doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
|---|---|
locale.T_FMT
Get a string that can be used as a format string for time.strftime() to represent a time in a locale-specific way. | python.library.locale#locale.T_FMT |
locale.T_FMT_AMPM
Get a format string for time.strftime() to represent time in the am/pm format. | python.library.locale#locale.T_FMT_AMPM |
locale.YESEXPR
Get a regular expression that can be used with the regex function to recognize a positive response to a yes/no question. Note The expression is in the syntax suitable for the regex() function from the C library, which might differ from the syntax used in re. | python.library.locale#locale.YESEXPR |
locals()
Update and return a dictionary representing the current local symbol table. Free variables are returned by locals() when it is called in function blocks, but not in class blocks. Note that at the module level, locals() and globals() are the same dictionary. Note The contents of this dictionary should not be modified; changes may not affect the values of local and free variables used by the interpreter. | python.library.functions#locals |
logging — Logging facility for Python Source code: Lib/logging/__init__.py Important This page contains the API reference information. For tutorial information and discussion of more advanced topics, see Basic Tutorial Advanced Tutorial Logging Cookbook This module defines functions and classes which implement a flexible event logging system for applications and libraries. The key benefit of having the logging API provided by a standard library module is that all Python modules can participate in logging, so your application log can include your own messages integrated with messages from third-party modules. The module provides a lot of functionality and flexibility. If you are unfamiliar with logging, the best way to get to grips with it is to see the tutorials (see the links on the right). The basic classes defined by the module, together with their functions, are listed below. Loggers expose the interface that application code directly uses. Handlers send the log records (created by loggers) to the appropriate destination. Filters provide a finer grained facility for determining which log records to output. Formatters specify the layout of log records in the final output. Logger Objects Loggers have the following attributes and methods. Note that Loggers should NEVER be instantiated directly, but always through the module-level function logging.getLogger(name). Multiple calls to getLogger() with the same name will always return a reference to the same Logger object. The name is potentially a period-separated hierarchical value, like foo.bar.baz (though it could also be just plain foo, for example). Loggers that are further down in the hierarchical list are children of loggers higher up in the list. For example, given a logger with a name of foo, loggers with names of foo.bar, foo.bar.baz, and foo.bam are all descendants of foo. The logger name hierarchy is analogous to the Python package hierarchy, and identical to it if you organise your loggers on a per-module basis using the recommended construction logging.getLogger(__name__). That’s because in a module, __name__ is the module’s name in the Python package namespace.
class logging.Logger
propagate
If this attribute evaluates to true, events logged to this logger will be passed to the handlers of higher level (ancestor) loggers, in addition to any handlers attached to this logger. Messages are passed directly to the ancestor loggers’ handlers - neither the level nor filters of the ancestor loggers in question are considered. If this evaluates to false, logging messages are not passed to the handlers of ancestor loggers. The constructor sets this attribute to True. Note If you attach a handler to a logger and one or more of its ancestors, it may emit the same record multiple times. In general, you should not need to attach a handler to more than one logger - if you just attach it to the appropriate logger which is highest in the logger hierarchy, then it will see all events logged by all descendant loggers, provided that their propagate setting is left set to True. A common scenario is to attach handlers only to the root logger, and to let propagation take care of the rest.
setLevel(level)
Sets the threshold for this logger to level. Logging messages which are less severe than level will be ignored; logging messages which have severity level or higher will be emitted by whichever handler or handlers service this logger, unless a handler’s level has been set to a higher severity level than level. When a logger is created, the level is set to NOTSET (which causes all messages to be processed when the logger is the root logger, or delegation to the parent when the logger is a non-root logger). Note that the root logger is created with level WARNING. The term ‘delegation to the parent’ means that if a logger has a level of NOTSET, its chain of ancestor loggers is traversed until either an ancestor with a level other than NOTSET is found, or the root is reached. If an ancestor is found with a level other than NOTSET, then that ancestor’s level is treated as the effective level of the logger where the ancestor search began, and is used to determine how a logging event is handled. If the root is reached, and it has a level of NOTSET, then all messages will be processed. Otherwise, the root’s level will be used as the effective level. See Logging Levels for a list of levels. Changed in version 3.2: The level parameter now accepts a string representation of the level such as ‘INFO’ as an alternative to the integer constants such as INFO. Note, however, that levels are internally stored as integers, and methods such as e.g. getEffectiveLevel() and isEnabledFor() will return/expect to be passed integers.
isEnabledFor(level)
Indicates if a message of severity level would be processed by this logger. This method checks first the module-level level set by logging.disable(level) and then the logger’s effective level as determined by getEffectiveLevel().
getEffectiveLevel()
Indicates the effective level for this logger. If a value other than NOTSET has been set using setLevel(), it is returned. Otherwise, the hierarchy is traversed towards the root until a value other than NOTSET is found, and that value is returned. The value returned is an integer, typically one of logging.DEBUG, logging.INFO etc.
getChild(suffix)
Returns a logger which is a descendant to this logger, as determined by the suffix. Thus, logging.getLogger('abc').getChild('def.ghi') would return the same logger as would be returned by logging.getLogger('abc.def.ghi'). This is a convenience method, useful when the parent logger is named using e.g. __name__ rather than a literal string. New in version 3.2.
debug(msg, *args, **kwargs)
Logs a message with level DEBUG on this logger. The msg is the message format string, and the args are the arguments which are merged into msg using the string formatting operator. (Note that this means that you can use keywords in the format string, together with a single dictionary argument.) No % formatting operation is performed on msg when no args are supplied. There are four keyword arguments in kwargs which are inspected: exc_info, stack_info, stacklevel and extra. If exc_info does not evaluate as false, it causes exception information to be added to the logging message. If an exception tuple (in the format returned by sys.exc_info()) or an exception instance is provided, it is used; otherwise, sys.exc_info() is called to get the exception information. The second optional keyword argument is stack_info, which defaults to False. If true, stack information is added to the logging message, including the actual logging call. Note that this is not the same stack information as that displayed through specifying exc_info: The former is stack frames from the bottom of the stack up to the logging call in the current thread, whereas the latter is information about stack frames which have been unwound, following an exception, while searching for exception handlers. You can specify stack_info independently of exc_info, e.g. to just show how you got to a certain point in your code, even when no exceptions were raised. The stack frames are printed following a header line which says: Stack (most recent call last):
This mimics the Traceback (most recent call last): which is used when displaying exception frames. The third optional keyword argument is stacklevel, which defaults to 1. If greater than 1, the corresponding number of stack frames are skipped when computing the line number and function name set in the LogRecord created for the logging event. This can be used in logging helpers so that the function name, filename and line number recorded are not the information for the helper function/method, but rather its caller. The name of this parameter mirrors the equivalent one in the warnings module. The fourth keyword argument is extra which can be used to pass a dictionary which is used to populate the __dict__ of the LogRecord created for the logging event with user-defined attributes. These custom attributes can then be used as you like. For example, they could be incorporated into logged messages. For example: FORMAT = '%(asctime)-15s %(clientip)s %(user)-8s %(message)s'
logging.basicConfig(format=FORMAT)
d = {'clientip': '192.168.0.1', 'user': 'fbloggs'}
logger = logging.getLogger('tcpserver')
logger.warning('Protocol problem: %s', 'connection reset', extra=d)
would print something like 2006-02-08 22:20:02,165 192.168.0.1 fbloggs Protocol problem: connection reset
The keys in the dictionary passed in extra should not clash with the keys used by the logging system. (See the Formatter documentation for more information on which keys are used by the logging system.) If you choose to use these attributes in logged messages, you need to exercise some care. In the above example, for instance, the Formatter has been set up with a format string which expects ‘clientip’ and ‘user’ in the attribute dictionary of the LogRecord. If these are missing, the message will not be logged because a string formatting exception will occur. So in this case, you always need to pass the extra dictionary with these keys. While this might be annoying, this feature is intended for use in specialized circumstances, such as multi-threaded servers where the same code executes in many contexts, and interesting conditions which arise are dependent on this context (such as remote client IP address and authenticated user name, in the above example). In such circumstances, it is likely that specialized Formatters would be used with particular Handlers. Changed in version 3.2: The stack_info parameter was added. Changed in version 3.5: The exc_info parameter can now accept exception instances. Changed in version 3.8: The stacklevel parameter was added.
info(msg, *args, **kwargs)
Logs a message with level INFO on this logger. The arguments are interpreted as for debug().
warning(msg, *args, **kwargs)
Logs a message with level WARNING on this logger. The arguments are interpreted as for debug(). Note There is an obsolete method warn which is functionally identical to warning. As warn is deprecated, please do not use it - use warning instead.
error(msg, *args, **kwargs)
Logs a message with level ERROR on this logger. The arguments are interpreted as for debug().
critical(msg, *args, **kwargs)
Logs a message with level CRITICAL on this logger. The arguments are interpreted as for debug().
log(level, msg, *args, **kwargs)
Logs a message with integer level level on this logger. The other arguments are interpreted as for debug().
exception(msg, *args, **kwargs)
Logs a message with level ERROR on this logger. The arguments are interpreted as for debug(). Exception info is added to the logging message. This method should only be called from an exception handler.
