"root": {
"level": "INFO",
"handlers": ["standard_output"]
}
})

YAML logging configuration
You can use YAML to store your logging configuration, as in the following sample:
import logging.config
import yaml

with open("path/to/logging_configuration.yaml", "r") as config_file:
logging.config.dictConfig(yaml.load(config_file))

Where logging_configuration.yaml can be a file containing the following sample:
version: 1
formatters:
json:
'()': logging_json.JSONFormatter
fields:
level_name: levelname
thread_name: threadName
process_name: processName
handlers:
standard_output:
class: logging.StreamHandler
formatter: json
stream: ext://sys.stdout
loggers:
my_app:
level: DEBUG
root:
level: INFO
handlers: [standard_output]

How to install

python 3.7+ must be installed
Use pip to install module:

python -m pip install logging_json