日志操作手册¶
- 作者
Vinay Sajip <vinay_sajip at red-dove dot com>
本页包含了许多日志记录相关的概念，这些概念在过去一直被认为很有用。
在多个模块中记录日志¶
多次调用``logging.getLogger(‘someLogger’)``会返回对同一个日志记录器对象的引用。不仅在同一个模块中是这样的，而且在不同模块之间，只要是在同一个Python解释器进程中，也是如此。 这就是对同一个对象的多个引用；此外，应用程序代码也可以在一个模块中定义和配置父日志记录器，在单独的模块中创建（但不配置）一个子日志记录器，并且对子日志记录器的所有调用都将传递给父日志记录器。 这里是一个主要模块:
import logging
import auxiliary_module
# create logger with 'spam_application'
logger = logging.getLogger('spam_application')
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler('spam.log')
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
logger.info('creating an instance of auxiliary_module.Auxiliary')
a = auxiliary_module.Auxiliary()
logger.info('created an instance of auxiliary_module.Auxiliary')
logger.info('calling auxiliary_module.Auxiliary.do_something')
a.do_something()
logger.info('finished auxiliary_module.Auxiliary.do_something')
logger.info('calling auxiliary_module.some_function()')
auxiliary_module.some_function()
logger.info('done with auxiliary_module.some_function()')
这里是辅助模块:
import logging
# create logger
module_logger = logging.getLogger('spam_application.auxiliary')
class Auxiliary:
def __init__(self):
self.logger = logging.getLogger('spam_application.auxiliary.Auxiliary')
self.logger.info('creating an instance of Auxiliary')
def do_something(self):
self.logger.info('doing something')
a = 1 + 1
self.logger.info('done doing something')
def some_function():
module_logger.info('received a call to "some_function"')
The output looks like this:
2005-03-23 23:47:11,663 - spam_application - INFO -
creating an instance of auxiliary_module.Auxiliary
2005-03-23 23:47:11,665 - spam_application.auxiliary.Auxiliary - INFO -
creating an instance of Auxiliary
2005-03-23 23:47:11,665 - spam_application - INFO -
created an instance of auxiliary_module.Auxiliary
2005-03-23 23:47:11,668 - spam_application - INFO -
calling auxiliary_module.Auxiliary.do_something
2005-03-23 23:47:11,668 - spam_application.auxiliary.Auxiliary - INFO -
doing something
2005-03-23 23:47:11,669 - spam_application.auxiliary.Auxiliary - INFO -
done doing something
2005-03-23 23:47:11,670 - spam_application - INFO -
finished auxiliary_module.Auxiliary.do_something
2005-03-23 23:47:11,671 - spam_application - INFO -
calling auxiliary_module.some_function()
2005-03-23 23:47:11,672 - spam_application.auxiliary - INFO -
received a call to 'some_function'
2005-03-23 23:47:11,673 - spam_application - INFO -
done with auxiliary_module.some_function()
在多个线程中记录日志¶
Logging from multiple threads requires no special effort. The following example shows logging from the main (initIal) thread and another thread:
import logging
import threading
import time
def worker(arg):
while not arg['stop']:
logging.debug('Hi from myfunc')
time.sleep(0.5)
def main():
logging.basicConfig(level=logging.DEBUG, format='%(relativeCreated)6d %(threadName)s %(message)s')
info = {'stop': False}
thread = threading.Thread(target=worker, args=(info,))
thread.start()
while True:
try:
logging.debug('Hello from main')
time.sleep(0.75)
except KeyboardInterrupt:
info['stop'] = True
break
thread.join()
if __name__ == '__main__':
main()
When run, the script should print something like the following:
0 Thread-1 Hi from myfunc
3 MainThread Hello from main
505 Thread-1 Hi from myfunc
755 MainThread Hello from main
1007 Thread-1 Hi from myfunc
1507 MainThread Hello from main
1508 Thread-1 Hi from myfunc
2010 Thread-1 Hi from myfunc
2258 MainThread Hello from main
2512 Thread-1 Hi from myfunc
3009 MainThread Hello from main
3013 Thread-1 Hi from myfunc
3515 Thread-1 Hi from myfunc
3761 MainThread Hello from main
4017 Thread-1 Hi from myfunc
4513 MainThread Hello from main
4518 Thread-1 Hi from myfunc
这表明不同线程的日志像期望的那样穿插输出，当然更多的线程也会像这样输出。
多个日志处理器以及多种格式化器¶
日志记录器是普通的Python对象。addHandler() 方法对可以添加的日志处理器的数量没有限制。有时候，应用程序需要将所有严重性的所有消息记录到一个文本文件，而将错误或更高等级的消息输出到控制台。要进行这样的设定，只需配置适当的日志处理器即可。在应用程序代码中，记录日志的调用将保持不变。以下是对之前基于模块的简单配置示例的略微修改:
import logging
logger = logging.getLogger('simple_example')
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler('spam.log')
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
fh.setFormatter(formatter)
# add the handlers to logger
logger.addHandler(ch)
logger.addHandler(fh)
# 'application' code
logger.debug('debug message')
logger.info('info message')
logger.warn('warn message')
logger.error('error message')
logger.critical('critical message')
需要注意的是，’应用程序’ 代码并不关心是否有多个日志处理器。所有的改变的只是添加和配置了一个新的名为*fh*的日志处理器。
在编写和测试应用程序时，能够创建带有更高或更低消息等级的过滤器的日志处理器是非常有用的。为了避免过多地使用 print 语句去调试，请使用 logger.debug ：它不像 print 语句需要你不得不在调试结束后注释或删除掉，logger.debug 语句可以在源代码中保持不变，在你再一次需要它之前保持无效。那时，唯一需要改变的是修改日志记录器和/或日志处理器的消息等级，以进行调试。
在多个地方记录日志¶
假设有这样一种情况，你需要将日志按不同的格式和不同的情况存储在控制台和文件中。比如说想把日志等级为DEBUG或更高的消息记录于文件中，而把那些等级为INFO或更高的消息输出在控制台。而且记录在文件中的消息格式需要包含时间戳，打印在控制台的不需要。以下示例展示了如何做到:
import logging
# set up logging to file - see previous section for more details
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename='/temp/myapp.log',
filemode='w')
# define a Handler which writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setLevel(logging.INFO)
# set a format which is simpler for console use
formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')
# tell the handler to use this format
console.setFormatter(formatter)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
# Now, we can log to the root logger, or any other logger. First the root...
logging.info('Jackdaws love my big sphinx of quartz.')
