logging-utilities 0.1.0a2

Python logging utilities

Python logging utilities

This package implements some usefull logging utilities. Here below are the main features of the package:

• JSON formatter
• Flask request context record attributes
• ISO Time in format YYYY-MM-DDThh:mm:ss.sss±hh:mm
• Add constant record attributes
• Logger Level Filter

All features can be fully configured from the configuration file.

NOTE: only python 3 is supported

Table of content

• Installation
• Contribution
• JSON Formatter
• Flask Request Context
• ISO Time with Timezone
• Constant Record Attribute
• Logger Level Filter
• Basic Usage
  • Case 1. Simple JSON Output
  • Case 2. JSON Output Configured within Python Code
  • Case 3. JSON Output Configured with a YAML File
  • Case 4. Add Flask Request Context Attributes to JSON Output
• Credits

Installation

logging_utilities is available on PyPI.

Use pip to install:

pip install logging-utilities

Contribution

TODO

JSON Formatter

JsonFormatter is a python logging formatter that transform the log output into a json object.

JSON log format is quite usefull especially when the logs are sent to LogStash.

This formatter supports embedded object as well as array.

Configure JSON Format

The format can be configured either using the format config parameter or the fmt constructor parameter. This parameter should be a dictionary (for Python version below 3.7, it is better to use OrderedDict to keep the attribute order). Each key is taken as such as key for the output JSON object, while each value is transformed as follow in the output:

Value	Type	Transformation	Example
attribute	string	The string is a LogRecord attribute name, then the value of this attribute is used as output.	"message"
str format	string	The string contains named string format, each named format are replaced by the corresponding LogRecord attribute value.	"%(asctime)s.%(msecs)s"
object	dict	The object is embedded in the output with its value following the same rules as defined in this table.	{"lineno": "lineno", "file": "filename"}
array	list	The list is embedded as an array in the output. Each value is processed using the rules from this table	["created", "asctime"]

You can find the LogRecord attributes list in Python Doc

JSON Formatter Options

You can change some behavior using the JsonFormatter constructor:

Parameter	Type	Default	Description
fmt	dict	None	Define the output format, see Configure JSON Format
datefmt	string	None	Date format for asctime, see time.strftime()
style	string	%	String formatting style, see logging.Formatter
add_always_extra	bool	False	When True, logging extra (logging.log('message', extra={'my-extra': 'some value'})) are always added to the output. Otherwise they are only added if present in fmt.
filter_attributes	list	None	When the formatter is used with a Logging.Filter that adds LogRecord attributes, they can be listed here to avoid to be treated as logging extra.
remove_empty	bool	False	When True, empty values (empty list, dict, None or empty string) are removed from output.

The constructor parameters can be also be specified in the log configuration file using the () class specifier instead of class:

formatters:
  json:
    (): logging_utilities.formatters.json_formatter.JsonFormatter
    add_always_extra: True
    fmt:
      time: asctime
      level: levelname
      logger: name
      module: module
      message: message

Flask Request Context

When using logging within a Flask application, you can use this Filter to add some context attributes to all LogRecord.

All Flask Request attributes are supported and they are added as LogRecord with the flask_request_ prefix.

Flask Request Context Filter Constructor

Parameter	Type	Default	Description
attributes	list	None	List of Flask Request attributes name to add to the LogRecord

Flask Request Context Config Example

filters:
  flask:
    (): logging_utilities.filters.flask_attribute.FlaskRequestAttribute
    attributes:
      - url
      - method
      - headers
      - remote_addr
      - json

NOTE: FlaskRequestAttribute only support the special key '()' factory in the configuration file (it doesn't work with the normal 'class' key).

ISO Time with Timezone

The standard logging doesn't support the time as ISO with timezone; YYYY-MM-DDThh:mm:ss.sss±hh:mm. By default asctime uses a ISO like format; YYYY-MM-DD hh:mm:ss.sss, but without T separator (although this one could be configured by overriding a global variable, this can't be done by config file). You can use the datefmt option to specify another date format, however this one don't supports milliseconds, so you could achieve this format: YYYY-MM-DDThh:mm:ss±hh:mm.

This Filter can be used to achieve the full ISO 8601 Time format including timezone and milliseconds.

ISO Time Filter Constructor

Parameter	Type	Default	Description
isotime	bool	True	Add log local time as isotime attribute to LogRecord with the YYYY-MM-DDThh:mm:ss.sss±hh:mm format.
utc_isotime	bool	False	Add log UTC time as utc_isotime attribute to LogRecord with the YYYY-MM-DDThh:mm:ss.sss±hh:mm format.

ISO Time Config Example

filters:
  isotime:
    (): logging_utilities.filters.TimeAttribute
    utc_isotime: True
    isotime: False

NOTE: TimeAttribute only support the special key '()' factory in the configuration file (it doesn't work with the normal 'class' key).

Constant Record Attribute

Simple logging Filter to add constant attribute to every LogRecord

Constant Record Attribute Config Example

filters:
  application:
    (): logging_utilities.filters.ConstAttribute
    application: my-application

NOTE: ConstAttribute only support the special key '()' factory in the configuration file (it doesn't work with the normal 'class' key).

Logger Level Filter

Sometimes you might want to have different log Level based on the logger and handler. The standard logging library allow to set a logger level or a handler level but not based on both. Let say you have a config with two loggers logging to two handlers, on the first handler you want all messages of both loggers and on the second handler you want all messages of the first logger but only the WARNING messages of the second logger. This is here were this filter come into play.

