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Python logging library to emit JSON log that can be easily indexed and searchable by logging infrastructure such as ELK (Elasticsearch, Logstash, and Kibana)
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README.md

json-logging

Python logging library to emit JSON log that can be easily indexed and searchable by logging infrastructure such as ELK, EFK

If you're using Cloud Foundry, it worth to check out the library SAP/cf-python-logging-support which I'm also original author.

Content

  1. Features
  2. Usage
    2.1 Non-web application log
    2.2 Web application log
    2.3 Get current correlation-id
    2.4 Log extra properties
    2.5 Root logger
    2.6 Custom log formatter
  3. Configuration
  4. Python References
  5. Framework support plugin development
  6. FAQ & Troubleshooting
  7. References

1. Features

  1. Emit JSON logs (format detail)
  2. Auto extract correlation-id for distributed tracing [1]
  3. Lightweight, no dependencies, minimal configuration needed (1 LoC to get it working)
  4. Fully compatible with Python logging module. Support both Python 2.7.x and 3.x
  5. Support HTTP request instrumentation. Built in support for Flask, Sanic, Quart, Connexion. Extensible to support other web frameworks. PR welcome 😃 .
  6. Support inject arbitrary extra properties to JSON log message.

2. Usage

Install by running this command:

pip install json-logging

By default log will be emitted in normal format to ease the local development. To enable it on production set either json_logging.ENABLE_JSON_LOGGING or ENABLE_JSON_LOGGING environment variable to true.

To configure, call json_logging.init_< framework_name >(). Once configured library will try to configure all loggers (existing and newly created) to emit log in JSON format.
See following use cases for more detail.

TODO: update guide on how to use ELK stack to view log

2.1 Non-web application log

This mode don't support correlation-id.

import json_logging, logging, sys

# log is initialized without a web framework name
json_logging.ENABLE_JSON_LOGGING = True
json_logging.init_non_web()

logger = logging.getLogger("test-logger")
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler(sys.stdout))

logger.info("test logging statement")

2.2 Web application log

Flask

import datetime, logging, sys, json_logging, flask

app = flask.Flask(__name__)
json_logging.ENABLE_JSON_LOGGING = True
json_logging.init_flask()
json_logging.init_request_instrument(app)

# init the logger as usual
logger = logging.getLogger("test-logger")
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler(sys.stdout))

@app.route('/')
def home():
    logger.info("test log statement")
    return "Hello world : " + str(datetime.datetime.now())

if __name__ == "__main__":
    app.run(host='0.0.0.0', port=int(5000), use_reloader=False)

Sanic

import logging, sys, json_logging, sanic

app = sanic.Sanic()
json_logging.ENABLE_JSON_LOGGING = True
json_logging.init_sanic()
json_logging.init_request_instrument(app)

# init the logger as usual
logger = logging.getLogger("sanic-integration-test-app")
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler(sys.stdout))

@app.route("/")
async def home(request):
    logger.info("test log statement")
    return sanic.response.text("hello world")

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8000)

Quart

import asyncio, logging, sys, json_logging, quart

app = quart.Quart(__name__)
json_logging.ENABLE_JSON_LOGGING = True
json_logging.init_quart()
json_logging.init_request_instrument(app)

# init the logger as usual
logger = logging.getLogger("test logger")
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler(sys.stdout))

@app.route('/')
async def home():
    logger.info("test log statement")
    logger.info("test log statement", extra={'props': {"extra_property": 'extra_value'}})
    return "Hello world"

if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    app.run(host='0.0.0.0', port=int(5000), use_reloader=False, loop=loop)

Connexion

import datetime, logging, sys, json_logging, connexion

app = connexion.FlaskApp(__name__)
json_logging.ENABLE_JSON_LOGGING = True
json_logging.init_connexion()
json_logging.init_request_instrument(app)

app.add_api('api.yaml')

# init the logger as usual
logger = logging.getLogger("test-logger")
logger.setLevel(logging.DEBUG)
logger.addHandler(logging.StreamHandler(sys.stdout))

if __name__ == "__main__":
    app.run()

2.3 Get current correlation-id

Current request correlation-id can be retrieved and pass to downstream services call as follow:

correlation_id = json_logging.get_correlation_id()
# use correlation id for downstream service calls here

In request context, if one is not present, a new one might be generated depends on CREATE_CORRELATION_ID_IF_NOT_EXISTS setting value.