# Now, define a couple of other loggers which might represent areas in your
# application:
logger1 = logging.getLogger('myapp.area1')
logger2 = logging.getLogger('myapp.area2')
logger1.debug('Quick zephyrs blow, vexing daft Jim.')
logger1.info('How quickly daft jumping zebras vex.')
logger2.warning('Jail zesty vixen who grabbed pay from quack.')
logger2.error('The five boxing wizards jump quickly.')
When you run this, on the console you will see
root : INFO Jackdaws love my big sphinx of quartz.
myapp.area1 : INFO How quickly daft jumping zebras vex.
myapp.area2 : WARNING Jail zesty vixen who grabbed pay from quack.
myapp.area2 : ERROR The five boxing wizards jump quickly.
and in the file you will see something like
10-22 22:19 root INFO Jackdaws love my big sphinx of quartz.
10-22 22:19 myapp.area1 DEBUG Quick zephyrs blow, vexing daft Jim.
10-22 22:19 myapp.area1 INFO How quickly daft jumping zebras vex.
10-22 22:19 myapp.area2 WARNING Jail zesty vixen who grabbed pay from quack.
10-22 22:19 myapp.area2 ERROR The five boxing wizards jump quickly.
正如你所看到的，DEBUG级别的消息只展示在文件中，而其他消息两个地方都会输出。
这个示例只演示了在控制台和文件中去记录日志，但你也可以自由组合任意数量的日志处理器。
日志服务器配置示例¶
以下是在一个模块中使用日志服务器配置的示例:
import logging
import logging.config
import time
import os
# read initial config file
logging.config.fileConfig('logging.conf')
# create and start listener on port 9999
t = logging.config.listen(9999)
t.start()
logger = logging.getLogger('simpleExample')
try:
# loop through logging calls to see the difference
# new configurations make, until Ctrl+C is pressed
while True:
logger.debug('debug message')
logger.info('info message')
logger.warn('warn message')
logger.error('error message')
logger.critical('critical message')
time.sleep(5)
except KeyboardInterrupt:
# cleanup
logging.config.stopListening()
t.join()
然后如下的脚本，它接收文件名做为命令行参数，并将该文件以二进制编码的方式传给服务器，做为新的日志服务器配置:
#!/usr/bin/env python
import socket, sys, struct
with open(sys.argv[1], 'rb') as f:
data_to_send = f.read()
HOST = 'localhost'
PORT = 9999
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
print('connecting...')
s.connect((HOST, PORT))
print('sending config...')
s.send(struct.pack('>L', len(data_to_send)))
s.send(data_to_send)
s.close()
print('complete')
通过网络发送和接收日志¶
如果你想在网络上发送日志，并在接收端处理它们。一个简单的方式是通过附加一个 SocketHandler 的实例在发送端的根日志处理器中:
import logging, logging.handlers
rootLogger = logging.getLogger('')
rootLogger.setLevel(logging.DEBUG)
socketHandler = logging.handlers.SocketHandler('localhost',
logging.handlers.DEFAULT_TCP_LOGGING_PORT)
# don't bother with a formatter, since a socket handler sends the event as
# an unformatted pickle
rootLogger.addHandler(socketHandler)
# Now, we can log to the root logger, or any other logger. First the root...
logging.info('Jackdaws love my big sphinx of quartz.')
# Now, define a couple of other loggers which might represent areas in your
# application:
logger1 = logging.getLogger('myapp.area1')
logger2 = logging.getLogger('myapp.area2')
logger1.debug('Quick zephyrs blow, vexing daft Jim.')
logger1.info('How quickly daft jumping zebras vex.')
logger2.warning('Jail zesty vixen who grabbed pay from quack.')
logger2.error('The five boxing wizards jump quickly.')
At the receiving end, you can set up a receiver using the SocketServer
module. Here is a basic working example:
import pickle
import logging
import logging.handlers
import SocketServer
import struct
class LogRecordStreamHandler(SocketServer.StreamRequestHandler):
"""Handler for a streaming logging request.
This basically logs the record using whatever logging policy is
configured locally.
"""
def handle(self):
"""
Handle multiple requests - each expected to be a 4-byte length,
followed by the LogRecord in pickle format. Logs the record
according to whatever policy is configured locally.
"""
while True:
chunk = self.connection.recv(4)
if len(chunk) < 4:
break
slen = struct.unpack('>L', chunk)[0]
chunk = self.connection.recv(slen)
while len(chunk) < slen:
chunk = chunk + self.connection.recv(slen - len(chunk))
obj = self.unPickle(chunk)
record = logging.makeLogRecord(obj)
self.handleLogRecord(record)
def unPickle(self, data):
return pickle.loads(data)
def handleLogRecord(self, record):
# if a name is specified, we use the named logger rather than the one
# implied by the record.
if self.server.logname is not None:
name = self.server.logname
else:
name = record.name
logger = logging.getLogger(name)
# N.B. EVERY record gets logged. This is because Logger.handle
# is normally called AFTER logger-level filtering. If you want
# to do filtering, do it at the client end to save wasting
# cycles and network bandwidth!
logger.handle(record)
class LogRecordSocketReceiver(SocketServer.ThreadingTCPServer):
"""
Simple TCP socket-based logging receiver suitable for testing.