Logger Level Filter Constructor

Parameter	Type	Default	Description
level	int | string	'DEBUG'	All messages with a lower level than this one will be filtered out.
logger	string	''	When non empty, only message from this logger will be fitlered out based on their level.

Logger Level Filter Config Example

root:
  handlers:
    - "console"
    - "file"
  level: "DEBUG"
  propagate: "True"

filters:
  B_filter:
    (): logging_utilities.filters.LevelFilter
    level: "WARNING"
    logger: 'B'

loggers:
  A:
    level: "DEBUG"
  B:
    level: "DEBUG"

handlers:
  console:
    class: "logging.StreamHandler"

  file:
    class: "logging.handlers.RotatingFileHandler"
    filters:
      - "B_filter"

NOTE: LevelFilter only support the special key '()' factory in the configuration file (it doesn't work with the normal 'class' key).

Basic Usage

Case 1. Simple JSON Output

import logging

from logging_utilites.formatters.json_formatter import basic_config

# default keyword parameter `format`: """{"levelname": "levelname", "name": "name", "message": "message"}"""
basic_config(level=logging.INFO)
logging.info('hello, json_formatter')

output:

{"levelname": "INFO", "name": "root", "message": "hello, json_formatter"}

Case 2. JSON Output Configured within Python Code

import logging

from logging_utilites.formatters.json_formatter import JsonFormatter

# `FORMAT` can be `json`, `OrderedDict` or `dict`.
# If `FORMAT` is `dict` and python version < 3.7.0, the output order is sorted by keys, otherwise it will be the same
# as the defined order.
#
# KEY := string, can be whatever you like.
# VALUE := `LogRecord` attribute name, string, formatted string (e.g. "%(asctime)s.%(msecs)s"), list or dict
FORMAT = {
    "Name":            "name",
    "Levelno":         "levelno",
    "Levelname":       "levelname",
    "Pathname":        "pathname",
    "Filename":        "filename",
    "Module":          "module",
    "Lineno":          "lineno",
    "FuncName":        "funcName",
    "Created":         "created",
    "Asctime":         "asctime",
    "Msecs":           "msecs",
    "RelativeCreated": "relativeCreated",
    "Thread":          "thread",
    "ThreadName":      "threadName",
    "Process":         "process",
    "Message":         "message"
}

root = logging.getLogger()
root.setLevel(logging.INFO)

formatter = JsonFormatter(FORMAT)

sh = logging.StreamHandler()
sh.setFormatter(formatter)
sh.setLevel(logging.INFO)

root.addHandler(sh)

def test():
  root.info("test %s format", 'string')

test()

output:

{"Name": "root", "Levelno": 20, "Levelname": "INFO", "Pathname": "test.py", "Filename": "test.py", "Module": "test", "Lineno": 75, "FuncName": "test", "Created": 1588185267.3198836, "Asctime": "2020-04-30 02:34:27,319", "Msecs": 319.8835849761963, "RelativeCreated": 88.2880687713623, "Thread": 16468, "ThreadName": "MainThread", "Process": 16828, "Message": "test string format"}

Case 3. JSON Output Configured with a YAML File

config.yaml:

version: 1

root:
  handlers:
    - console
  level: DEBUG
  propagate: True

formatters:
  json:
    class: logging_utilities.formatters.json_formatter.JsonFormatter
    format:
      time: asctime
      level: levelname
      logger: name
      module: module
      function: funcName
      process: process
      thread: thread
      message: message

handlers:
  console:
    class: logging.StreamHandler
    formatter: json
    stream: ext://sys.stdout

Then in your python code use it as follow:

import logging
import logging.config

import yaml


config = {}
with open('example-config.yaml', 'r') as fd:
    config = yaml.safe_load(fd.read())

logging.config.dictConfig(config)

root = logging.getLogger()
root.info('Test file config')

output:

{"function": "<module>", "level": "INFO", "logger": "root", "message": "Test file config", "module": "<stdin>", "process": 12264, "thread": 139815989413696, "time": "asctime"}

Case 4. Add Flask Request Context Attributes to JSON Output

config.yaml

version: 1

root:
  handlers:
    - console
  level: DEBUG
  propagate: True

filters:
  isotime:
    (): logging_utilities.filters.TimeAttribute
  flask:
    (): logging_utilities.filters.flask_attribute.FlaskRequestAttribute
    attributes:
      - url
      - method
      - headers
      - remote_addr
      - json

formatters:
  json:
    class: logging_utilities.formatters.json_formatter.JsonFormatter
    format:
      time: isotime
      level: levelname
      logger: name
      module: module
      function: funcName
      process: process
      thread: thread
      request:
        url: flask_request_url
        method: flask_request_method
        headers: flask_request_headers
        data: flask_request_json
        remote: flask_request_remote_addr
      message: message

handlers:
  console:
    class: logging.StreamHandler
    formatter: json
    stream: ext://sys.stdout
    filters:
      - isotime
      - flask

NOTE: This require to have flask package installed otherwise it raises ImportError

Then in your python code use it as follow:

import logging
import logging.config

import yaml


config = {}
with open('example-config.yaml', 'r') as fd:
    config = yaml.safe_load(fd.read())

logging.config.dictConfig(config)

root = logging.getLogger()
root.info('Test file config')

output:

{"function": "<module>", "level": "INFO", "logger": "root", "message": "Test file config", "module": "<stdin>", "process": 24190, "request": {"url": "", "method": "", "headers": "", "data": "", "remote": ""}, "thread": 140163374577472, "time": "isotime"}

Credits

The JSON Formatter implementation has been inspired by MyColorfulDays/jsonformatter