addFilter(filter)
Adds the specified filter filter to this logger.
removeFilter(filter)
Removes the specified filter filter from this logger.
filter(record)
Apply this logger’s filters to the record and return True if the record is to be processed. The filters are consulted in turn, until one of them returns a false value. If none of them return a false value, the record will be processed (passed to handlers). If one returns a false value, no further processing of the record occurs.
addHandler(hdlr)
Adds the specified handler hdlr to this logger.
removeHandler(hdlr)
Removes the specified handler hdlr from this logger.
findCaller(stack_info=False, stacklevel=1)
Finds the caller’s source filename and line number. Returns the filename, line number, function name and stack information as a 4-element tuple. The stack information is returned as None unless stack_info is True. The stacklevel parameter is passed from code calling the debug() and other APIs. If greater than 1, the excess is used to skip stack frames before determining the values to be returned. This will generally be useful when calling logging APIs from helper/wrapper code, so that the information in the event log refers not to the helper/wrapper code, but to the code that calls it.
handle(record)
Handles a record by passing it to all handlers associated with this logger and its ancestors (until a false value of propagate is found). This method is used for unpickled records received from a socket, as well as those created locally. Logger-level filtering is applied using filter().
makeRecord(name, level, fn, lno, msg, args, exc_info, func=None, extra=None, sinfo=None)
This is a factory method which can be overridden in subclasses to create specialized LogRecord instances.
hasHandlers()
Checks to see if this logger has any handlers configured. This is done by looking for handlers in this logger and its parents in the logger hierarchy. Returns True if a handler was found, else False. The method stops searching up the hierarchy whenever a logger with the ‘propagate’ attribute set to false is found - that will be the last logger which is checked for the existence of handlers. New in version 3.2.
Changed in version 3.7: Loggers can now be pickled and unpickled.
Logging Levels The numeric values of logging levels are given in the following table. These are primarily of interest if you want to define your own levels, and need them to have specific values relative to the predefined levels. If you define a level with the same numeric value, it overwrites the predefined value; the predefined name is lost.
Level Numeric value
CRITICAL 50
ERROR 40
WARNING 30
INFO 20
DEBUG 10
NOTSET 0 Handler Objects Handlers have the following attributes and methods. Note that Handler is never instantiated directly; this class acts as a base for more useful subclasses. However, the __init__() method in subclasses needs to call Handler.__init__().
class logging.Handler
__init__(level=NOTSET)
Initializes the Handler instance by setting its level, setting the list of filters to the empty list and creating a lock (using createLock()) for serializing access to an I/O mechanism.
createLock()
Initializes a thread lock which can be used to serialize access to underlying I/O functionality which may not be threadsafe.
acquire()
Acquires the thread lock created with createLock().
release()
Releases the thread lock acquired with acquire().
setLevel(level)
Sets the threshold for this handler to level. Logging messages which are less severe than level will be ignored. When a handler is created, the level is set to NOTSET (which causes all messages to be processed). See Logging Levels for a list of levels. Changed in version 3.2: The level parameter now accepts a string representation of the level such as ‘INFO’ as an alternative to the integer constants such as INFO.
setFormatter(fmt)
Sets the Formatter for this handler to fmt.
addFilter(filter)
Adds the specified filter filter to this handler.
removeFilter(filter)
Removes the specified filter filter from this handler.
filter(record)
Apply this handler’s filters to the record and return True if the record is to be processed. The filters are consulted in turn, until one of them returns a false value. If none of them return a false value, the record will be emitted. If one returns a false value, the handler will not emit the record.
flush()
Ensure all logging output has been flushed. This version does nothing and is intended to be implemented by subclasses.
close()
Tidy up any resources used by the handler. This version does no output but removes the handler from an internal list of handlers which is closed when shutdown() is called. Subclasses should ensure that this gets called from overridden close() methods.
handle(record)
Conditionally emits the specified logging record, depending on filters which may have been added to the handler. Wraps the actual emission of the record with acquisition/release of the I/O thread lock.
handleError(record)
This method should be called from handlers when an exception is encountered during an emit() call. If the module-level attribute raiseExceptions is False, exceptions get silently ignored. This is what is mostly wanted for a logging system - most users will not care about errors in the logging system, they are more interested in application errors. You could, however, replace this with a custom handler if you wish. The specified record is the one which was being processed when the exception occurred. (The default value of raiseExceptions is True, as that is more useful during development).
format(record)
Do formatting for a record - if a formatter is set, use it. Otherwise, use the default formatter for the module.
emit(record)
Do whatever it takes to actually log the specified logging record. This version is intended to be implemented by subclasses and so raises a NotImplementedError.
For a list of handlers included as standard, see logging.handlers. Formatter Objects Formatter objects have the following attributes and methods. They are responsible for converting a LogRecord to (usually) a string which can be interpreted by either a human or an external system. The base Formatter allows a formatting string to be specified. If none is supplied, the default value of '%(message)s' is used, which just includes the message in the logging call. To have additional items of information in the formatted output (such as a timestamp), keep reading. A Formatter can be initialized with a format string which makes use of knowledge of the LogRecord attributes - such as the default value mentioned above making use of the fact that the user’s message and arguments are pre-formatted into a LogRecord’s message attribute. This format string contains standard Python %-style mapping keys. See section printf-style String Formatting for more information on string formatting. The useful mapping keys in a LogRecord are given in the section on LogRecord attributes.
class logging.Formatter(fmt=None, datefmt=None, style='%', validate=True)
Returns a new instance of the Formatter class. The instance is initialized with a format string for the message as a whole, as well as a format string for the date/time portion of a message. If no fmt is specified, '%(message)s' is used. If no datefmt is specified, a format is used which is described in the formatTime() documentation. The style parameter can be one of ‘%’, ‘{‘ or ‘$’ and determines how the format string will be merged with its data: using one of %-formatting, str.format() or string.Template. This only applies to the format string fmt (e.g. '%(message)s' or {message}), not to the actual log messages passed to Logger.debug etc; see Using particular formatting styles throughout your application for more information on using {- and $-formatting for log messages. Changed in version 3.2: The style parameter was added. Changed in version 3.8: The validate parameter was added. Incorrect or mismatched style and fmt will raise a ValueError. For example: logging.Formatter('%(asctime)s - %(message)s', style='{').
format(record)
The record’s attribute dictionary is used as the operand to a string formatting operation. Returns the resulting string. Before formatting the dictionary, a couple of preparatory steps are carried out. The message attribute of the record is computed using msg % args. If the formatting string contains '(asctime)', formatTime() is called to format the event time. If there is exception information, it is formatted using formatException() and appended to the message. Note that the formatted exception information is cached in attribute exc_text. This is useful because the exception information can be pickled and sent across the wire, but you should be careful if you have more than one Formatter subclass which customizes the formatting of exception information. In this case, you will have to clear the cached value after a formatter has done its formatting, so that the next formatter to handle the event doesn’t use the cached value but recalculates it afresh. If stack information is available, it’s appended after the exception information, using formatStack() to transform it if necessary.
formatTime(record, datefmt=None)
This method should be called from format() by a formatter which wants to make use of a formatted time. This method can be overridden in formatters to provide for any specific requirement, but the basic behavior is as follows: if datefmt (a string) is specified, it is used with time.strftime() to format the creation time of the record. Otherwise, the format ‘%Y-%m-%d %H:%M:%S,uuu’ is used, where the uuu part is a millisecond value and the other letters are as per the time.strftime() documentation. An example time in this format is 2003-01-23 00:29:50,411. The resulting string is returned. This function uses a user-configurable function to convert the creation time to a tuple. By default, time.localtime() is used; to change this for a particular formatter instance, set the converter attribute to a function with the same signature as time.localtime() or time.gmtime(). To change it for all formatters, for example if you want all logging times to be shown in GMT, set the converter attribute in the Formatter class. Changed in version 3.3: Previously, the default format was hard-coded as in this example: 2010-09-06 22:38:15,292 where the part before the comma is handled by a strptime format string ('%Y-%m-%d %H:%M:%S'), and the part after the comma is a millisecond value. Because strptime does not have a format placeholder for milliseconds, the millisecond value is appended using another format string, '%s,%03d' — and both of these format strings have been hardcoded into this method. With the change, these strings are defined as class-level attributes which can be overridden at the instance level when desired. The names of the attributes are default_time_format (for the strptime format string) and default_msec_format (for appending the millisecond value). Changed in version 3.9: The default_msec_format can be None.
formatException(exc_info)
Formats the specified exception information (a standard exception tuple as returned by sys.exc_info()) as a string. This default implementation just uses traceback.print_exception(). The resulting string is returned.
formatStack(stack_info)
Formats the specified stack information (a string as returned by traceback.print_stack(), but with the last newline removed) as a string. This default implementation just returns the input value.
Filter Objects Filters can be used by Handlers and Loggers for more sophisticated filtering than is provided by levels. The base filter class only allows events which are below a certain point in the logger hierarchy. For example, a filter initialized with ‘A.B’ will allow events logged by loggers ‘A.B’, ‘A.B.C’, ‘A.B.C.D’, ‘A.B.D’ etc. but not ‘A.BB’, ‘B.A.B’ etc. If initialized with the empty string, all events are passed.
class logging.Filter(name='')
Returns an instance of the Filter class. If name is specified, it names a logger which, together with its children, will have its events allowed through the filter. If name is the empty string, allows every event.
filter(record)
Is the specified record to be logged? Returns zero for no, nonzero for yes. If deemed appropriate, the record may be modified in-place by this method.