"""
allow_reuse_address = 1
def __init__(self, host='localhost',
port=logging.handlers.DEFAULT_TCP_LOGGING_PORT,
handler=LogRecordStreamHandler):
SocketServer.ThreadingTCPServer.__init__(self, (host, port), handler)
self.abort = 0
self.timeout = 1
self.logname = None
def serve_until_stopped(self):
import select
abort = 0
while not abort:
rd, wr, ex = select.select([self.socket.fileno()],
[], [],
self.timeout)
if rd:
self.handle_request()
abort = self.abort
def main():
logging.basicConfig(
format='%(relativeCreated)5d %(name)-15s %(levelname)-8s %(message)s')
tcpserver = LogRecordSocketReceiver()
print('About to start TCP server...')
tcpserver.serve_until_stopped()
if __name__ == '__main__':
main()
First run the server, and then the client. On the client side, nothing is printed on the console; on the server side, you should see something like:
About to start TCP server...
59 root INFO Jackdaws love my big sphinx of quartz.
59 myapp.area1 DEBUG Quick zephyrs blow, vexing daft Jim.
69 myapp.area1 INFO How quickly daft jumping zebras vex.
69 myapp.area2 WARNING Jail zesty vixen who grabbed pay from quack.
69 myapp.area2 ERROR The five boxing wizards jump quickly.
请注意，在某些情况下序列化会存在一些安全。如果这影响到你，那么你可以通过覆盖 makePickle() 方法，使用自己的实现来解决，并调整上述脚本也使用覆盖后的序列化方法。
在日志记录中添加上下文信息¶
有时，除了传递给日志记录器调用的参数外，我们还希望日志记录中包含上下文信息。例如，有一个网络应用，可能需要记录一些特殊的客户端信息在日志中（比如客户端的用户名、IP地址等）。虽然你可以通过设置额外的参数去达到这个目的，但这种方式不一定方便。或者你可能想到在每个连接的基础上创建一个 Logger 的实例，但这些实例是不会被垃圾回收的，这在练习中也许不是问题，但当 Logger 的实例数量取决于你应用程序中想记录的细致程度时，如果 Logger 的实例数量不受限制的话，将会变得难以管理。
使用日志适配器传递上下文信息¶
一个传递上下文信息和日志事件信息的简单办法是使用类 LoggerAdapter。 这个类设计的像 Logger，所以可以直接调用 debug()、info()、 warning()、 error()、exception()、 critical() 和 log()。 这些方法在对应的 Logger 中使用相同的签名，所以可以交替使用两种类型的实例。
当你创建一个 LoggerAdapter 的实例时，你会传入一个 Logger 的实例和一个包含了上下文信息的字典对象。当你调用一个 LoggerAdapter 实例的方法时，它会把调用委托给内部的 Logger 的实例，并为其整理相关的上下文信息。这是 LoggerAdapter 的一个代码片段:
def debug(self, msg, *args, **kwargs):
"""
Delegate a debug call to the underlying logger, after adding
contextual information from this adapter instance.
"""
msg, kwargs = self.process(msg, kwargs)
self.logger.debug(msg, *args, **kwargs)
LoggerAdapter 的 process() 方法是将上下文信息添加到日志的输出中。 它传入日志消息和日志调用的关键字参数，并传回（隐式的）这些修改后的内容去调用底层的日志记录器。此方法的默认参数只是一个消息字段，但留有一个 ‘extra’ 的字段作为关键字参数传给构造器。当然，如果你在调用适配器时传入了一个 ‘extra’ 字段的参数，它会被静默覆盖。
使用 ‘extra’ 的优点是这些键值对会被传入 LogRecord 实例的 __dict__ 中，让你通过 Formatter 的实例直接使用定制的字符串，实例能找到这个字典类对象的键。 如果你需要一个其他的方法，比如说，想要在消息字符串前后增加上下文信息，你只需要创建一个 LoggerAdapter 的子类，并覆盖它的 process() 方法来做你想做的事情，以下是一个简单的示例:
class CustomAdapter(logging.LoggerAdapter):
"""
This example adapter expects the passed in dict-like object to have a
'connid' key, whose value in brackets is prepended to the log message.
"""
def process(self, msg, kwargs):
return '[%s] %s' % (self.extra['connid'], msg), kwargs
你可以这样使用:
logger = logging.getLogger(__name__)
adapter = CustomAdapter(logger, {'connid': some_conn_id})
然后，你记录在适配器中的任何事件消息前将添加``some_conn_id``的值。
使用除字典之外的其它对象传递上下文信息¶
你不需要将一个实际的字典传递给 LoggerAdapter-你可以传入一个实现了``__getitem__`` 和``__iter__``的类的实例，这样它就像是一个字典。这对于你想动态生成值（而字典中的值往往是常量）将很有帮助。
使用过滤器传递上下文信息¶
你也可以使用一个用户定义的类 Filter 在日志输出中添加上下文信息。Filter 的实例是被允许修改传入的 LogRecords，包括添加其他的属性，然后可以使用合适的格式化字符串输出，或者可以使用一个自定义的类 Formatter。
例如，在一个web应用程序中，正在处理的请求（或者至少是请求的一部分），可以存储在一个线程本地(threading.local) 变量中，然后从``Filter``中去访问。请求中的信息，如IP地址和用户名将被存储在``LogRecord``中，使用上例``LoggerAdapter``中的 ‘ip’ 和 ‘user’ 属性名。在这种情况下，可以使用相同的格式化字符串来得到上例中类似的输出结果。这是一段示例代码:
import logging
from random import choice
class ContextFilter(logging.Filter):
"""
This is a filter which injects contextual information into the log.