2.4 Log extra properties

Extra property can be added to logging statement as follow:

logger.info("test log statement", extra = {'props' : {'extra_property' : 'extra_value'}})

2.5 Root logger

If you want to use root logger as main logger to emit log. Made sure you call config_root_logger() after initialize root logger (by logging.basicConfig() or logging.getLogger('root')) [2]

logging.basicConfig()
json_logging.config_root_logger()

2.6 Custom log formatter

Customer JSON log formatter can be passed to init method. see example for more detail: https://github.com/thangbn/json-logging-python/blob/master/example/custom_log_format.py

3. Configuration

logging library can be configured by setting the value in json_logging, all configuration must be placed before json_logging.init method call

Name Description Default value
ENABLE_JSON_LOGGING Whether to enable JSON logging mode.Can be set as an environment variable, enable when set to to either one in following list (case-insensitive) ['true', '1', 'y', 'yes'] , this have no effect on request logger false
ENABLE_JSON_LOGGING_DEBUG Whether to enable debug logging for this library for development purpose. true
CORRELATION_ID_HEADERS List of HTTP headers that will be used to look for correlation-id value. HTTP headers will be searched one by one according to list order ['X-Correlation-ID','X-Request-ID']
EMPTY_VALUE Default value when a logging record property is None '-'
CORRELATION_ID_GENERATOR function to generate unique correlation-id uuid.uuid1
JSON_SERIALIZER function to encode object to JSON json.dumps
COMPONENT_ID Uniquely identifies the software component that has processed the current request EMPTY_VALUE
COMPONENT_NAME A human-friendly name representing the software component EMPTY_VALUE
COMPONENT_INSTANCE_INDEX Instance's index of horizontally scaled service 0
CREATE_CORRELATION_ID_IF_NOT_EXISTS Whether to generate a new correlation-id in case one is not present True

4. Python References

TODO: update Python API docs on Github page

5. Framework support plugin development

To add support for a new web framework, you need to extend following classes in framework_base and register support using json_logging.register_framework_support method:

Class Description Mandatory
RequestAdapter Helper class help to extract logging-relevant information from HTTP request object no
ResponseAdapter Helper class help to extract logging-relevant information from HTTP response object yes
FrameworkConfigurator Class to perform logging configuration for given framework as needed no
AppRequestInstrumentationConfigurator Class to perform request instrumentation logging configuration no

Take a look at json_logging/base_framework.py, json_logging.flask and json_logging.sanic packages for reference implementations.

6. FAQ & Troubleshooting

  1. I configured everything, but no logs are printed out?

    • Forgot to add handlers to your logger?
    • Check whether logger is disabled.
  2. Same log statement is printed out multiple times.

    • Check whether the same handler is added to both parent and child loggers [2]
    • If you using flask, by default option use_reloader is set to True which will start 2 instances of web application. change it to False to disable this behaviour [3]
  3. Can not install Sanic on Windows?

you can install Sanic on windows by running these commands:

git clone --branch 0.7.0 https://github.com/channelcat/sanic.git
set SANIC_NO_UVLOOP=true
set SANIC_NO_UJSON=true
pip3 install .

7. References

[0] Full logging format references

2 types of logging statement will be emitted by this library:

  • Application log: normal logging statement e.g.:
{
	"type": "log",
	"written_at": "2017-12-23T16:55:37.280Z",
	"written_ts": 1514048137280721000,
	"component_id": "1d930c0xd-19-s3213",
	"component_name": "ny-component_name",
	"component_instance": 0,
	"logger": "test logger",
	"thread": "MainThread",
	"level": "INFO",
	"line_no": 22,
	"filename": "/path/to/foo.py"
	"exc_info": "Traceback (most recent call last): \n  File "<stdin>", line 1, in <module>\n ValueError: There is something wrong with your input",
	"correlation_id": "1975a02e-e802-11e7-8971-28b2bd90b19a",
	"extra_property": "extra_value",
	"msg": "This is a message"
}
  • Request log: request instrumentation logging statement which recorded request information such as response time, request size, etc.
{
	"type": "request",
	"written_at": "2017-12-23T16:55:37.280Z",
	"written_ts": 1514048137280721000,
	"component_id": "-",
	"component_name": "-",
	"component_instance": 0,
	"correlation_id": "1975a02e-e802-11e7-8971-28b2bd90b19a",
	"remote_user": "user_a",
	"request": "/index.html",
	"referer": "-",
	"x_forwarded_for": "-",
	"protocol": "HTTP/1.1",
	"method": "GET",
	"remote_ip": "127.0.0.1",
	"request_size_b": 1234,
	"remote_host": "127.0.0.1",
	"remote_port": 50160,
	"request_received_at": "2017-12-23T16:55:37.280Z",
	"response_time_ms": 0,
	"response_status": 200,
	"response_size_b": "122",
	"response_content_type": "text/html; charset=utf-8",
	"response_sent_at": "2017-12-23T16:55:37.280Z"
}

