logging.config
— 日志配置
¶
重要
此页面仅包含参考信息。有关教程，请参阅
本节描述配置日志模块的 API。
下列函数用于配置 logging 模块。它们位于
logging.config
module. Their use is optional — you can configure the logging module using these functions or by making calls to the main API (defined in
logging
itself) and defining handlers which are declared either in
logging
or
logging.handlers
.
logging.config.
dictConfig
(
config
)
¶
Takes the logging configuration from a dictionary. The contents of this dictionary are described in 配置字典模式 下文。
If an error is encountered during configuration, this function will raise a
ValueError,TypeError,AttributeErrororImportErrorwith a suitably descriptive message. The following is a (possibly incomplete) list of conditions which will raise an error:
- A
levelwhich is not a string or which is a string not corresponding to an actual logging level.- A
propagatevalue which is not a boolean.- An id which does not have a corresponding destination.
- A non-existent handler id found during an incremental call.
- An invalid logger name.
- Inability to resolve to an internal or external object.
Parsing is performed by the
DictConfiguratorclass, whose constructor is passed the dictionary used for configuration, and has aconfigure()方法。logging.configmodule has a callable attributedictConfigClasswhich is initially set toDictConfigurator. You can replace the value ofdictConfigClasswith a suitable implementation of your own.
dictConfig()调用dictConfigClasspassing the specified dictionary, and then calls theconfigure()method on the returned object to put the configuration into effect:def dictConfig(config): dictConfigClass(config).configure()例如，子类化的
DictConfiguratorcould callDictConfigurator.__init__()in its own__init__(), then set up custom prefixes which would be usable in the subsequentconfigure()调用。dictConfigClasswould be bound to this new subclass, and thendictConfig()could be called exactly as in the default, uncustomized state.3.2 版新增。
logging.config.
fileConfig
(
fname
,
defaults=None
,
disable_existing_loggers=True
)
¶
读取日志记录配置从
configparser
格式文件。文件格式的描述应该如在
配置文件格式
. This function can be called several times from an application, allowing an end user to select from various pre-canned configurations (if the developer provides a mechanism to present the choices and load the chosen configuration).
| 参数： |
|
|---|
3.4 版改变：
An instance of a subclass of
RawConfigParser
is now accepted as a value for
fname
. This facilitates:
fileConfig
.
logging.config.
listen
(
port=DEFAULT_LOGGING_CONFIG_PORT
,
verify=None
)
¶
Starts up a socket server on the specified port, and listens for new configurations. If no port is specified, the module’s default
DEFAULT_LOGGING_CONFIG_PORT
is used. Logging configurations will be sent as a file suitable for processing by
fileConfig()
。返回
Thread
instance on which you can call
start()
to start the server, and which you can
join()
when appropriate. To stop the server, call
stopListening()
.
The
verify
argument, if specified, should be a callable which should verify whether bytes received across the socket are valid and should be processed. This could be done by encrypting and/or signing what is sent across the socket, such that the
verify
callable can perform signature verification and/or decryption. The
verify
callable is called with a single argument - the bytes received across the socket - and should return the bytes to be processed, or None to indicate that the bytes should be discarded. The returned bytes could be the same as the passed in bytes (e.g. when only verification is done), or they could be completely different (perhaps if decryption were performed).
To send a configuration to the socket, read in the configuration file and send it to the socket as a string of bytes preceded by a four-byte length string packed in binary using
struct.pack('>L', n)
.
注意
Because portions of the configuration are passed through
eval()
, use of this function may open its users to a security risk. While the function only binds to a socket on
localhost
, and so does not accept connections from remote machines, there are scenarios where untrusted code could be run under the account of the process which calls
listen()
. Specifically, if the process calling
listen()
runs on a multi-user machine where users cannot trust each other, then a malicious user could arrange to run essentially arbitrary code in a victim user’s process, simply by connecting to the victim’s
listen()
socket and sending a configuration which runs whatever code the attacker wants to have executed in the victim’s process. This is especially easy to do if the default port is used, but not hard even if a different port is used). To avoid the risk of this happening, use the
verify
自变量对于
listen()
to prevent unrecognised configurations from being applied.
Changed in version 3.4.:
The
verify
自变量被添加。
logging.config.
stopListening
(
)
¶
Stops the listening server which was created with a call to
listen()
. This is typically called before calling
join()
on the return value from
listen()
.
