A Python logging handler for Fluentd event collector
.. image:: https://travis-ci.org/fluent/fluent-logger-python.svg?branch=master :target: https://travis-ci.org/fluent/fluent-logger-python :alt: Build Status
.. image:: https://coveralls.io/repos/fluent/fluent-logger-python/badge.svg :target: https://coveralls.io/r/fluent/fluent-logger-python :alt: Coverage Status
Many web/mobile applications generate huge amount of event logs (c,f. login, logout, purchase, follow, etc). To analyze these event logs could be really valuable for improving the service. However, the challenge is collecting these logs easily and reliably.
Fluentd <https://github.com/fluent/fluentd>__ solves that problem by
having: easy installation, small footprint, plugins, reliable buffering,
log forwarding, etc.
fluent-logger-python is a Python library, to record the events from Python application.
msgpackThis library is distributed as 'fluent-logger' python package. Please execute the following command to install it.
.. code:: sh
$ pip install fluent-logger
Fluentd daemon must be launched with a tcp source configuration:
::
<source>
type forward
port 24224
</source>
To quickly test your setup, add a matcher that logs to the stdout:
::
<match app.**>
type stdout
</match>
FluentSender Interface
`sender.FluentSender` is a structured event logger for Fluentd.
By default, the logger assumes fluentd daemon is launched locally. You
can also specify remote logger by passing the options.
.. code:: python
from fluent import sender
# for local fluent
logger = sender.FluentSender('app')
# for remote fluent
logger = sender.FluentSender('app', host='host', port=24224)
For sending event, call `emit` method with your event. Following example will send the event to
fluentd, with tag 'app.follow' and the attributes 'from' and 'to'.
.. code:: python
# Use current time
logger.emit('follow', {'from': 'userA', 'to': 'userB'})
# Specify optional time
cur_time = int(time.time())
logger.emit_with_time('follow', cur_time, {'from': 'userA', 'to':'userB'})
To send events with nanosecond-precision timestamps (Fluent 0.14 and up),
specify `nanosecond_precision` on `FluentSender`.
.. code:: python
# Use nanosecond
logger = sender.FluentSender('app', nanosecond_precision=True)
logger.emit('follow', {'from': 'userA', 'to': 'userB'})
logger.emit_with_time('follow', time.time(), {'from': 'userA', 'to': 'userB'})
You can detect an error via return value of `emit`. If an error happens in `emit`, `emit` returns `False` and get an error object using `last_error` method.
.. code:: python
if not logger.emit('follow', {'from': 'userA', 'to': 'userB'}):
print(logger.last_error)
logger.clear_last_error() # clear stored error after handled errors
If you want to shutdown the client, call `close()` method.
.. code:: python
logger.close()
Event-Based Interface
~~~~~~~~~~~~~~~~~~~~~
This API is a wrapper for `sender.FluentSender`.
First, you need to call ``sender.setup()`` to create global `sender.FluentSender` logger
instance. This call needs to be called only once, at the beginning of
the application for example.
Initialization code of Event-Based API is below:
.. code:: python
from fluent import sender
# for local fluent
sender.setup('app')
# for remote fluent
sender.setup('app', host='host', port=24224)
Then, please create the events like this. This will send the event to
fluentd, with tag 'app.follow' and the attributes 'from' and 'to'.
.. code:: python
from fluent import event
# send event to fluentd, with 'app.follow' tag
event.Event('follow', {
'from': 'userA',
'to': 'userB'
})
`event.Event` has one limitation which can't return success/failure result.
Other methods for Event-Based Interface.
.. code:: python
sender.get_global_sender # get instance of global sender
sender.close # Call FluentSender#close
Handler for buffer overflow
You can inject your own custom proc to handle buffer overflow in the event of connection failure. This will mitigate the loss of data instead of simply throwing data away.
.. code:: python
import msgpack
from io import BytesIO
def overflow_handler(pendings):
unpacker = msgpack.Unpacker(BytesIO(pendings))
for unpacked in unpacker:
print(unpacked)
logger = sender.FluentSender('app', host='host', port=24224, buffer_overflow_handler=overflow_handler)
You should handle any exception in handler. fluent-logger ignores exceptions from buffer_overflow_handler.
This handler is also called when pending events exist during close().
Python logging.Handler interface
This client-library also has ``FluentHandler`` class for Python logging
module.
.. code:: python
import logging
from fluent import handler
custom_format = {
'host': '%(hostname)s',
'where': '%(module)s.%(funcName)s',
'type': '%(levelname)s',
'stack_trace': '%(exc_text)s'
}
logging.basicConfig(level=logging.INFO)
l = logging.getLogger('fluent.test')
h = handler.FluentHandler('app.follow', host='host', port=24224, buffer_overflow_handler=overflow_handler)
formatter = handler.FluentRecordFormatter(custom_format)
h.setFormatter(formatter)
l.addHandler(h)
l.info({
'from': 'userA',
'to': 'userB'
})
l.info('{"from": "userC", "to": "userD"}')
l.info("This log entry will be logged with the additional key: 'message'.")