Rather than use actual contextual information, we just use random
data in this demo.
"""
USERS = ['jim', 'fred', 'sheila']
IPS = ['123.231.231.123', '127.0.0.1', '192.168.0.1']
def filter(self, record):
record.ip = choice(ContextFilter.IPS)
record.user = choice(ContextFilter.USERS)
return True
if __name__ == '__main__':
levels = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL)
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)-15s %(name)-5s %(levelname)-8s IP: %(ip)-15s User: %(user)-8s %(message)s')
a1 = logging.getLogger('a.b.c')
a2 = logging.getLogger('d.e.f')
f = ContextFilter()
a1.addFilter(f)
a2.addFilter(f)
a1.debug('A debug message')
a1.info('An info message with %s', 'some parameters')
for x in range(10):
lvl = choice(levels)
lvlname = logging.getLevelName(lvl)
a2.log(lvl, 'A message at %s level with %d %s', lvlname, 2, 'parameters')
which, when run, produces something like:
2010-09-06 22:38:15,292 a.b.c DEBUG IP: 123.231.231.123 User: fred A debug message
2010-09-06 22:38:15,300 a.b.c INFO IP: 192.168.0.1 User: sheila An info message with some parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 127.0.0.1 User: sheila A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f ERROR IP: 127.0.0.1 User: jim A message at ERROR level with 2 parameters
2010-09-06 22:38:15,300 d.e.f DEBUG IP: 127.0.0.1 User: sheila A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,300 d.e.f ERROR IP: 123.231.231.123 User: fred A message at ERROR level with 2 parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 192.168.0.1 User: jim A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 127.0.0.1 User: sheila A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f DEBUG IP: 192.168.0.1 User: jim A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,301 d.e.f ERROR IP: 127.0.0.1 User: sheila A message at ERROR level with 2 parameters
2010-09-06 22:38:15,301 d.e.f DEBUG IP: 123.231.231.123 User: fred A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,301 d.e.f INFO IP: 123.231.231.123 User: fred A message at INFO level with 2 parameters
从多个进程记录至单个文件¶
尽管 logging 是线程安全的，将单个进程中的多个线程日志记录至单个文件也 是 受支持的，但将 多个进程 中的日志记录至单个文件则 不是 受支持的，因为在 Python 中并没有在多个进程中实现对单个文件访问的序列化的标准方案。 如果你需要将多个进程中的日志记录至单个文件，有一个方案是让所有进程都将日志记录至一个 SocketHandler，然后用一个实现了套接字服务器的单独进程一边从套接字中读取一边将日志记录至文件。 （如果愿意的话，你可以在一个现有进程中专门开一个线程来执行此项功能。） 这一部分 文档对此方式有更详细的介绍，并包含一个可用的套接字接收器，你自己的应用可以在此基础上进行适配。
如果你使用的是包含了 multiprocessing 模块的较新版本的 Python，你也可以使用 Lock 来编写自己的处理程序让其从多个进程中按顺序记录至文件。 现有的 FileHandler 和它的子类目前没有使用 multiprocessing，尽管将来可能会这样做。 请注意目前 multiprocessing 模块并非在所有平台上提供可用的锁功能 (参见 https://bugs.python.org/issue3770)。
轮换日志文件¶
有时，你希望当日志文件不断记录增长至一定大小时，打开一个新的文件接着记录。 你可能希望只保留一定数量的日志文件，当不断的创建文件到达该数量时，又覆盖掉最开始的文件形成循环。 对于这种使用场景，日志包提供了 RotatingFileHandler:
import glob
import logging
import logging.handlers
LOG_FILENAME = 'logging_rotatingfile_example.out'
# Set up a specific logger with our desired output level
my_logger = logging.getLogger('MyLogger')
my_logger.setLevel(logging.DEBUG)
# Add the log message handler to the logger
handler = logging.handlers.RotatingFileHandler(
LOG_FILENAME, maxBytes=20, backupCount=5)
my_logger.addHandler(handler)
# Log some messages
for i in range(20):
my_logger.debug('i = %d' % i)
# See what files are created
logfiles = glob.glob('%s*' % LOG_FILENAME)
for filename in logfiles:
print(filename)
The result should be 6 separate files, each with part of the log history for the application:
logging_rotatingfile_example.out
logging_rotatingfile_example.out.1
logging_rotatingfile_example.out.2
logging_rotatingfile_example.out.3
logging_rotatingfile_example.out.4
logging_rotatingfile_example.out.5
最新的文件始终是:file:logging_rotatingfile_example.out，每次到达大小限制时，都会使用后缀``.1``重命名。每个现有的备份文件都会被重命名并增加其后缀（例如``.1`` 变为``.2``），而``.6``文件会被删除掉。
显然，这个例子将日志长度设置得太小，这是一个极端的例子。 你可能希望将*maxBytes*设置为一个合适的值。
An example dictionary-based configuration¶
Below is an example of a logging configuration dictionary - it’s taken from
the documentation on the Django project.
This dictionary is passed to dictConfig() to put the configuration into effect:
LOGGING = {
'version': 1,
'disable_existing_loggers': True,
'formatters': {
'verbose': {
'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s'
},
'simple': {
'format': '%(levelname)s %(message)s'
},
},
'filters': {
'special': {
'()': 'project.logging.SpecialFilter',
'foo': 'bar',
}
},
'handlers': {
'null': {
'level':'DEBUG',
'class':'django.utils.log.NullHandler',
},
'console':{
'level':'DEBUG',
'class':'logging.StreamHandler',
'formatter': 'simple'
},
'mail_admins': {
'level': 'ERROR',
'class': 'django.utils.log.AdminEmailHandler',
'filters': ['special']
}
},
'loggers': {
'django': {
'handlers':['null'],
'propagate': True,
'level':'INFO',
},
'django.request': {
'handlers': ['mail_admins'],
'level': 'ERROR',
'propagate': False,
},
'myproject.custom': {
'handlers': ['console', 'mail_admins'],
'level': 'INFO',
'filters': ['special']
}
}
}
For more information about this configuration, you can see the relevant section of the Django documentation.