Describing a logging configuration requires listing the various objects to create and the connections between them; for example, you may create a handler named ‘console’ and then say that the logger named ‘startup’ will send its messages to the ‘console’ handler. These objects aren’t limited to those provided by the
logging
module because you might write your own formatter or handler class. The parameters to these classes may also need to include external objects such as
sys.stderr
. The syntax for describing these objects and connections is defined in
对象连接
下文。
The dictionary passed to
dictConfig()
must contain the following keys:
All other keys are optional, but if present they will be interpreted as described below. In all cases below where a ‘configuring dict’ is mentioned, it will be checked for the special
'()'
key to see if a custom instantiation is required. If so, the mechanism described in
用户定义对象
below is used to create an instance; otherwise, the context is used to determine what to instantiate.
formatters
- the corresponding value will be a dict in which each key is a formatter id and each value is a dict describing how to configure the corresponding
Formatter
实例。
The configuring dict is searched for keys
format
and
datefmt
(with defaults of
None
) and these are used to construct a
Formatter
实例。
过滤 - the corresponding value will be a dict in which each key is a filter id and each value is a dict describing how to configure the corresponding Filter instance.
The configuring dict is searched for the key
name
(defaulting to the empty string) and this is used to construct a
logging.Filter
实例。
handlers - the corresponding value will be a dict in which each key is a handler id and each value is a dict describing how to configure the corresponding Handler instance.
The configuring dict is searched for the following keys:
class
(mandatory). This is the fully qualified name of the
handler class.
level
(optional). The level of the handler.
formatter
(optional). The id of the formatter for this
handler.
filters
(optional). A list of ids of the filters for this
handler.
所有 other keys are passed through as keyword arguments to the handler’s constructor. For example, given the snippet:
handlers:
console:
class : logging.StreamHandler
formatter: brief
level : INFO
filters: [allow_foo]
stream : ext://sys.stdout
文件：
class : logging.handlers.RotatingFileHandler
formatter: precise
filename: logconfig.log
maxBytes: 1024
backupCount: 3
the handler with id
console
is instantiated as a
logging.StreamHandler
，使用
sys.stdout
as the underlying stream. The handler with id
file
is instantiated as a
logging.handlers.RotatingFileHandler
with the keyword arguments
filename='logconfig.log', maxBytes=1024, backupCount=3
.
loggers - the corresponding value will be a dict in which each key is a logger name and each value is a dict describing how to configure the corresponding Logger instance.
The configuring dict is searched for the following keys:
level
(optional). The level of the logger.
propagate
(optional). The propagation setting of the logger.
filters
(optional). A list of ids of the filters for this
logger.
handlers
(optional). A list of ids of the handlers for this
logger.
The specified loggers will be configured according to the level, propagation, filters and handlers specified.
root
- this will be the configuration for the root logger. Processing of the configuration will be as for any logger, except that the
propagate
setting will not be applicable.
incremental
- whether the configuration is to be interpreted as incremental to the existing configuration. This value defaults to
False
, which means that the specified configuration replaces the existing configuration with the same semantics as used by the existing
fileConfig()
API。
If the specified value is
True
, the configuration is processed as described in the section on
增量配置
.
disable_existing_loggers
- whether any existing loggers are to be disabled. This setting mirrors the parameter of the same name in
fileConfig()
. If absent, this parameter defaults to
True
. This value is ignored if
incremental
is
True
.
It is difficult to provide complete flexibility for incremental configuration. For example, because objects such as filters and formatters are anonymous, once a configuration is set up, it is not possible to refer to such anonymous objects when augmenting a configuration.
Furthermore, there is not a compelling case for arbitrarily altering the object graph of loggers, handlers, filters, formatters at run-time, once a configuration is set up; the verbosity of loggers and handlers can be controlled just by setting levels (and, in the case of loggers, propagation flags). Changing the object graph arbitrarily in a safe way is problematic in a multi-threaded environment; while not impossible, the benefits are not worth the complexity it adds to the implementation.
Thus, when the
incremental
key of a configuration dict is present and is
True
, the system will completely ignore any
formatters
and
filters
entries, and process only the
level
settings in the
handlers
entries, and the
level
and
propagate
settings in the
loggers
and
root
条目。
Using a value in the configuration dict lets configurations to be sent over the wire as pickled dicts to a socket listener. Thus, the logging verbosity of a long-running application can be altered over time with no need to stop and restart the application.
The schema describes a set of logging objects - loggers, handlers, formatters, filters - which are connected to each other in an object graph. Thus, the schema needs to represent connections between the objects. For example, say that, once configured, a particular logger has attached to it a particular handler. For the purposes of this discussion, we can say that the logger represents the source, and the handler the destination, of a connection between the two. Of course in the configured objects this is represented by the logger holding a reference to the handler. In the configuration dict, this is done by giving each destination object an id which identifies it unambiguously, and then using the id in the source object’s configuration to indicate that a connection exists between the source and the destination object with that id.