Note that filters attached to handlers are consulted before an event is emitted by the handler, whereas filters attached to loggers are consulted whenever an event is logged (using debug(), info(), etc.), before sending an event to handlers. This means that events which have been generated by descendant loggers will not be filtered by a logger’s filter setting, unless the filter has also been applied to those descendant loggers. You don’t actually need to subclass Filter: you can pass any instance which has a filter method with the same semantics. Changed in version 3.2: You don’t need to create specialized Filter classes, or use other classes with a filter method: you can use a function (or other callable) as a filter. The filtering logic will check to see if the filter object has a filter attribute: if it does, it’s assumed to be a Filter and its filter() method is called. Otherwise, it’s assumed to be a callable and called with the record as the single parameter. The returned value should conform to that returned by filter(). Although filters are used primarily to filter records based on more sophisticated criteria than levels, they get to see every record which is processed by the handler or logger they’re attached to: this can be useful if you want to do things like counting how many records were processed by a particular logger or handler, or adding, changing or removing attributes in the LogRecord being processed. Obviously changing the LogRecord needs to be done with some care, but it does allow the injection of contextual information into logs (see Using Filters to impart contextual information). LogRecord Objects LogRecord instances are created automatically by the Logger every time something is logged, and can be created manually via makeLogRecord() (for example, from a pickled event received over the wire).
class logging.LogRecord(name, level, pathname, lineno, msg, args, exc_info, func=None, sinfo=None)
Contains all the information pertinent to the event being logged. The primary information is passed in msg and args, which are combined using msg % args to create the message field of the record. Parameters
name – The name of the logger used to log the event represented by this LogRecord. Note that this name will always have this value, even though it may be emitted by a handler attached to a different (ancestor) logger.
level – The numeric level of the logging event (one of DEBUG, INFO etc.) Note that this is converted to two attributes of the LogRecord: levelno for the numeric value and levelname for the corresponding level name.
pathname – The full pathname of the source file where the logging call was made.
lineno – The line number in the source file where the logging call was made.
msg – The event description message, possibly a format string with placeholders for variable data.
args – Variable data to merge into the msg argument to obtain the event description.
exc_info – An exception tuple with the current exception information, or None if no exception information is available.
func – The name of the function or method from which the logging call was invoked.
sinfo – A text string representing stack information from the base of the stack in the current thread, up to the logging call.
getMessage()
Returns the message for this LogRecord instance after merging any user-supplied arguments with the message. If the user-supplied message argument to the logging call is not a string, str() is called on it to convert it to a string. This allows use of user-defined classes as messages, whose __str__ method can return the actual format string to be used.
Changed in version 3.2: The creation of a LogRecord has been made more configurable by providing a factory which is used to create the record. The factory can be set using getLogRecordFactory() and setLogRecordFactory() (see this for the factory’s signature). This functionality can be used to inject your own values into a LogRecord at creation time. You can use the following pattern: old_factory = logging.getLogRecordFactory()
def record_factory(*args, **kwargs):
record = old_factory(*args, **kwargs)
record.custom_attribute = 0xdecafbad
return record
logging.setLogRecordFactory(record_factory)
With this pattern, multiple factories could be chained, and as long as they don’t overwrite each other’s attributes or unintentionally overwrite the standard attributes listed above, there should be no surprises.
LogRecord attributes The LogRecord has a number of attributes, most of which are derived from the parameters to the constructor. (Note that the names do not always correspond exactly between the LogRecord constructor parameters and the LogRecord attributes.) These attributes can be used to merge data from the record into the format string. The following table lists (in alphabetical order) the attribute names, their meanings and the corresponding placeholder in a %-style format string. If you are using {}-formatting (str.format()), you can use {attrname} as the placeholder in the format string. If you are using $-formatting (string.Template), use the form ${attrname}. In both cases, of course, replace attrname with the actual attribute name you want to use. In the case of {}-formatting, you can specify formatting flags by placing them after the attribute name, separated from it with a colon. For example: a placeholder of {msecs:03d} would format a millisecond value of 4 as 004. Refer to the str.format() documentation for full details on the options available to you.
Attribute name Format Description
args You shouldn’t need to format this yourself. The tuple of arguments merged into msg to produce message, or a dict whose values are used for the merge (when there is only one argument, and it is a dictionary).
asctime %(asctime)s Human-readable time when the LogRecord was created. By default this is of the form ‘2003-07-08 16:49:45,896’ (the numbers after the comma are millisecond portion of the time).
created %(created)f Time when the LogRecord was created (as returned by time.time()).
exc_info You shouldn’t need to format this yourself. Exception tuple (à la sys.exc_info) or, if no exception has occurred, None.
filename %(filename)s Filename portion of pathname.
funcName %(funcName)s Name of function containing the logging call.
levelname %(levelname)s Text logging level for the message ('DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL').
levelno %(levelno)s Numeric logging level for the message (DEBUG, INFO, WARNING, ERROR, CRITICAL).
lineno %(lineno)d Source line number where the logging call was issued (if available).
message %(message)s The logged message, computed as msg %
args. This is set when Formatter.format() is invoked.
module %(module)s Module (name portion of filename).
msecs %(msecs)d Millisecond portion of the time when the LogRecord was created.
msg You shouldn’t need to format this yourself. The format string passed in the original logging call. Merged with args to produce message, or an arbitrary object (see Using arbitrary objects as messages).
name %(name)s Name of the logger used to log the call.
pathname %(pathname)s Full pathname of the source file where the logging call was issued (if available).
process %(process)d Process ID (if available).
processName %(processName)s Process name (if available).
relativeCreated %(relativeCreated)d Time in milliseconds when the LogRecord was created, relative to the time the logging module was loaded.
stack_info You shouldn’t need to format this yourself. Stack frame information (where available) from the bottom of the stack in the current thread, up to and including the stack frame of the logging call which resulted in the creation of this record.
thread %(thread)d Thread ID (if available).
threadName %(threadName)s Thread name (if available). Changed in version 3.1: processName was added. LoggerAdapter Objects LoggerAdapter instances are used to conveniently pass contextual information into logging calls. For a usage example, see the section on adding contextual information to your logging output.
class logging.LoggerAdapter(logger, extra)
Returns an instance of LoggerAdapter initialized with an underlying Logger instance and a dict-like object.
process(msg, kwargs)
Modifies the message and/or keyword arguments passed to a logging call in order to insert contextual information. This implementation takes the object passed as extra to the constructor and adds it to kwargs using key ‘extra’. The return value is a (msg, kwargs) tuple which has the (possibly modified) versions of the arguments passed in.
In addition to the above, LoggerAdapter supports the following methods of Logger: debug(), info(), warning(), error(), exception(), critical(), log(), isEnabledFor(), getEffectiveLevel(), setLevel() and hasHandlers(). These methods have the same signatures as their counterparts in Logger, so you can use the two types of instances interchangeably. Changed in version 3.2: The isEnabledFor(), getEffectiveLevel(), setLevel() and hasHandlers() methods were added to LoggerAdapter. These methods delegate to the underlying logger. Thread Safety The logging module is intended to be thread-safe without any special work needing to be done by its clients. It achieves this though using threading locks; there is one lock to serialize access to the module’s shared data, and each handler also creates a lock to serialize access to its underlying I/O. If you are implementing asynchronous signal handlers using the signal module, you may not be able to use logging from within such handlers. This is because lock implementations in the threading module are not always re-entrant, and so cannot be invoked from such signal handlers. Module-Level Functions In addition to the classes described above, there are a number of module-level functions.
logging.getLogger(name=None)
Return a logger with the specified name or, if name is None, return a logger which is the root logger of the hierarchy. If specified, the name is typically a dot-separated hierarchical name like ‘a’, ‘a.b’ or ‘a.b.c.d’. Choice of these names is entirely up to the developer who is using logging. All calls to this function with a given name return the same logger instance. This means that logger instances never need to be passed between different parts of an application.
logging.getLoggerClass()
Return either the standard Logger class, or the last class passed to setLoggerClass(). This function may be called from within a new class definition, to ensure that installing a customized Logger class will not undo customizations already applied by other code. For example: class MyLogger(logging.getLoggerClass()):
# ... override behaviour here
logging.getLogRecordFactory()
Return a callable which is used to create a LogRecord. New in version 3.2: This function has been provided, along with setLogRecordFactory(), to allow developers more control over how the LogRecord representing a logging event is constructed. See setLogRecordFactory() for more information about the how the factory is called.
