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📄 COPYING Copyright (C) 2011 FURUHASHI Sadayuki Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.	591 B	2025-05-25 12:47
📄 INSTALLER pip	4 B	2025-05-25 12:47
📄 METADATA Metadata-Version: 2.1 Name: fluent-logger Version: 0.9.6 Summary: A Python logging handler for Fluentd event collector Home-page: https://github.com/fluent/fluent-logger-python Author: Kazuki Ohta Author-email: kazuki.ohta@gmail.com License: Apache License, Version 2.0 Download-URL: http://pypi.python.org/pypi/fluent-logger/ Platform: UNKNOWN Classifier: Programming Language :: Python :: 2 Classifier: Programming Language :: Python :: 2.7 Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.4 Classifier: Programming Language :: Python :: 3.5 Classifier: Programming Language :: Python :: 3.6 Classifier: Programming Language :: Python :: 3.7 Classifier: Programming Language :: Python :: Implementation :: CPython Classifier: Programming Language :: Python :: Implementation :: PyPy Classifier: Development Status :: 5 - Production/Stable Classifier: Topic :: System :: Logging Classifier: Intended Audience :: Developers Requires-Python: >=2.7,!=3.0,!=3.1,!=3.2,!=3.3 Requires-Dist: msgpack (<1.0.0) A Python structured logger for Fluentd ====================================== .. 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. Requirements ------------ - Python 2.7 or 3.4+ - ``msgpack-python`` - IMPORTANT: Version 0.8.0 is the last version supporting Python 2.6, 3.2 and 3.3 Installation ------------ This library is distributed as 'fluent-logger' python package. Please execute the following command to install it. .. code:: sh $ pip install fluent-logger Configuration ------------- 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> Usage ----- 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	12.25 KB	2025-05-25 12:47
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📄 REQUESTED	0 B	2025-05-25 12:47
📄 WHEEL Wheel-Version: 1.0 Generator: bdist_wheel (0.34.2) Root-Is-Purelib: true Tag: py2-none-any Tag: py3-none-any	110 B	2025-05-25 12:47
📄 top_level.txt fluent	7 B	2025-05-25 12:47

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