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 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.
- Python 2.6 or greater including 3.x
msgpack-python
This library is distributed as 'fluent-logger' python package. Please execute the following command to install it.
$ pip install fluent-loggerFluentd 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>
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.
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'.
# 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.
# 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.
if not logger.emit('follow', {'from': 'userA', 'to': 'userB'}):
print(logger.last_error)
logger.clear_last_error() # clear stored error after handled errorsIf you want to shutdown the client, call close() method.
logger.close()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:
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'.
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.
sender.get_global_sender # get instance of global sender
sender.close # Call FluentSender#closeYou 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.
import msgpack
from io import BytesIO
def handler(pendings):
unpacker = msgpack.Unpacker(BytesIO(pendings))
for unpacked in unpacker:
print(unpacked)
logger = sender.FluentSender('app', host='host', port=24224, buffer_overflow_handler=handler)You should handle any exception in handler. fluent-logger ignores exceptions from buffer_ove
940B
rflow_handler.
This handler is also called when pending events exist during close().
This client-library also has FluentHandler class for Python logging
module.
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=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
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.
import msgpack
from io import BytesIO
def handler(pendings):
unpacker = msgpack.Unpacker(BytesIO(pendings))
for unpacked in unpacker:
print(unpacked)A sample configuration logging.yaml would be:
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: 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: FalseTesting can be done using nose.
Need wheel package.
$ pip install wheelAfter that, type following command:
$ python setup.py clean sdist bdist_wheel uploadPatches contributed by those people.
Apache License, Version 2.0