logging.debug(msg, *args, **kwargs)
Logs a message with level DEBUG on the root logger. The msg is the message format string, and the args are the arguments which are merged into msg using the string formatting operator. (Note that this means that you can use keywords in the format string, together with a single dictionary argument.) There are three keyword arguments in kwargs which are inspected: exc_info which, if it does not evaluate as false, causes exception information to be added to the logging message. If an exception tuple (in the format returned by sys.exc_info()) or an exception instance is provided, it is used; otherwise, sys.exc_info() is called to get the exception information. The second optional keyword argument is stack_info, which defaults to False. If true, stack information is added to the logging message, including the actual logging call. Note that this is not the same stack information as that displayed through specifying exc_info: The former is stack frames from the bottom of the stack up to the logging call in the current thread, whereas the latter is information about stack frames which have been unwound, following an exception, while searching for exception handlers. You can specify stack_info independently of exc_info, e.g. to just show how you got to a certain point in your code, even when no exceptions were raised. The stack frames are printed following a header line which says: Stack (most recent call last):
This mimics the Traceback (most recent call last): which is used when displaying exception frames. The third optional keyword argument is extra which can be used to pass a dictionary which is used to populate the __dict__ of the LogRecord created for the logging event with user-defined attributes. These custom attributes can then be used as you like. For example, they could be incorporated into logged messages. For example: FORMAT = '%(asctime)-15s %(clientip)s %(user)-8s %(message)s'
logging.basicConfig(format=FORMAT)
d = {'clientip': '192.168.0.1', 'user': 'fbloggs'}
logging.warning('Protocol problem: %s', 'connection reset', extra=d)
would print something like: 2006-02-08 22:20:02,165 192.168.0.1 fbloggs Protocol problem: connection reset
The keys in the dictionary passed in extra should not clash with the keys used by the logging system. (See the Formatter documentation for more information on which keys are used by the logging system.) If you choose to use these attributes in logged messages, you need to exercise some care. In the above example, for instance, the Formatter has been set up with a format string which expects ‘clientip’ and ‘user’ in the attribute dictionary of the LogRecord. If these are missing, the message will not be logged because a string formatting exception will occur. So in this case, you always need to pass the extra dictionary with these keys. While this might be annoying, this feature is intended for use in specialized circumstances, such as multi-threaded servers where the same code executes in many contexts, and interesting conditions which arise are dependent on this context (such as remote client IP address and authenticated user name, in the above example). In such circumstances, it is likely that specialized Formatters would be used with particular Handlers. Changed in version 3.2: The stack_info parameter was added.
logging.info(msg, *args, **kwargs)
Logs a message with level INFO on the root logger. The arguments are interpreted as for debug().
logging.warning(msg, *args, **kwargs)
Logs a message with level WARNING on the root logger. The arguments are interpreted as for debug(). Note There is an obsolete function warn which is functionally identical to warning. As warn is deprecated, please do not use it - use warning instead.
logging.error(msg, *args, **kwargs)
Logs a message with level ERROR on the root logger. The arguments are interpreted as for debug().
logging.critical(msg, *args, **kwargs)
Logs a message with level CRITICAL on the root logger. The arguments are interpreted as for debug().
logging.exception(msg, *args, **kwargs)
Logs a message with level ERROR on the root logger. The arguments are interpreted as for debug(). Exception info is added to the logging message. This function should only be called from an exception handler.
logging.log(level, msg, *args, **kwargs)
Logs a message with level level on the root logger. The other arguments are interpreted as for debug(). Note The above module-level convenience functions, which delegate to the root logger, call basicConfig() to ensure that at least one handler is available. Because of this, they should not be used in threads, in versions of Python earlier than 2.7.1 and 3.2, unless at least one handler has been added to the root logger before the threads are started. In earlier versions of Python, due to a thread safety shortcoming in basicConfig(), this can (under rare circumstances) lead to handlers being added multiple times to the root logger, which can in turn lead to multiple messages for the same event.
logging.disable(level=CRITICAL)
Provides an overriding level level for all loggers which takes precedence over the logger’s own level. When the need arises to temporarily throttle logging output down across the whole application, this function can be useful. Its effect is to disable all logging calls of severity level and below, so that if you call it with a value of INFO, then all INFO and DEBUG events would be discarded, whereas those of severity WARNING and above would be processed according to the logger’s effective level. If logging.disable(logging.NOTSET) is called, it effectively removes this overriding level, so that logging output again depends on the effective levels of individual loggers. Note that if you have defined any custom logging level higher than CRITICAL (this is not recommended), you won’t be able to rely on the default value for the level parameter, but will have to explicitly supply a suitable value. Changed in version 3.7: The level parameter was defaulted to level CRITICAL. See bpo-28524 for more information about this change.
logging.addLevelName(level, levelName)
Associates level level with text levelName in an internal dictionary, which is used to map numeric levels to a textual representation, for example when a Formatter formats a message. This function can also be used to define your own levels. The only constraints are that all levels used must be registered using this function, levels should be positive integers and they should increase in increasing order of severity. Note If you are thinking of defining your own levels, please see the section on Custom Levels.
logging.getLevelName(level)
Returns the textual or numeric representation of logging level level. If level is one of the predefined levels CRITICAL, ERROR, WARNING, INFO or DEBUG then you get the corresponding string. If you have associated levels with names using addLevelName() then the name you have associated with level is returned. If a numeric value corresponding to one of the defined levels is passed in, the corresponding string representation is returned. The level parameter also accepts a string representation of the level such as ‘INFO’. In such cases, this functions returns the corresponding numeric value of the level. If no matching numeric or string value is passed in, the string ‘Level %s’ % level is returned. Note Levels are internally integers (as they need to be compared in the logging logic). This function is used to convert between an integer level and the level name displayed in the formatted log output by means of the %(levelname)s format specifier (see LogRecord attributes), and vice versa. Changed in version 3.4: In Python versions earlier than 3.4, this function could also be passed a text level, and would return the corresponding numeric value of the level. This undocumented behaviour was considered a mistake, and was removed in Python 3.4, but reinstated in 3.4.2 due to retain backward compatibility.
logging.makeLogRecord(attrdict)
Creates and returns a new LogRecord instance whose attributes are defined by attrdict. This function is useful for taking a pickled LogRecord attribute dictionary, sent over a socket, and reconstituting it as a LogRecord instance at the receiving end.
logging.basicConfig(**kwargs)
Does basic configuration for the logging system by creating a StreamHandler with a default Formatter and adding it to the root logger. The functions debug(), info(), warning(), error() and critical() will call basicConfig() automatically if no handlers are defined for the root logger. This function does nothing if the root logger already has handlers configured, unless the keyword argument force is set to True. Note This function should be called from the main thread before other threads are started. In versions of Python prior to 2.7.1 and 3.2, if this function is called from multiple threads, it is possible (in rare circumstances) that a handler will be added to the root logger more than once, leading to unexpected results such as messages being duplicated in the log. The following keyword arguments are supported.
Format Description
filename Specifies that a FileHandler be created, using the specified filename, rather than a StreamHandler.
filemode If filename is specified, open the file in this mode. Defaults to 'a'.
format Use the specified format string for the handler. Defaults to attributes levelname, name and message separated by colons.
datefmt Use the specified date/time format, as accepted by time.strftime().
style If format is specified, use this style for the format string. One of '%', '{' or '$' for printf-style, str.format() or string.Template respectively. Defaults to '%'.
level Set the root logger level to the specified level.
stream Use the specified stream to initialize the StreamHandler. Note that this argument is incompatible with filename - if both are present, a ValueError is raised.
handlers If specified, this should be an iterable of already created handlers to add to the root logger. Any handlers which don’t already have a formatter set will be assigned the default formatter created in this function. Note that this argument is incompatible with filename or stream - if both are present, a ValueError is raised.
force If this keyword argument is specified as true, any existing handlers attached to the root logger are removed and closed, before carrying out the configuration as specified by the other arguments.
encoding If this keyword argument is specified along with filename, its value is used when the FileHandler is created, and thus used when opening the output file.
errors If this keyword argument is specified along with filename, its value is used when the FileHandler is created, and thus used when opening the output file. If not specified, the value ‘backslashreplace’ is used. Note that if None is specified, it will be passed as such to func:open, which means that it will be treated the same as passing ‘errors’. Changed in version 3.2: The style argument was added. Changed in version 3.3: The handlers argument was added. Additional checks were added to catch situations where incompatible arguments are specified (e.g. handlers together with stream or filename, or stream together with filename). Changed in version 3.8: The force argument was added. Changed in version 3.9: The encoding and errors arguments were added.
logging.shutdown()
Informs the logging system to perform an orderly shutdown by flushing and closing all handlers. This should be called at application exit and no further use of the logging system should be made after this call. When the logging module is imported, it registers this function as an exit handler (see atexit), so normally there’s no need to do that manually.
logging.setLoggerClass(klass)
Tells the logging system to use the class klass when instantiating a logger. The class should define __init__() such that only a name argument is required, and the __init__() should call Logger.__init__(). This function is typically called before any loggers are instantiated by applications which need to use custom logger behavior. After this call, as at any other time, do not instantiate loggers directly using the subclass: continue to use the logging.getLogger() API to get your loggers.
logging.setLogRecordFactory(factory)
Set a callable which is used to create a LogRecord. Parameters
factory – The factory callable to be used to instantiate a log record. New in version 3.2: This function has been provided, along with getLogRecordFactory(), to allow developers more control over how the LogRecord representing a logging event is constructed. The factory has the following signature: factory(name, level, fn, lno, msg, args, exc_info, func=None, sinfo=None, **kwargs) name
The logger name. level
The logging level (numeric). fn
The full pathname of the file where the logging call was made. lno
The line number in the file where the logging call was made. msg
The logging message. args
The arguments for the logging message. exc_info
An exception tuple, or None. func
The name of the function or method which invoked the logging call. sinfo
A stack traceback such as is provided by traceback.print_stack(), showing the call hierarchy. kwargs
Additional keyword arguments.