So, for example, consider the following YAML snippet:
formatters: brief: # configuration for formatter with id 'brief' goes here precise: # configuration for formatter with id 'precise' goes here handlers: h1: #This is an id # configuration of handler with id 'h1' goes here formatter: brief h2: #This is another id # configuration of handler with id 'h2' goes here formatter: precise loggers: foo.bar.baz: # other configuration for logger 'foo.bar.baz' handlers: [h1, h2]
(Note: YAML used here because it’s a little more readable than the equivalent Python source form for the dictionary.)
The ids for loggers are the logger names which would be used programmatically to obtain a reference to those loggers, e.g.
foo.bar.baz
. The ids for Formatters and Filters can be any string value (such as
brief
,
precise
above) and they are transient, in that they are only meaningful for processing the configuration dictionary and used to determine connections between objects, and are not persisted anywhere when the configuration call is complete.
The above snippet indicates that logger named
foo.bar.baz
should have two handlers attached to it, which are described by the handler ids
h1
and
h2
. The formatter for
h1
is that described by id
brief
, and the formatter for
h2
is that described by id
precise
.
The schema supports user-defined objects for handlers, filters and formatters. (Loggers do not need to have different types for different instances, so there is no support in this configuration schema for user-defined logger classes.)
Objects to be configured are described by dictionaries which detail their configuration. In some places, the logging system will be able to infer from the context how an object is to be instantiated, but when a user-defined object is to be instantiated, the system will not know how to do this. In order to provide complete flexibility for user-defined object instantiation, the user needs to provide a ‘factory’ - a callable which is called with a configuration dictionary and which returns the instantiated object. This is signalled by an absolute import path to the factory being made available under the special key
'()'
。这里是具体范例：
formatters: brief: format: '%(message)s' default: format: '%(asctime)s %(levelname)-8s %(name)-15s %(message)s' datefmt: '%Y-%m-%d %H:%M:%S' custom: (): my.package.customFormatterFactory bar: baz spam: 99.9 answer: 42
The above YAML snippet defines three formatters. The first, with id
brief
, is a standard
logging.Formatter
instance with the specified format string. The second, with id
default
, has a longer format and also defines the time format explicitly, and will result in a
logging.Formatter
initialized with those two format strings. Shown in Python source form, the
brief
and
default
formatters have configuration sub-dictionaries:
{ 'format' : '%(message)s' }
and:
{ 'format' : '%(asctime)s %(levelname)-8s %(name)-15s %(message)s', 'datefmt' : '%Y-%m-%d %H:%M:%S' }
respectively, and as these dictionaries do not contain the special key
'()'
, the instantiation is inferred from the context: as a result, standard
logging.Formatter
instances are created. The configuration sub-dictionary for the third formatter, with id
custom
, is:
{ '()' : 'my.package.customFormatterFactory', 'bar' : 'baz', 'spam' : 99.9, 'answer' : 42 }
and this contains the special key
'()'
, which means that user-defined instantiation is wanted. In this case, the specified factory callable will be used. If it is an actual callable it will be used directly - otherwise, if you specify a string (as in the example) the actual callable will be located using normal import mechanisms. The callable will be called with the
remaining
items in the configuration sub-dictionary as keyword arguments. In the above example, the formatter with id
custom
will be assumed to be returned by the call:
my.package.customFormatterFactory(bar='baz', spam=99.9, answer=42)
The key
'()'
has been used as the special key because it is not a valid keyword parameter name, and so will not clash with the names of the keyword arguments used in the call. The
'()'
also serves as a mnemonic that the corresponding value is a callable.
There are times where a configuration needs to refer to objects external to the configuration, for example
sys.stderr
. If the configuration dict is constructed using Python code, this is straightforward, but a problem arises when the configuration is provided via a text file (e.g. JSON, YAML). In a text file, there is no standard way to distinguish
sys.stderr
from the literal string
'sys.stderr'
. To facilitate this distinction, the configuration system looks for certain special prefixes in string values and treat them specially. For example, if the literal string
'ext://sys.stderr'
is provided as a value in the configuration, then the
ext://
will be stripped off and the remainder of the value processed using normal import mechanisms.
The handling of such prefixes is done in a way analogous to protocol handling: there is a generic mechanism to look for prefixes which match the regular expression
^(?P<prefix>[a-z]+)://(?P<suffix>.*)$
whereby, if the
prefix
is recognised, the
suffix
is processed in a prefix-dependent manner and the result of the processing replaces the string value. If the prefix is not recognised, then the string value will be left as-is.