Inserting a BOM into messages sent to a SysLogHandler¶
RFC 5424 requires that a Unicode message be sent to a syslog daemon as a set of bytes which have the following structure: an optional pure-ASCII component, followed by a UTF-8 Byte Order Mark (BOM), followed by Unicode encoded using UTF-8. (See the relevant section of the specification.)
In Python 2.6 and 2.7, code was added to
SysLogHandler to insert a BOM into the message, but
unfortunately, it was implemented incorrectly, with the BOM appearing at the
beginning of the message and hence not allowing any pure-ASCII component to
appear before it.
As this behaviour is broken, the incorrect BOM insertion code is being removed from Python 2.7.4 and later. However, it is not being replaced, and if you want to produce RFC 5424-compliant messages which include a BOM, an optional pure-ASCII sequence before it and arbitrary Unicode after it, encoded using UTF-8, then you need to do the following:
Attach a
Formatterinstance to yourSysLogHandlerinstance, with a format string such as:u'ASCII section\ufeffUnicode section'
The Unicode code point
u'\ufeff', when encoded using UTF-8, will be encoded as a UTF-8 BOM – the byte-string'\xef\xbb\xbf'.Replace the ASCII section with whatever placeholders you like, but make sure that the data that appears in there after substitution is always ASCII (that way, it will remain unchanged after UTF-8 encoding).
Replace the Unicode section with whatever placeholders you like; if the data which appears there after substitution contains characters outside the ASCII range, that’s fine – it will be encoded using UTF-8.
If the formatted message is Unicode, it will be encoded using UTF-8 encoding
by SysLogHandler. If you follow the above rules, you should be able to
produce RFC 5424-compliant messages. If you don’t, logging may not complain,
but your messages will not be RFC 5424-compliant, and your syslog daemon may
complain.
Implementing structured logging¶
Although most logging messages are intended for reading by humans, and thus not readily machine-parseable, there might be circumstances where you want to output messages in a structured format which is capable of being parsed by a program (without needing complex regular expressions to parse the log message). This is straightforward to achieve using the logging package. There are a number of ways in which this could be achieved, but the following is a simple approach which uses JSON to serialise the event in a machine-parseable manner:
import json
import logging
class StructuredMessage(object):
def __init__(self, message, **kwargs):
self.message = message
self.kwargs = kwargs
def __str__(self):
return '%s >>> %s' % (self.message, json.dumps(self.kwargs))
_ = StructuredMessage # optional, to improve readability
logging.basicConfig(level=logging.INFO, format='%(message)s')
logging.info(_('message 1', foo='bar', bar='baz', num=123, fnum=123.456))
If the above script is run, it prints:
message 1 >>> {"fnum": 123.456, "num": 123, "bar": "baz", "foo": "bar"}
Note that the order of items might be different according to the version of Python used.
If you need more specialised processing, you can use a custom JSON encoder, as in the following complete example:
from __future__ import unicode_literals
import json
import logging
# This next bit is to ensure the script runs unchanged on 2.x and 3.x
try:
unicode
except NameError:
unicode = str
class Encoder(json.JSONEncoder):
def default(self, o):
if isinstance(o, set):
return tuple(o)
elif isinstance(o, unicode):
return o.encode('unicode_escape').decode('ascii')
return super(Encoder, self).default(o)
class StructuredMessage(object):
def __init__(self, message, **kwargs):
self.message = message
self.kwargs = kwargs
def __str__(self):
s = Encoder().encode(self.kwargs)
return '%s >>> %s' % (self.message, s)
_ = StructuredMessage # optional, to improve readability
def main():
logging.basicConfig(level=logging.INFO, format='%(message)s')
logging.info(_('message 1', set_value=set([1, 2, 3]), snowman='\u2603'))
if __name__ == '__main__':
main()
When the above script is run, it prints:
message 1 >>> {"snowman": "\u2603", "set_value": [1, 2, 3]}
Note that the order of items might be different according to the version of Python used.
Customizing handlers with dictConfig()¶
There are times when you want to customize logging handlers in particular ways,
and if you use dictConfig() you may be able to do this without
subclassing. As an example, consider that you may want to set the ownership of a
log file. On POSIX, this is easily done using os.chown(), but the file
handlers in the stdlib don’t offer built-in support. You can customize handler
creation using a plain function such as:
def owned_file_handler(filename, mode='a', encoding=None, owner=None):
if owner:
import os, pwd, grp
# convert user and group names to uid and gid
uid = pwd.getpwnam(owner[0]).pw_uid
gid = grp.getgrnam(owner[1]).gr_gid
owner = (uid, gid)
if not os.path.exists(filename):
open(filename, 'a').close()
os.chown(filename, *owner)
return logging.FileHandler(filename, mode, encoding)
You can then specify, in a logging configuration passed to dictConfig(),
that a logging handler be created by calling this function:
LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'default': {
'format': '%(asctime)s %(levelname)s %(name)s %(message)s'