You can also customize formatter via logging.config.dictConfig
.. code:: python
import logging.config
import yaml
with open('logging.yaml') as fd:
conf = yaml.load(fd)
logging.config.dictConfig(conf['logging'])
You can inject your own custom proc to handle buffer overflow in the event of connection failure. This will mitigate the loss of data instead of simply throwing data away.
.. code:: python
import msgpack
from io import BytesIO
def overflow_handler(pendings):
unpacker = msgpack.Unpacker(BytesIO(pendings))
for unpacked in unpacker:
print(unpacked)
A sample configuration ``logging.yaml`` would be:
.. code:: python
logging:
version: 1
formatters:
brief:
format: '%(message)s'
default:
format: '%(asctime)s %(levelname)-8s %(name)-15s %(message)s'
datefmt: '%Y-%m-%d %H:%M:%S'
fluent_fmt:
'()': fluent.handler.FluentRecordFormatter
format:
level: '%(levelname)s'
hostname: '%(hostname)s'
where: '%(module)s.%(funcName)s'
handlers:
console:
class : logging.StreamHandler
level: DEBUG
formatter: default
stream: ext://sys.stdout
fluent:
class: fluent.handler.FluentHandler
host: localhost
port: 24224
tag: test.logging
buffer_overflow_handler: overflow_handler
formatter: fluent_fmt
level: DEBUG
none:
class: logging.NullHandler
loggers:
amqp:
handlers: [none]
propagate: False
conf:
handlers: [none]
propagate: False
'': # root logger
handlers: [console, fluent]
level: DEBUG
propagate: False
Asynchronous Communication
~~~~~~~~~~~~~~~~~~~~~~~~~~
Besides the regular interfaces - the event-based one provided by ``sender.FluentSender`` and the python logging one
provided by ``handler.FluentHandler`` - there are also corresponding asynchronous versions in ``asyncsender`` and
``asynchandler`` respectively. These versions use a separate thread to handle the communication with the remote fluentd
server. In this way the client of the library won't be blocked during the logging of the events, and won't risk going
into timeout if the fluentd server becomes unreachable. Also it won't be slowed down by the network overhead.
The interfaces in ``asyncsender`` and ``asynchandler`` are exactly the same as those in ``sender`` and ``handler``, so it's
just a matter of importing from a different module.
For instance, for the event-based interface:
.. code:: python
from fluent import asyncsender as sender
# for local fluent
sender.setup('app')
# for remote fluent
sender.setup('app', host='host', port=24224)
# do your work
...
# IMPORTANT: before program termination, close the sender
sender.close()
or for the python logging interface:
.. code:: python
import logging
from fluent import asynchandler as handler
custom_format = {
'host': '%(hostname)s',
'where': '%(module)s.%(funcName)s',
'type': '%(levelname)s',
'stack_trace': '%(exc_text)s'
}
logging.basicConfig(level=logging.INFO)
l = logging.getLogger('fluent.test')
h = handler.FluentHandler('app.follow', host='host', port=24224, buffer_overflow_handler=overflow_handler)
formatter = handler.FluentRecordFormatter(custom_format)
h.setFormatter(formatter)
l.addHandler(h)
l.info({
'from': 'userA',
'to': 'userB'
})
l.info('{"from": "userC", "to": "userD"}')
l.info("This log entry will be logged with the additional key: 'message'.")
...
# IMPORTANT: before program termination, close the handler
h.close()
**NOTE**: please note that it's important to close the sender or the handler at program termination. This will make
sure the communication thread terminates and it's joined correctly. Otherwise the program won't exit, waiting for
the thread, unless forcibly killed.
Circular queue mode
+++++++++++++++++++
In some applications it can be especially important to guarantee that the logging process won't block under *any*
circumstance, even when it's logging faster than the sending thread could handle (*backpressure*). In this case it's
possible to enable the `circular queue` mode, by passing `True` in the `queue_circular` parameter of
``asyncsender.FluentSender`` or ``asynchandler.FluentHandler``. By doing so the thread doing the logging won't block
even when the queue is full, the new event will be added to the queue by discarding the oldest one.
**WARNING**: setting `queue_circular` to `True` will cause loss of events if the queue fills up completely! Make sure
that this doesn't happen, or it's acceptable for your application.
Testing
-------
Testing can be done using
`nose <https://nose.readthedocs.org/en/latest/>`__.
Release
-------
Need wheel package.
.. code:: sh
$ pip install wheel
After that, type following command:
.. code:: sh
$ python setup.py clean sdist bdist_wheel upload
Contributors
------------
Patches contributed by `those
people <https://github.com/fluent/fluent-logger-python/contributors>`__.
License
-------
Apache License, Version 2.0