Module-Level Attributes
logging.lastResort
A “handler of last resort” is available through this attribute. This is a StreamHandler writing to sys.stderr with a level of WARNING, and is used to handle logging events in the absence of any logging configuration. The end result is to just print the message to sys.stderr. This replaces the earlier error message saying that “no handlers could be found for logger XYZ”. If you need the earlier behaviour for some reason, lastResort can be set to None. New in version 3.2.
Integration with the warnings module The captureWarnings() function can be used to integrate logging with the warnings module.
logging.captureWarnings(capture)
This function is used to turn the capture of warnings by logging on and off. If capture is True, warnings issued by the warnings module will be redirected to the logging system. Specifically, a warning will be formatted using warnings.formatwarning() and the resulting string logged to a logger named 'py.warnings' with a severity of WARNING. If capture is False, the redirection of warnings to the logging system will stop, and warnings will be redirected to their original destinations (i.e. those in effect before captureWarnings(True) was called).
See also
Module logging.config
Configuration API for the logging module.
Module logging.handlers
Useful handlers included with the logging module.
PEP 282 - A Logging System
The proposal which described this feature for inclusion in the Python standard library. Original Python logging package
This is the original source for the logging package. The version of the package available from this site is suitable for use with Python 1.5.2, 2.1.x and 2.2.x, which do not include the logging package in the standard library. | python.library.logging |
logging.addLevelName(level, levelName)
Associates level level with text levelName in an internal dictionary, which is used to map numeric levels to a textual representation, for example when a Formatter formats a message. This function can also be used to define your own levels. The only constraints are that all levels used must be registered using this function, levels should be positive integers and they should increase in increasing order of severity. Note If you are thinking of defining your own levels, please see the section on Custom Levels. | python.library.logging#logging.addLevelName |
logging.basicConfig(**kwargs)
Does basic configuration for the logging system by creating a StreamHandler with a default Formatter and adding it to the root logger. The functions debug(), info(), warning(), error() and critical() will call basicConfig() automatically if no handlers are defined for the root logger. This function does nothing if the root logger already has handlers configured, unless the keyword argument force is set to True. Note This function should be called from the main thread before other threads are started. In versions of Python prior to 2.7.1 and 3.2, if this function is called from multiple threads, it is possible (in rare circumstances) that a handler will be added to the root logger more than once, leading to unexpected results such as messages being duplicated in the log. The following keyword arguments are supported.
Format Description
filename Specifies that a FileHandler be created, using the specified filename, rather than a StreamHandler.
filemode If filename is specified, open the file in this mode. Defaults to 'a'.
format Use the specified format string for the handler. Defaults to attributes levelname, name and message separated by colons.
datefmt Use the specified date/time format, as accepted by time.strftime().
style If format is specified, use this style for the format string. One of '%', '{' or '$' for printf-style, str.format() or string.Template respectively. Defaults to '%'.
level Set the root logger level to the specified level.
stream Use the specified stream to initialize the StreamHandler. Note that this argument is incompatible with filename - if both are present, a ValueError is raised.
handlers If specified, this should be an iterable of already created handlers to add to the root logger. Any handlers which don’t already have a formatter set will be assigned the default formatter created in this function. Note that this argument is incompatible with filename or stream - if both are present, a ValueError is raised.
force If this keyword argument is specified as true, any existing handlers attached to the root logger are removed and closed, before carrying out the configuration as specified by the other arguments.
encoding If this keyword argument is specified along with filename, its value is used when the FileHandler is created, and thus used when opening the output file.
errors If this keyword argument is specified along with filename, its value is used when the FileHandler is created, and thus used when opening the output file. If not specified, the value ‘backslashreplace’ is used. Note that if None is specified, it will be passed as such to func:open, which means that it will be treated the same as passing ‘errors’. Changed in version 3.2: The style argument was added. Changed in version 3.3: The handlers argument was added. Additional checks were added to catch situations where incompatible arguments are specified (e.g. handlers together with stream or filename, or stream together with filename). Changed in version 3.8: The force argument was added. Changed in version 3.9: The encoding and errors arguments were added. | python.library.logging#logging.basicConfig |
logging.captureWarnings(capture)
This function is used to turn the capture of warnings by logging on and off. If capture is True, warnings issued by the warnings module will be redirected to the logging system. Specifically, a warning will be formatted using warnings.formatwarning() and the resulting string logged to a logger named 'py.warnings' with a severity of WARNING. If capture is False, the redirection of warnings to the logging system will stop, and warnings will be redirected to their original destinations (i.e. those in effect before captureWarnings(True) was called). | python.library.logging#logging.captureWarnings |
logging.config — Logging configuration Source code: Lib/logging/config.py Important This page contains only reference information. For tutorials, please see Basic Tutorial Advanced Tutorial Logging Cookbook This section describes the API for configuring the logging module. Configuration functions The following functions configure the logging module. They are located in the 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 Configuration dictionary schema below. If an error is encountered during configuration, this function will raise a ValueError, TypeError, AttributeError or ImportError with a suitably descriptive message. The following is a (possibly incomplete) list of conditions which will raise an error: A level which is not a string or which is a string not corresponding to an actual logging level. A propagate value 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 DictConfigurator class, whose constructor is passed the dictionary used for configuration, and has a configure() method. The logging.config module has a callable attribute dictConfigClass which is initially set to DictConfigurator. You can replace the value of dictConfigClass with a suitable implementation of your own. dictConfig() calls dictConfigClass passing the specified dictionary, and then calls the configure() method on the returned object to put the configuration into effect: def dictConfig(config):
dictConfigClass(config).configure()
For example, a subclass of DictConfigurator could call DictConfigurator.__init__() in its own __init__(), then set up custom prefixes which would be usable in the subsequent configure() call. dictConfigClass would be bound to this new subclass, and then dictConfig() could be called exactly as in the default, uncustomized state. New in version 3.2.
logging.config.fileConfig(fname, defaults=None, disable_existing_loggers=True)
Reads the logging configuration from a configparser-format file. The format of the file should be as described in Configuration file format. 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). Parameters
fname – A filename, or a file-like object, or an instance derived from RawConfigParser. If a RawConfigParser-derived instance is passed, it is used as is. Otherwise, a Configparser is instantiated, and the configuration read by it from the object passed in fname. If that has a readline() method, it is assumed to be a file-like object and read using read_file(); otherwise, it is assumed to be a filename and passed to read().
defaults – Defaults to be passed to the ConfigParser can be specified in this argument.
disable_existing_loggers – If specified as False, loggers which exist when this call is made are left enabled. The default is True because this enables old behaviour in a backward-compatible way. This behaviour is to disable any existing non-root loggers unless they or their ancestors are explicitly named in the logging configuration. Changed in version 3.4: An instance of a subclass of RawConfigParser is now accepted as a value for fname. This facilitates: Use of a configuration file where logging configuration is just part of the overall application configuration. Use of a configuration read from a file, and then modified by the using application (e.g. based on command-line parameters or other aspects of the runtime environment) before being passed to 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 dictConfig() or fileConfig(). Returns a 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 sequence of bytes preceded by a four-byte length string packed in binary using struct.pack('>L', n). Note 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 argument to listen() to prevent unrecognised configurations from being applied. Changed in version 3.4: The verify argument was added. Note If you want to send configurations to the listener which don’t disable existing loggers, you will need to use a JSON format for the configuration, which will use dictConfig() for configuration. This method allows you to specify disable_existing_loggers as False in the configuration you send.
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().
Configuration dictionary schema 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 Object connections below. Dictionary Schema Details The dictionary passed to dictConfig() must contain the following keys:
version - to be set to an integer value representing the schema version. The only valid value at present is 1, but having this key allows the schema to evolve while still preserving backwards compatibility. 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 User-defined objects 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 instance. The configuring dict is searched for keys format and datefmt (with defaults of None) and these are used to construct a Formatter instance. Changed in version 3.8: a validate key (with default of True) can be added into the formatters section of the configuring dict, this is to validate the format.
filters - 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 instance.