},
},
'handlers': {
'file':{
# The values below are popped from this dictionary and
# used to create the handler, set the handler's level and
# its formatter.
'()': owned_file_handler,
'level':'DEBUG',
'formatter': 'default',
# The values below are passed to the handler creator callable
# as keyword arguments.
'owner': ['pulse', 'pulse'],
'filename': 'chowntest.log',
'mode': 'w',
'encoding': 'utf-8',
},
},
'root': {
'handlers': ['file'],
'level': 'DEBUG',
},
}
In this example I am setting the ownership using the pulse user and group,
just for the purposes of illustration. Putting it together into a working
script, chowntest.py:
import logging, logging.config, os, shutil
def owned_file_handler(filename, mode='a', encoding=None, owner=None):
if owner:
if not os.path.exists(filename):
open(filename, 'a').close()
shutil.chown(filename, *owner)
return logging.FileHandler(filename, mode, encoding)
LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'default': {
'format': '%(asctime)s %(levelname)s %(name)s %(message)s'
},
},
'handlers': {
'file':{
# The values below are popped from this dictionary and
# used to create the handler, set the handler's level and
# its formatter.
'()': owned_file_handler,
'level':'DEBUG',
'formatter': 'default',
# The values below are passed to the handler creator callable
# as keyword arguments.
'owner': ['pulse', 'pulse'],
'filename': 'chowntest.log',
'mode': 'w',
'encoding': 'utf-8',
},
},
'root': {
'handlers': ['file'],
'level': 'DEBUG',
},
}
logging.config.dictConfig(LOGGING)
logger = logging.getLogger('mylogger')
logger.debug('A debug message')
To run this, you will probably need to run as root:
$ sudo python3.3 chowntest.py
$ cat chowntest.log
2013-11-05 09:34:51,128 DEBUG mylogger A debug message
$ ls -l chowntest.log
-rw-r--r-- 1 pulse pulse 55 2013-11-05 09:34 chowntest.log
Note that this example uses Python 3.3 because that’s where shutil.chown()
makes an appearance. This approach should work with any Python version that
supports dictConfig() - namely, Python 2.7, 3.2 or later. With pre-3.3
versions, you would need to implement the actual ownership change using e.g.
os.chown().
In practice, the handler-creating function may be in a utility module somewhere in your project. Instead of the line in the configuration:
'()': owned_file_handler,
you could use e.g.:
'()': 'ext://project.util.owned_file_handler',
where project.util can be replaced with the actual name of the package
where the function resides. In the above working script, using
'ext://__main__.owned_file_handler' should work. Here, the actual callable
is resolved by dictConfig() from the ext:// specification.
This example hopefully also points the way to how you could implement other
types of file change - e.g. setting specific POSIX permission bits - in the
same way, using os.chmod().
Of course, the approach could also be extended to types of handler other than a
FileHandler - for example, one of the rotating file handlers,
or a different type of handler altogether.
Configuring filters with dictConfig()¶
You can configure filters using dictConfig(), though it
might not be obvious at first glance how to do it (hence this recipe). Since
Filter is the only filter class included in the standard
library, and it is unlikely to cater to many requirements (it’s only there as a
base class), you will typically need to define your own Filter
subclass with an overridden filter() method. To do this,
specify the () key in the configuration dictionary for the filter,
specifying a callable which will be used to create the filter (a class is the
most obvious, but you can provide any callable which returns a
Filter instance). Here is a complete example:
import logging
import logging.config
import sys
class MyFilter(logging.Filter):
def __init__(self, param=None):
self.param = param
def filter(self, record):
if self.param is None:
allow = True
else:
allow = self.param not in record.msg
if allow:
record.msg = 'changed: ' + record.msg
return allow
LOGGING = {
'version': 1,
'filters': {
'myfilter': {
'()': MyFilter,
'param': 'noshow',
}
},
'handlers': {
'console': {
'class': 'logging.StreamHandler',
'filters': ['myfilter']
}
},
'root': {
'level': 'DEBUG',
'handlers': ['console']
},
}
if __name__ == '__main__':
logging.config.dictConfig(LOGGING)
logging.debug('hello')
logging.debug('hello - noshow')
This example shows how you can pass configuration data to the callable which constructs the instance, in the form of keyword parameters. When run, the above script will print:
changed: hello
which shows that the filter is working as configured.
A couple of extra points to note:
If you can’t refer to the callable directly in the configuration (e.g. if it lives in a different module, and you can’t import it directly where the configuration dictionary is), you can use the form
ext://...as described in Access to external objects. For example, you could have used the text'ext://__main__.MyFilter'instead ofMyFilterin the above example.As well as for filters, this technique can also be used to configure custom handlers and formatters. See User-defined objects for more information on how logging supports using user-defined objects in its configuration, and see the other cookbook recipe Customizing handlers with dictConfig() above.
Customized exception formatting¶
There might be times when you want to do customized exception formatting - for argument’s sake, let’s say you want exactly one line per logged event, even when exception information is present. You can do this with a custom formatter class, as shown in the following example:
import logging
class OneLineExceptionFormatter(logging.Formatter):
def formatException(self, exc_info):
"""
Format an exception so that it prints on a single line.
"""
result = super(OneLineExceptionFormatter, self).formatException(exc_info)
return repr(result) # or format into one line however you want to
def format(self, record):
s = super(OneLineExceptionFormatter, self).format(record)
if record.exc_text:
s = s.replace('\n', '') + '|'
return s
def configure_logging():
fh = logging.FileHandler('output.txt', 'w')
f = OneLineExceptionFormatter('%(asctime)s|%(levelname)s|%(message)s|',
'%d/%m/%Y %H:%M:%S')
fh.setFormatter(f)
root = logging.getLogger()
root.setLevel(logging.DEBUG)
root.addHandler(fh)
def main():
configure_logging()
logging.info('Sample message')
try:
x = 1 / 0
except ZeroDivisionError as e:
logging.exception('ZeroDivisionError: %s', e)
if __name__ == '__main__':
main()
When run, this produces a file with exactly two lines:
28/01/2015 07:21:23|INFO|Sample message|
28/01/2015 07:21:23|ERROR|ZeroDivisionError: integer division or modulo by zero|'Traceback (most recent call last):\n File "logtest7.py", line 30, in main\n x = 1 / 0\nZeroDivisionError: integer division or modulo by zero'|
While the above treatment is simplistic, it points the way to how exception
information can be formatted to your liking. The traceback module may be
helpful for more specialized needs.