As well as external objects, there is sometimes also a need to refer to objects in the configuration. This will be done implicitly by the configuration system for things that it knows about. For example, the string value
'DEBUG'
对于
level
in a logger or handler will automatically be converted to the value
logging.DEBUG
，和
handlers
,
filters
and
formatter
entries will take an object id and resolve to the appropriate destination object.
However, a more generic mechanism is needed for user-defined objects which are not known to the
logging
module. For example, consider
logging.handlers.MemoryHandler
, which takes a
target
argument which is another handler to delegate to. Since the system already knows about this class, then in the configuration, the given
target
just needs to be the object id of the relevant target handler, and the system will resolve to the handler from the id. If, however, a user defines a
my.package.MyHandler
which has an
alternate
handler, the configuration system would not know that the
alternate
referred to a handler. To cater for this, a generic resolution system allows the user to specify:
handlers: file: # configuration of file handler goes here custom: (): my.package.MyHandler alternate: cfg://handlers.file
文字字符串
'cfg://handlers.file'
will be resolved in an analogous way to strings with the
ext://
prefix, but looking in the configuration itself rather than the import namespace. The mechanism allows access by dot or by index, in a similar way to that provided by
str.format
。因此，给出以下片段：
handlers: email: class: logging.handlers.SMTPHandler mailhost: localhost fromaddr: my_app@domain.tld toaddrs: - support_team@domain.tld - dev_team@domain.tld subject: Houston, we have a problem.
in the configuration, the string
'cfg://handlers'
would resolve to the dict with key
handlers
，字符串
'cfg://handlers.email
would resolve to the dict with key
email
在
handlers
dict, and so on. The string
'cfg://handlers.email.toaddrs[1]
would resolve to
'dev_team.domain.tld'
and the string
'cfg://handlers.email.toaddrs[0]'
would resolve to the value
'support_team@domain.tld'
。
subject
value could be accessed using either
'cfg://handlers.email.subject'
or, equivalently,
'cfg://handlers.email[subject]'
. The latter form only needs to be used if the key contains spaces or non-alphanumeric characters. If an index value consists only of decimal digits, access will be attempted using the corresponding integer value, falling back to the string value if needed.
Given a string
cfg://handlers.myhandler.mykey.123
, this will resolve to
config_dict['handlers']['myhandler']['mykey']['123']
. If the string is specified as
cfg://handlers.myhandler.mykey[123]
, the system will attempt to retrieve the value from
config_dict['handlers']['myhandler']['mykey'][123]
, and fall back to
config_dict['handlers']['myhandler']['mykey']['123']
if that fails.
Import resolution, by default, uses the builtin
__import__()
function to do its importing. You may want to replace this with your own importing mechanism: if so, you can replace the
importer
属性在
DictConfigurator
or its superclass, the
BaseConfigurator
class. However, you need to be careful because of the way functions are accessed from classes via descriptors. If you are using a Python callable to do your imports, and you want to define it at class level rather than instance level, you need to wrap it with
staticmethod()
。例如：
from importlib import import_module from logging.config import BaseConfigurator BaseConfigurator.importer = staticmethod(import_module)
You don’t need to wrap with
staticmethod()
if you’re setting the import callable on a configurator
instance
.
配置文件格式的理解是通过
fileConfig()
基于
configparser
功能。文件必须包含的区间称为
[loggers]
,
[handlers]
and
[formatters]
which identify by name the entities of each type which are defined in the file. For each such entity, there is a separate section which identifies how that entity is configured. Thus, for a logger named
log01
在
[loggers]
section, the relevant configuration details are held in a section
[logger_log01]
. Similarly, a handler called
hand01
在
[handlers]
section will have its configuration held in a section called
[handler_hand01]
, while a formatter called
form01
在
[formatters]
section will have its configuration specified in a section called
[formatter_form01]
. The root logger configuration must be specified in a section called
[logger_root]
.
注意
The
fileConfig()
API is older than the
dictConfig()
API and does not provide functionality to cover certain aspects of logging. For example, you cannot configure
Filter
objects, which provide for filtering of messages beyond simple integer levels, using
fileConfig()
. If you need to have instances of
Filter
in your logging configuration, you will need to use
dictConfig()
. Note that future enhancements to configuration functionality will be added to
dictConfig()
, so it’s worth considering transitioning to this newer API when it’s convenient to do so.
Examples of these sections in the file are given below.