See following tables for detail format explanation:

  • Common field
Field Description Format Example
written_at The date when this log message was written. ISO 8601 YYYY-MM-DDTHH:MM:SS.milliZ 2017-12-23T15:14:02.208Z
written_ts The timestamp in nano-second precision when this request metric message was written. long number 1456820553816849408
correlation_id The timestamp in nano-second precision when this request metric message was written. string db2d002e-2702-41ec-66f5-c002a80a3d3f
type Type of logging. "logs" or "request" string
component_id Uniquely identifies the software component that has processed the current request string 9e6f3ecf-def0-4baf-8fac-9339e61d5645
component_name A human-friendly name representing the software component string my-fancy-component
component_instance Instance's index of horizontally scaled service string 0
  • application logs
Field Description Format Example
msg The actual message string passed to the logger. string This is a log message
level The log "level" indicating the severity of the log message. string INFO
thread Identifies the execution thread in which this log message has been written. string http-nio-4655
logger The logger name that emits the log message. string requests-logger
filename The file name where an exception originated string /path/to/foo.py
exc_info Traceback information about an exception string "Traceback (most recent call last): \n File "", line 1, in \n ValueError: There is something wrong with your input"
  • request logs:
Field Description Format Example
request request path that has been processed. string /get/api/v2
request_received_at The date when an incoming request was received by the producer. ISO 8601 YYYY-MM-DDTHH:MM:SS.milliZ The precision is in milliseconds. The timezone is UTC. 2015-01-24 14:06:05.071Z
response_sent_at The date when the response to an incoming request was sent to the consumer. ditto 2015-01-24 14:06:05.071Z
response_time_ms How many milliseconds it took the producer to prepare the response. float 43.476
protocol Which protocol was used to issue a request to a producer. In most cases, this will be HTTP (including a version specifier), but for outgoing requests reported by a producer, it may contain other values. E.g. a database call via JDBC may report, e.g. "JDBC/1.2" string HTTP/1.1
method The corresponding protocol method. string GET
remote_ip IP address of the consumer (might be a proxy, might be the actual client) string 192.168.0.1
remote_host host name of the consumer (might be a proxy, might be the actual client) string my.happy.host
remote_port Which TCP port is used by the consumer to establish a connection to the remote producer. string 1234
remote_user The username associated with the request string user_name
request_size_b The size in bytes of the requesting entity or "body" (e.g., in case of POST requests). long 1234
response_size_b The size in bytes of the response entity long 1234
response_status The status code of the response. long 200
response_content_type The MIME type associated with the entity of the response if available/specified long application/json
referer For HTTP requests, identifies the address of the webpage (i.e. the URI or IRI) that linked to the resource being requested. string /index.html
x_forwarded_for Comma-separated list of IP addresses, the left-most being the original client, followed by proxy server addresses that forwarded the client request. string 192.0.2.60,10.12.9.23

[1] What is correlation-id/request id

https://stackoverflow.com/questions/25433258/what-is-the-x-request-id-http-header

[2] Python logging propagate

https://docs.python.org/3/library/logging.html#logging.Logger.propagate https://docs.python.org/2/library/logging.html#logging.Logger.propagate

[3] more on flask use_reloader

http://flask.pocoo.org/docs/0.12/errorhandling/#working-with-debuggers

Development

create file .pypirc

[distutils]
index-servers =
  pypi
  pypitest

[pypi]
repository: https://upload.pypi.org/legacy/
username:
password:

[pypitest]
repository: https://test.pypi.org/legacy/
username=
password=

build

python setup.py bdist_wheel --universal

pypitest

twine upload --repository-url https://test.pypi.org/legacy/ dist/*
pip3 install json_logging --index-url https://test.pypi.org/simple/

pypi

twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
pip3 install json_logging
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