Speaking logging messages¶
There might be situations when it is desirable to have logging messages rendered
in an audible rather than a visible format. This is easy to do if you have
text-to-speech (TTS) functionality available in your system, even if it doesn’t have
a Python binding. Most TTS systems have a command line program you can run, and
this can be invoked from a handler using subprocess. It’s assumed here
that TTS command line programs won’t expect to interact with users or take a
long time to complete, and that the frequency of logged messages will be not so
high as to swamp the user with messages, and that it’s acceptable to have the
messages spoken one at a time rather than concurrently, The example implementation
below waits for one message to be spoken before the next is processed, and this
might cause other handlers to be kept waiting. Here is a short example showing
the approach, which assumes that the espeak TTS package is available:
import logging
import subprocess
import sys
class TTSHandler(logging.Handler):
def emit(self, record):
msg = self.format(record)
# Speak slowly in a female English voice
cmd = ['espeak', '-s150', '-ven+f3', msg]
p = subprocess.Popen(cmd, stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
# wait for the program to finish
p.communicate()
def configure_logging():
h = TTSHandler()
root = logging.getLogger()
root.addHandler(h)
# the default formatter just returns the message
root.setLevel(logging.DEBUG)
def main():
logging.info('Hello')
logging.debug('Goodbye')
if __name__ == '__main__':
configure_logging()
sys.exit(main())
When run, this script should say “Hello” and then “Goodbye” in a female voice.
The above approach can, of course, be adapted to other TTS systems and even other systems altogether which can process messages via external programs run from a command line.
缓冲日志消息并有条件地输出它们¶
在某些情况下，你可能希望在临时区域中记录日志消息，并且只在发生某种特定的情况下才输出它们。 例如，你可能希望起始在函数中记录调试事件，如果函数执行完成且没有错误，你不希望输出收集的调试信息以避免造成日志混乱，但如果出现错误，那么你希望所有调试以及错误消息被输出。
下面是一个示例，展示如何在你的日志记录函数上使用装饰器以实现这一功能。该示例使用 logging.handlers.MemoryHandler ，它允许缓冲已记录的事件直到某些条件发生，缓冲的事件才会被刷新（flushed） - 传递给另一个处理程序（ target handler）进行处理。 默认情况下， MemoryHandler 在其缓冲区被填满时被刷新，或者看到一个级别大于或等于指定阈值的事件。 如果想要自定义刷新行为，你可以通过更专业的 MemoryHandler 子类来使用这个秘诀。
The example script has a simple function, foo, which just cycles through
all the logging levels, writing to sys.stderr to say what level it’s about
to log at, and then actually logging a message at that level. You can pass a
parameter to foo which, if true, will log at ERROR and CRITICAL levels -
otherwise, it only logs at DEBUG, INFO and WARNING levels.
The script just arranges to decorate foo with a decorator which will do the
conditional logging that’s required. The decorator takes a logger as a parameter
and attaches a memory handler for the duration of the call to the decorated
function. The decorator can be additionally parameterised using a target handler,
a level at which flushing should occur, and a capacity for the buffer. These
default to a StreamHandler which writes to sys.stderr,
logging.ERROR and 100 respectively.
Here’s the script:
import logging
from logging.handlers import MemoryHandler
import sys
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
def log_if_errors(logger, target_handler=None, flush_level=None, capacity=None):
if target_handler is None:
target_handler = logging.StreamHandler()
if flush_level is None:
flush_level = logging.ERROR
if capacity is None:
capacity = 100
handler = MemoryHandler(capacity, flushLevel=flush_level, target=target_handler)
def decorator(fn):
def wrapper(*args, **kwargs):
logger.addHandler(handler)
try:
return fn(*args, **kwargs)
except Exception:
logger.exception('call failed')
raise
finally:
super(MemoryHandler, handler).flush()
logger.removeHandler(handler)
return wrapper
return decorator
def write_line(s):
sys.stderr.write('%s\n' % s)
def foo(fail=False):
write_line('about to log at DEBUG ...')
logger.debug('Actually logged at DEBUG')
write_line('about to log at INFO ...')
logger.info('Actually logged at INFO')
write_line('about to log at WARNING ...')
logger.warning('Actually logged at WARNING')
if fail:
write_line('about to log at ERROR ...')
logger.error('Actually logged at ERROR')
write_line('about to log at CRITICAL ...')
logger.critical('Actually logged at CRITICAL')
return fail
decorated_foo = log_if_errors(logger)(foo)
if __name__ == '__main__':
logger.setLevel(logging.DEBUG)
write_line('Calling undecorated foo with False')
assert not foo(False)
write_line('Calling undecorated foo with True')
assert foo(True)
write_line('Calling decorated foo with False')
assert not decorated_foo(False)
write_line('Calling decorated foo with True')
assert decorated_foo(True)
When this script is run, the following output should be observed:
Calling undecorated foo with False
about to log at DEBUG ...
about to log at INFO ...
about to log at WARNING ...
Calling undecorated foo with True
about to log at DEBUG ...
about to log at INFO ...
about to log at WARNING ...
about to log at ERROR ...
about to log at CRITICAL ...
Calling decorated foo with False
about to log at DEBUG ...
about to log at INFO ...
about to log at WARNING ...
Calling decorated foo with True
about to log at DEBUG ...
about to log at INFO ...
about to log at WARNING ...
about to log at ERROR ...