[loggers] keys=root,log02,log03,log04,log05,log06,log07 [handlers] keys=hand01,hand02,hand03,hand04,hand05,hand06,hand07,hand08,hand09 [formatters] keys=form01,form02,form03,form04,form05,form06,form07,form08,form09
The root logger must specify a level and a list of handlers. An example of a root logger section is given below.
[logger_root] level=NOTSET handlers=hand01
The
level
entry can be one of
DEBUG, INFO, WARNING, ERROR, CRITICAL
or
NOTSET
. For the root logger only,
NOTSET
means that all messages will be logged. Level values are
eval()
uated in the context of the
logging
package’s namespace.
The
handlers
entry is a comma-separated list of handler names, which must appear in the
[handlers]
section. These names must appear in the
[handlers]
section and have corresponding sections in the configuration file.
For loggers other than the root logger, some additional information is required. This is illustrated by the following example.
[logger_parser] level=DEBUG handlers=hand01 propagate=1 qualname=compiler.parser
The
level
and
handlers
entries are interpreted as for the root logger, except that if a non-root logger’s level is specified as
NOTSET
, the system consults loggers higher up the hierarchy to determine the effective level of the logger. The
propagate
entry is set to 1 to indicate that messages must propagate to handlers higher up the logger hierarchy from this logger, or 0 to indicate that messages are
not
propagated to handlers up the hierarchy. The
qualname
entry is the hierarchical channel name of the logger, that is to say the name used by the application to get the logger.
Sections which specify handler configuration are exemplified by the following.
[handler_hand01] class=StreamHandler level=NOTSET formatter=form01 args=(sys.stdout,)
The
class
entry indicates the handler’s class (as determined by
eval()
在
logging
package’s namespace). The
level
is interpreted as for loggers, and
NOTSET
is taken to mean ‘log everything’.
The
formatter
entry indicates the key name of the formatter for this handler. If blank, a default formatter (
logging._defaultFormatter
) is used. If a name is specified, it must appear in the
[formatters]
section and have a corresponding section in the configuration file.
The
args
entry, when
eval()
uated in the context of the
logging
package’s namespace, is the list of arguments to the constructor for the handler class. Refer to the constructors for the relevant handlers, or to the examples below, to see how typical entries are constructed.
[handler_hand02] class=FileHandler level=DEBUG formatter=form02 args=('python.log', 'w') [handler_hand03] class=handlers.SocketHandler level=INFO formatter=form03 args=('localhost', handlers.DEFAULT_TCP_LOGGING_PORT) [handler_hand04] class=handlers.DatagramHandler level=WARN formatter=form04 args=('localhost', handlers.DEFAULT_UDP_LOGGING_PORT) [handler_hand05] class=handlers.SysLogHandler level=ERROR formatter=form05 args=(('localhost', handlers.SYSLOG_UDP_PORT), handlers.SysLogHandler.LOG_USER) [handler_hand06] class=handlers.NTEventLogHandler level=CRITICAL formatter=form06 args=('Python Application', '', 'Application') [handler_hand07] class=handlers.SMTPHandler level=WARN formatter=form07 args=('localhost', 'from@abc', ['user1@abc', 'user2@xyz'], 'Logger Subject') [handler_hand08] class=handlers.MemoryHandler level=NOTSET formatter=form08 target= args=(10, ERROR) [handler_hand09] class=handlers.HTTPHandler level=NOTSET formatter=form09 args=('localhost:9022', '/log', 'GET')
Sections which specify formatter configuration are typified by the following.
[formatter_form01] format=F1 %(asctime)s %(levelname)s %(message)s datefmt= class=logging.Formatter
The
format
entry is the overall format string, and the
datefmt
entry is the
strftime()
-compatible date/time format string. If empty, the package substitutes ISO8601 format date/times, which is almost equivalent to specifying the date format string
'%Y-%m-%d %H:%M:%S'
. The ISO8601 format also specifies milliseconds, which are appended to the result of using the above format string, with a comma separator. An example time in ISO8601 format is
2003-01-23 00:29:50,411
.
The
class
entry is optional. It indicates the name of the formatter’s class (as a dotted module and class name.) This option is useful for instantiating a
Formatter
subclass. Subclasses of
Formatter
can present exception tracebacks in an expanded or condensed format.
注意
Due to the use of
eval()
as described above, there are potential security risks which result from using the
listen()
to send and receive configurations via sockets. The risks are limited to where multiple users with no mutual trust run code on the same machine; see the
listen()
文档编制，了解更多信息。
另请参阅
logging
logging.handlers
logging.config
— 日志配置
16.8.
logging.handlers
— 日志处理程序
键入搜索术语或模块、类、函数名称。