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. All 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
file:
class : logging.handlers.RotatingFileHandler
formatter: precise
filename: logconfig.log
maxBytes: 1024
backupCount: 3
the handler with id console is instantiated as a logging.StreamHandler, using 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 Incremental Configuration.
disable_existing_loggers - whether any existing non-root 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. Incremental Configuration 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 entries. 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. Object connections 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. User-defined objects 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 '()'. Here’s a concrete example: 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. Access to external objects 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. Access to internal objects 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' for a level in a logger or handler will automatically be converted to the value logging.DEBUG, and the 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
The literal string '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. Thus, given the following snippet: 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, the string 'cfg://handlers.email would resolve to the dict with key email in the 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'. The 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 and custom importers 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 attribute of the 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(). For example: 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. Configuration file format The configuration file format understood by fileConfig() is based on configparser functionality. The file must contain sections called [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 in the [loggers] section, the relevant configuration details are held in a section [logger_log01]. Similarly, a handler called hand01 in the [handlers] section will have its configuration held in a section called [handler_hand01], while a formatter called form01 in the [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]. Note 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() in the 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. If not provided, it defaults to (). The optional kwargs entry, when eval()uated in the context of the logging package’s namespace, is the keyword argument dict to the constructor for the handler class. If not provided, it defaults to {}. [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')
kwargs={'timeout': 10.0}
[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')
kwargs={'secure': True}
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 something which is almost equivalent to specifying the date format string '%Y-%m-%d %H:%M:%S'. This format also specifies milliseconds, which are appended to the result of using the above format string, with a comma separator. An example time in this 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. Note 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() documentation for more information. See also
Module logging
API reference for the logging module.
Module logging.handlers
Useful handlers included with the logging module. | python.library.logging.config |
logging.config.dictConfig(config)
Takes the logging configuration from a dictionary. The contents of this dictionary are described in Configuration dictionary schema below. If an error is encountered during configuration, this function will raise a ValueError, TypeError, AttributeError or ImportError with a suitably descriptive message. The following is a (possibly incomplete) list of conditions which will raise an error: A level which is not a string or which is a string not corresponding to an actual logging level. A propagate value 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 DictConfigurator class, whose constructor is passed the dictionary used for configuration, and has a configure() method. The logging.config module has a callable attribute dictConfigClass which is initially set to DictConfigurator. You can replace the value of dictConfigClass with a suitable implementation of your own. dictConfig() calls dictConfigClass passing the specified dictionary, and then calls the configure() method on the returned object to put the configuration into effect: def dictConfig(config):
dictConfigClass(config).configure()
For example, a subclass of DictConfigurator could call DictConfigurator.__init__() in its own __init__(), then set up custom prefixes which would be usable in the subsequent configure() call. dictConfigClass would be bound to this new subclass, and then dictConfig() could be called exactly as in the default, uncustomized state. New in version 3.2. | python.library.logging.config#logging.config.dictConfig |
logging.config.fileConfig(fname, defaults=None, disable_existing_loggers=True)
Reads the logging configuration from a configparser-format file. The format of the file should be as described in Configuration file format. 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). Parameters
fname – A filename, or a file-like object, or an instance derived from RawConfigParser. If a RawConfigParser-derived instance is passed, it is used as is. Otherwise, a Configparser is instantiated, and the configuration read by it from the object passed in fname. If that has a readline() method, it is assumed to be a file-like object and read using read_file(); otherwise, it is assumed to be a filename and passed to read().
defaults – Defaults to be passed to the ConfigParser can be specified in this argument.
disable_existing_loggers – If specified as False, loggers which exist when this call is made are left enabled. The default is True because this enables old behaviour in a backward-compatible way. This behaviour is to disable any existing non-root loggers unless they or their ancestors are explicitly named in the logging configuration. Changed in version 3.4: An instance of a subclass of RawConfigParser is now accepted as a value for fname. This facilitates: Use of a configuration file where logging configuration is just part of the overall application configuration. Use of a configuration read from a file, and then modified by the using application (e.g. based on command-line parameters or other aspects of the runtime environment) before being passed to fileConfig. | python.library.logging.config#logging.config.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 dictConfig() or fileConfig(). Returns a 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 sequence of bytes preceded by a four-byte length string packed in binary using struct.pack('>L', n). Note 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 argument to listen() to prevent unrecognised configurations from being applied. Changed in version 3.4: The verify argument was added. Note If you want to send configurations to the listener which don’t disable existing loggers, you will need to use a JSON format for the configuration, which will use dictConfig() for configuration. This method allows you to specify disable_existing_loggers as False in the configuration you send. | python.library.logging.config#logging.config.listen |
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(). | python.library.logging.config#logging.config.stopListening |
logging.critical(msg, *args, **kwargs)
Logs a message with level CRITICAL on the root logger. The arguments are interpreted as for debug(). | python.library.logging#logging.critical |
logging.debug(msg, *args, **kwargs)
Logs a message with level DEBUG on the root logger. The msg is the message format string, and the args are the arguments which are merged into msg using the string formatting operator. (Note that this means that you can use keywords in the format string, together with a single dictionary argument.) There are three keyword arguments in kwargs which are inspected: exc_info which, if it does not evaluate as false, causes exception information to be added to the logging message. If an exception tuple (in the format returned by sys.exc_info()) or an exception instance is provided, it is used; otherwise, sys.exc_info() is called to get the exception information. The second optional keyword argument is stack_info, which defaults to False. If true, stack information is added to the logging message, including the actual logging call. Note that this is not the same stack information as that displayed through specifying exc_info: The former is stack frames from the bottom of the stack up to the logging call in the current thread, whereas the latter is information about stack frames which have been unwound, following an exception, while searching for exception handlers. You can specify stack_info independently of exc_info, e.g. to just show how you got to a certain point in your code, even when no exceptions were raised. The stack frames are printed following a header line which says: Stack (most recent call last):
This mimics the Traceback (most recent call last): which is used when displaying exception frames. The third optional keyword argument is extra which can be used to pass a dictionary which is used to populate the __dict__ of the LogRecord created for the logging event with user-defined attributes. These custom attributes can then be used as you like. For example, they could be incorporated into logged messages. For example: FORMAT = '%(asctime)-15s %(clientip)s %(user)-8s %(message)s'
logging.basicConfig(format=FORMAT)
d = {'clientip': '192.168.0.1', 'user': 'fbloggs'}
logging.warning('Protocol problem: %s', 'connection reset', extra=d)
would print something like: 2006-02-08 22:20:02,165 192.168.0.1 fbloggs Protocol problem: connection reset
The keys in the dictionary passed in extra should not clash with the keys used by the logging system. (See the Formatter documentation for more information on which keys are used by the logging system.) If you choose to use these attributes in logged messages, you need to exercise some care. In the above example, for instance, the Formatter has been set up with a format string which expects ‘clientip’ and ‘user’ in the attribute dictionary of the LogRecord. If these are missing, the message will not be logged because a string formatting exception will occur. So in this case, you always need to pass the extra dictionary with these keys. While this might be annoying, this feature is intended for use in specialized circumstances, such as multi-threaded servers where the same code executes in many contexts, and interesting conditions which arise are dependent on this context (such as remote client IP address and authenticated user name, in the above example). In such circumstances, it is likely that specialized Formatters would be used with particular Handlers. Changed in version 3.2: The stack_info parameter was added. | python.library.logging#logging.debug |
logging.disable(level=CRITICAL)
Provides an overriding level level for all loggers which takes precedence over the logger’s own level. When the need arises to temporarily throttle logging output down across the whole application, this function can be useful. Its effect is to disable all logging calls of severity level and below, so that if you call it with a value of INFO, then all INFO and DEBUG events would be discarded, whereas those of severity WARNING and above would be processed according to the logger’s effective level. If logging.disable(logging.NOTSET) is called, it effectively removes this overriding level, so that logging output again depends on the effective levels of individual loggers. Note that if you have defined any custom logging level higher than CRITICAL (this is not recommended), you won’t be able to rely on the default value for the level parameter, but will have to explicitly supply a suitable value. Changed in version 3.7: The level parameter was defaulted to level CRITICAL. See bpo-28524 for more information about this change. | python.library.logging#logging.disable |
logging.error(msg, *args, **kwargs)
Logs a message with level ERROR on the root logger. The arguments are interpreted as for debug(). | python.library.logging#logging.error |
logging.exception(msg, *args, **kwargs)
Logs a message with level ERROR on the root logger. The arguments are interpreted as for debug(). Exception info is added to the logging message. This function should only be called from an exception handler. | python.library.logging#logging.exception |
class logging.FileHandler(filename, mode='a', encoding=None, delay=False, errors=None)
Returns a new instance of the FileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely. If errors is specified, it’s used to determine how encoding errors are handled. Changed in version 3.6: As well as string values, Path objects are also accepted for the filename argument. Changed in version 3.9: The errors parameter was added.
close()
Closes the file.
emit(record)
Outputs the record to the file. | python.library.logging.handlers#logging.FileHandler |
close()
Closes the file. | python.library.logging.handlers#logging.FileHandler.close |
emit(record)
Outputs the record to the file. | python.library.logging.handlers#logging.FileHandler.emit |
class logging.Filter(name='')
Returns an instance of the Filter class. If name is specified, it names a logger which, together with its children, will have its events allowed through the filter. If name is the empty string, allows every event.