Actually logged at DEBUG
Actually logged at INFO
Actually logged at WARNING
Actually logged at ERROR
about to log at CRITICAL ...
Actually logged at CRITICAL
如你所见，实际日志记录输出仅在消息等级为ERROR或更高的事件时发生，但在这种情况下，任何之前较低消息等级的事件还会被记录。
你当然可以使用传统的装饰方法:
@log_if_errors(logger)
def foo(fail=False):
...
通过配置使用UTC (GMT) 格式化时间¶
有时候，你希望使用UTC来格式化时间，这可以通过使用一个类来实现，例如`UTCFormatter`，如下所示：
import logging
import time
class UTCFormatter(logging.Formatter):
converter = time.gmtime
然后你可以在你的代码中使用 UTCFormatter，而不是 Formatter。 如果你想通过配置来实现这一功能，你可以使用 dictConfig() API 来完成，该方法在以下完整示例中展示:
import logging
import logging.config
import time
class UTCFormatter(logging.Formatter):
converter = time.gmtime
LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'formatters': {
'utc': {
'()': UTCFormatter,
'format': '%(asctime)s %(message)s',
},
'local': {
'format': '%(asctime)s %(message)s',
}
},
'handlers': {
'console1': {
'class': 'logging.StreamHandler',
'formatter': 'utc',
},
'console2': {
'class': 'logging.StreamHandler',
'formatter': 'local',
},
},
'root': {
'handlers': ['console1', 'console2'],
}
}
if __name__ == '__main__':
logging.config.dictConfig(LOGGING)
logging.warning('The local time is %s', time.asctime())
When this script is run, it should print something like:
2015-10-17 12:53:29,501 The local time is Sat Oct 17 13:53:29 2015
2015-10-17 13:53:29,501 The local time is Sat Oct 17 13:53:29 2015
展示了如何将时间格式化为本地时间和UTC两种形式，其中每种形式对应一个日志处理器 。
使用上下文管理器的可选的日志记录¶
有时候，我们需要暂时更改日志配置，并在执行某些操作后将其还原。为此，上下文管理器是实现保存和恢复日志上下文的最明显的方式。这是一个关于上下文管理器的简单例子，它允许你在上下文管理器的作用域内更改日志记录等级以及增加日志处理器：
import logging
import sys
class LoggingContext(object):
def __init__(self, logger, level=None, handler=None, close=True):
self.logger = logger
self.level = level
self.handler = handler
self.close = close
def __enter__(self):
if self.level is not None:
self.old_level = self.logger.level
self.logger.setLevel(self.level)
if self.handler:
self.logger.addHandler(self.handler)
def __exit__(self, et, ev, tb):
if self.level is not None:
self.logger.setLevel(self.old_level)
if self.handler:
self.logger.removeHandler(self.handler)
if self.handler and self.close:
self.handler.close()
# implicit return of None => don't swallow exceptions
如果指定上下文管理器的日志记录等级属性，则在上下文管理器的with语句所涵盖的代码中，日志记录器的记录等级将临时设置为上下文管理器所配置的日志记录等级。 如果指定上下文管理的日志处理器属性，则该句柄在进入上下文管理器的上下文时添加到记录器中，并在退出时被删除。 如果你再也不需要该日志处理器时，你可以让上下文管理器在退出上下文管理器的上下文时关闭它。
为了说明它是如何工作的，我们可以在上面添加以下代码块:
if __name__ == '__main__':
logger = logging.getLogger('foo')
logger.addHandler(logging.StreamHandler())
logger.setLevel(logging.INFO)
logger.info('1. This should appear just once on stderr.')
logger.debug('2. This should not appear.')
with LoggingContext(logger, level=logging.DEBUG):
logger.debug('3. This should appear once on stderr.')
logger.debug('4. This should not appear.')
h = logging.StreamHandler(sys.stdout)
with LoggingContext(logger, level=logging.DEBUG, handler=h, close=True):
logger.debug('5. This should appear twice - once on stderr and once on stdout.')
logger.info('6. This should appear just once on stderr.')
logger.debug('7. This should not appear.')
我们最初设置日志记录器的消息等级为``INFO``，因此消息#1出现，消息#2没有出现。在接下来的``with``代码块中我们暂时将消息等级变更为``DEBUG``，从而消息#3出现。在这一代码块退出后，日志记录器的消息等级恢复为``INFO``，从而消息#4没有出现。在下一个``with``代码块中，我们再一次将设置消息等级设置为``DEBUG``，同时添加一个将消息写入``sys.stdout``的日志处理器。因此，消息#5在控制台出现两次（分别通过``stderr``和``stdout``）。在``with``语句完成后，状态与之前一样，因此消息#6出现（类似消息#1），而消息#7没有出现（类似消息#2）。
如果我们运行生成的脚本，结果如下：
$ python logctx.py
1. This should appear just once on stderr.
3. This should appear once on stderr.
5. This should appear twice - once on stderr and once on stdout.
5. This should appear twice - once on stderr and once on stdout.
6. This should appear just once on stderr.
我们将``stderr``标准错误重定向到``/dev/null``，我再次运行生成的脚步，唯一被写入``stdout``标准输出的消息，即我们所能看见的消息，如下：
$ python logctx.py 2>/dev/null
5. This should appear twice - once on stderr and once on stdout.
再一次，将``stdout``标准输出重定向到``/dev/null``，我获得如下结果：
$ python logctx.py >/dev/null
1. This should appear just once on stderr.
3. This should appear once on stderr.
5. This should appear twice - once on stderr and once on stdout.
6. This should appear just once on stderr.
在这种情况下，与预期一致，打印到``stdout``标准输出的消息＃5不会出现。
当然，这里描述的方法可以被推广，例如临时附加日志记录过滤器。 请注意，上面的代码适用于Python 2以及Python 3。