filter(record)
Is the specified record to be logged? Returns zero for no, nonzero for yes. If deemed appropriate, the record may be modified in-place by this method. | python.library.logging#logging.Filter |
filter(record)
Is the specified record to be logged? Returns zero for no, nonzero for yes. If deemed appropriate, the record may be modified in-place by this method. | python.library.logging#logging.Filter.filter |
class logging.Formatter(fmt=None, datefmt=None, style='%', validate=True)
Returns a new instance of the Formatter class. The instance is initialized with a format string for the message as a whole, as well as a format string for the date/time portion of a message. If no fmt is specified, '%(message)s' is used. If no datefmt is specified, a format is used which is described in the formatTime() documentation. The style parameter can be one of ‘%’, ‘{‘ or ‘$’ and determines how the format string will be merged with its data: using one of %-formatting, str.format() or string.Template. This only applies to the format string fmt (e.g. '%(message)s' or {message}), not to the actual log messages passed to Logger.debug etc; see Using particular formatting styles throughout your application for more information on using {- and $-formatting for log messages. Changed in version 3.2: The style parameter was added. Changed in version 3.8: The validate parameter was added. Incorrect or mismatched style and fmt will raise a ValueError. For example: logging.Formatter('%(asctime)s - %(message)s', style='{').
format(record)
The record’s attribute dictionary is used as the operand to a string formatting operation. Returns the resulting string. Before formatting the dictionary, a couple of preparatory steps are carried out. The message attribute of the record is computed using msg % args. If the formatting string contains '(asctime)', formatTime() is called to format the event time. If there is exception information, it is formatted using formatException() and appended to the message. Note that the formatted exception information is cached in attribute exc_text. This is useful because the exception information can be pickled and sent across the wire, but you should be careful if you have more than one Formatter subclass which customizes the formatting of exception information. In this case, you will have to clear the cached value after a formatter has done its formatting, so that the next formatter to handle the event doesn’t use the cached value but recalculates it afresh. If stack information is available, it’s appended after the exception information, using formatStack() to transform it if necessary.
formatTime(record, datefmt=None)
This method should be called from format() by a formatter which wants to make use of a formatted time. This method can be overridden in formatters to provide for any specific requirement, but the basic behavior is as follows: if datefmt (a string) is specified, it is used with time.strftime() to format the creation time of the record. Otherwise, the format ‘%Y-%m-%d %H:%M:%S,uuu’ is used, where the uuu part is a millisecond value and the other letters are as per the time.strftime() documentation. An example time in this format is 2003-01-23 00:29:50,411. The resulting string is returned. This function uses a user-configurable function to convert the creation time to a tuple. By default, time.localtime() is used; to change this for a particular formatter instance, set the converter attribute to a function with the same signature as time.localtime() or time.gmtime(). To change it for all formatters, for example if you want all logging times to be shown in GMT, set the converter attribute in the Formatter class. Changed in version 3.3: Previously, the default format was hard-coded as in this example: 2010-09-06 22:38:15,292 where the part before the comma is handled by a strptime format string ('%Y-%m-%d %H:%M:%S'), and the part after the comma is a millisecond value. Because strptime does not have a format placeholder for milliseconds, the millisecond value is appended using another format string, '%s,%03d' — and both of these format strings have been hardcoded into this method. With the change, these strings are defined as class-level attributes which can be overridden at the instance level when desired. The names of the attributes are default_time_format (for the strptime format string) and default_msec_format (for appending the millisecond value). Changed in version 3.9: The default_msec_format can be None.
formatException(exc_info)
Formats the specified exception information (a standard exception tuple as returned by sys.exc_info()) as a string. This default implementation just uses traceback.print_exception(). The resulting string is returned.
formatStack(stack_info)
Formats the specified stack information (a string as returned by traceback.print_stack(), but with the last newline removed) as a string. This default implementation just returns the input value. | python.library.logging#logging.Formatter |
format(record)
The record’s attribute dictionary is used as the operand to a string formatting operation. Returns the resulting string. Before formatting the dictionary, a couple of preparatory steps are carried out. The message attribute of the record is computed using msg % args. If the formatting string contains '(asctime)', formatTime() is called to format the event time. If there is exception information, it is formatted using formatException() and appended to the message. Note that the formatted exception information is cached in attribute exc_text. This is useful because the exception information can be pickled and sent across the wire, but you should be careful if you have more than one Formatter subclass which customizes the formatting of exception information. In this case, you will have to clear the cached value after a formatter has done its formatting, so that the next formatter to handle the event doesn’t use the cached value but recalculates it afresh. If stack information is available, it’s appended after the exception information, using formatStack() to transform it if necessary. | python.library.logging#logging.Formatter.format |
formatException(exc_info)
Formats the specified exception information (a standard exception tuple as returned by sys.exc_info()) as a string. This default implementation just uses traceback.print_exception(). The resulting string is returned. | python.library.logging#logging.Formatter.formatException |
formatStack(stack_info)
Formats the specified stack information (a string as returned by traceback.print_stack(), but with the last newline removed) as a string. This default implementation just returns the input value. | python.library.logging#logging.Formatter.formatStack |
formatTime(record, datefmt=None)
This method should be called from format() by a formatter which wants to make use of a formatted time. This method can be overridden in formatters to provide for any specific requirement, but the basic behavior is as follows: if datefmt (a string) is specified, it is used with time.strftime() to format the creation time of the record. Otherwise, the format ‘%Y-%m-%d %H:%M:%S,uuu’ is used, where the uuu part is a millisecond value and the other letters are as per the time.strftime() documentation. An example time in this format is 2003-01-23 00:29:50,411. The resulting string is returned. This function uses a user-configurable function to convert the creation time to a tuple. By default, time.localtime() is used; to change this for a particular formatter instance, set the converter attribute to a function with the same signature as time.localtime() or time.gmtime(). To change it for all formatters, for example if you want all logging times to be shown in GMT, set the converter attribute in the Formatter class. Changed in version 3.3: Previously, the default format was hard-coded as in this example: 2010-09-06 22:38:15,292 where the part before the comma is handled by a strptime format string ('%Y-%m-%d %H:%M:%S'), and the part after the comma is a millisecond value. Because strptime does not have a format placeholder for milliseconds, the millisecond value is appended using another format string, '%s,%03d' — and both of these format strings have been hardcoded into this method. With the change, these strings are defined as class-level attributes which can be overridden at the instance level when desired. The names of the attributes are default_time_format (for the strptime format string) and default_msec_format (for appending the millisecond value). Changed in version 3.9: The default_msec_format can be None. | python.library.logging#logging.Formatter.formatTime |
logging.getLevelName(level)
Returns the textual or numeric representation of logging level level. If level is one of the predefined levels CRITICAL, ERROR, WARNING, INFO or DEBUG then you get the corresponding string. If you have associated levels with names using addLevelName() then the name you have associated with level is returned. If a numeric value corresponding to one of the defined levels is passed in, the corresponding string representation is returned. The level parameter also accepts a string representation of the level such as ‘INFO’. In such cases, this functions returns the corresponding numeric value of the level. If no matching numeric or string value is passed in, the string ‘Level %s’ % level is returned. Note Levels are internally integers (as they need to be compared in the logging logic). This function is used to convert between an integer level and the level name displayed in the formatted log output by means of the %(levelname)s format specifier (see LogRecord attributes), and vice versa. Changed in version 3.4: In Python versions earlier than 3.4, this function could also be passed a text level, and would return the corresponding numeric value of the level. This undocumented behaviour was considered a mistake, and was removed in Python 3.4, but reinstated in 3.4.2 due to retain backward compatibility. | python.library.logging#logging.getLevelName |
logging.getLogger(name=None)
Return a logger with the specified name or, if name is None, return a logger which is the root logger of the hierarchy. If specified, the name is typically a dot-separated hierarchical name like ‘a’, ‘a.b’ or ‘a.b.c.d’. Choice of these names is entirely up to the developer who is using logging. All calls to this function with a given name return the same logger instance. This means that logger instances never need to be passed between different parts of an application. | python.library.logging#logging.getLogger |
logging.getLoggerClass()
Return either the standard Logger class, or the last class passed to setLoggerClass(). This function may be called from within a new class definition, to ensure that installing a customized Logger class will not undo customizations already applied by other code. For example: class MyLogger(logging.getLoggerClass()):
# ... override behaviour here | python.library.logging#logging.getLoggerClass |
logging.getLogRecordFactory()
Return a callable which is used to create a LogRecord. New in version 3.2: This function has been provided, along with setLogRecordFactory(), to allow developers more control over how the LogRecord representing a logging event is constructed. See setLogRecordFactory() for more information about the how the factory is called. | python.library.logging#logging.getLogRecordFactory |
class logging.Handler
__init__(level=NOTSET)
Initializes the Handler instance by setting its level, setting the list of filters to the empty list and creating a lock (using createLock()) for serializing access to an I/O mechanism.
createLock()
Initializes a thread lock which can be used to serialize access to underlying I/O functionality which may not be threadsafe.
acquire()
Acquires the thread lock created with createLock().
release()
Releases the thread lock acquired with acquire().
setLevel(level)
Sets the threshold for this handler to level. Logging messages which are less severe than level will be ignored. When a handler is created, the level is set to NOTSET (which causes all messages to be processed). See Logging Levels for a list of levels. Changed in version 3.2: The level parameter now accepts a string representation of the level such as ‘INFO’ as an alternative to the integer constants such as INFO.
setFormatter(fmt)
Sets the Formatter for this handler to fmt.
addFilter(filter)
Adds the specified filter filter to this handler.
removeFilter(filter)
Removes the specified filter filter from this handler.
filter(record)
Apply this handler’s filters to the record and return True if the record is to be processed. The filters are consulted in turn, until one of them returns a false value. If none of them return a false value, the record will be emitted. If one returns a false value, the handler will not emit the record.
flush()
Ensure all logging output has been flushed. This version does nothing and is intended to be implemented by subclasses.
close()
Tidy up any resources used by the handler. This version does no output but removes the handler from an internal list of handlers which is closed when shutdown() is called. Subclasses should ensure that this gets called from overridden close() methods.
handle(record)
Conditionally emits the specified logging record, depending on filters which may have been added to the handler. Wraps the actual emission of the record with acquisition/release of the I/O thread lock.
handleError(record)
This method should be called from handlers when an exception is encountered during an emit() call. If the module-level attribute raiseExceptions is False, exceptions get silently ignored. This is what is mostly wanted for a logging system - most users will not care about errors in the logging system, they are more interested in application errors. You could, however, replace this with a custom handler if you wish. The specified record is the one which was being processed when the exception occurred. (The default value of raiseExceptions is True, as that is more useful during development).
format(record)
Do formatting for a record - if a formatter is set, use it. Otherwise, use the default formatter for the module.
emit(record)
Do whatever it takes to actually log the specified logging record. This version is intended to be implemented by subclasses and so raises a NotImplementedError. | python.library.logging#logging.Handler |
acquire()
Acquires the thread lock created with createLock(). | python.library.logging#logging.Handler.acquire |
addFilter(filter)
Adds the specified filter filter to this handler. | python.library.logging#logging.Handler.addFilter |
close()
Tidy up any resources used by the handler. This version does no output but removes the handler from an internal list of handlers which is closed when shutdown() is called. Subclasses should ensure that this gets called from overridden close() methods. | python.library.logging#logging.Handler.close |
createLock()
Initializes a thread lock which can be used to serialize access to underlying I/O functionality which may not be threadsafe. | python.library.logging#logging.Handler.createLock |
emit(record)
Do whatever it takes to actually log the specified logging record. This version is intended to be implemented by subclasses and so raises a NotImplementedError. | python.library.logging#logging.Handler.emit |
filter(record)
Apply this handler’s filters to the record and return True if the record is to be processed. The filters are consulted in turn, until one of them returns a false value. If none of them return a false value, the record will be emitted. If one returns a false value, the handler will not emit the record. | python.library.logging#logging.Handler.filter |
flush()
Ensure all logging output has been flushed. This version does nothing and is intended to be implemented by subclasses. | python.library.logging#logging.Handler.flush |
format(record)
Do formatting for a record - if a formatter is set, use it. Otherwise, use the default formatter for the module. | python.library.logging#logging.Handler.format |
handle(record)
Conditionally emits the specified logging record, depending on filters which may have been added to the handler. Wraps the actual emission of the record with acquisition/release of the I/O thread lock. | python.library.logging#logging.Handler.handle |
handleError(record)
This method should be called from handlers when an exception is encountered during an emit() call. If the module-level attribute raiseExceptions is False, exceptions get silently ignored. This is what is mostly wanted for a logging system - most users will not care about errors in the logging system, they are more interested in application errors. You could, however, replace this with a custom handler if you wish. The specified record is the one which was being processed when the exception occurred. (The default value of raiseExceptions is True, as that is more useful during development). | python.library.logging#logging.Handler.handleError |
release()
Releases the thread lock acquired with acquire(). | python.library.logging#logging.Handler.release |
removeFilter(filter)
Removes the specified filter filter from this handler. | python.library.logging#logging.Handler.removeFilter |
setFormatter(fmt)
Sets the Formatter for this handler to fmt. | python.library.logging#logging.Handler.setFormatter |
setLevel(level)
Sets the threshold for this handler to level. Logging messages which are less severe than level will be ignored. When a handler is created, the level is set to NOTSET (which causes all messages to be processed). See Logging Levels for a list of levels. Changed in version 3.2: The level parameter now accepts a string representation of the level such as ‘INFO’ as an alternative to the integer constants such as INFO. | python.library.logging#logging.Handler.setLevel |
__init__(level=NOTSET)
Initializes the Handler instance by setting its level, setting the list of filters to the empty list and creating a lock (using createLock()) for serializing access to an I/O mechanism. | python.library.logging#logging.Handler.__init__ |
logging.handlers — Logging handlers Source code: Lib/logging/handlers.py Important This page contains only reference information. For tutorials, please see Basic Tutorial Advanced Tutorial Logging Cookbook The following useful handlers are provided in the package. Note that three of the handlers (StreamHandler, FileHandler and NullHandler) are actually defined in the logging module itself, but have been documented here along with the other handlers. StreamHandler The StreamHandler class, located in the core logging package, sends logging output to streams such as sys.stdout, sys.stderr or any file-like object (or, more precisely, any object which supports write() and flush() methods).
class logging.StreamHandler(stream=None)
Returns a new instance of the StreamHandler class. If stream is specified, the instance will use it for logging output; otherwise, sys.stderr will be used.
emit(record)
If a formatter is specified, it is used to format the record. The record is then written to the stream followed by terminator. If exception information is present, it is formatted using traceback.print_exception() and appended to the stream.
flush()
Flushes the stream by calling its flush() method. Note that the close() method is inherited from Handler and so does no output, so an explicit flush() call may be needed at times.
setStream(stream)
Sets the instance’s stream to the specified value, if it is different. The old stream is flushed before the new stream is set. Parameters
stream – The stream that the handler should use. Returns
the old stream, if the stream was changed, or None if it wasn’t. New in version 3.7.
terminator
String used as the terminator when writing a formatted record to a stream. Default value is '\n'. If you don’t want a newline termination, you can set the handler instance’s terminator attribute to the empty string. In earlier versions, the terminator was hardcoded as '\n'. New in version 3.2.
FileHandler The FileHandler class, located in the core logging package, sends logging output to a disk file. It inherits the output functionality from StreamHandler.
class logging.FileHandler(filename, mode='a', encoding=None, delay=False, errors=None)
Returns a new instance of the FileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely. If errors is specified, it’s used to determine how encoding errors are handled. Changed in version 3.6: As well as string values, Path objects are also accepted for the filename argument. Changed in version 3.9: The errors parameter was added.
close()
Closes the file.
emit(record)
Outputs the record to the file.
NullHandler New in version 3.1. The NullHandler class, located in the core logging package, does not do any formatting or output. It is essentially a ‘no-op’ handler for use by library developers.
class logging.NullHandler
Returns a new instance of the NullHandler class.
emit(record)
This method does nothing.
handle(record)
This method does nothing.
createLock()
This method returns None for the lock, since there is no underlying I/O to which access needs to be serialized.
See Configuring Logging for a Library for more information on how to use NullHandler. WatchedFileHandler The WatchedFileHandler class, located in the logging.handlers module, is a FileHandler which watches the file it is logging to. If the file changes, it is closed and reopened using the file name. A file change can happen because of usage of programs such as newsyslog and logrotate which perform log file rotation. This handler, intended for use under Unix/Linux, watches the file to see if it has changed since the last emit. (A file is deemed to have changed if its device or inode have changed.) If the file has changed, the old file stream is closed, and the file opened to get a new stream. This handler is not appropriate for use under Windows, because under Windows open log files cannot be moved or renamed - logging opens the files with exclusive locks - and so there is no need for such a handler. Furthermore, ST_INO is not supported under Windows; stat() always returns zero for this value.
class logging.handlers.WatchedFileHandler(filename, mode='a', encoding=None, delay=False, errors=None)
Returns a new instance of the WatchedFileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely. If errors is provided, it determines how encoding errors are handled. Changed in version 3.6: As well as string values, Path objects are also accepted for the filename argument. Changed in version 3.9: The errors parameter was added.
reopenIfNeeded()
Checks to see if the file has changed. If it has, the existing stream is flushed and closed and the file opened again, typically as a precursor to outputting the record to the file. New in version 3.6.
emit(record)
Outputs the record to the file, but first calls reopenIfNeeded() to reopen the file if it has changed.
BaseRotatingHandler The BaseRotatingHandler class, located in the logging.handlers module, is the base class for the rotating file handlers, RotatingFileHandler and TimedRotatingFileHandler. You should not need to instantiate this class, but it has attributes and methods you may need to override.
class logging.handlers.BaseRotatingHandler(filename, mode, encoding=None, delay=False, errors=None)
The parameters are as for FileHandler. The attributes are:
namer
If this attribute is set to a callable, the rotation_filename() method delegates to this callable. The parameters passed to the callable are those passed to rotation_filename(). Note The namer function is called quite a few times during rollover, so it should be as simple and as fast as possible. It should also return the same output every time for a given input, otherwise the rollover behaviour may not work as expected. New in version 3.3.
rotator
If this attribute is set to a callable, the rotate() method delegates to this callable. The parameters passed to the callable are those passed to rotate(). New in version 3.3.
rotation_filename(default_name)
Modify the filename of a log file when rotating. This is provided so that a custom filename can be provided. The default implementation calls the ‘namer’ attribute of the handler, if it’s callable, passing the default name to it. If the attribute isn’t callable (the default is None), the name is returned unchanged. Parameters
default_name – The default name for the log file. New in version 3.3.
rotate(source, dest)
When rotating, rotate the current log. The default implementation calls the ‘rotator’ attribute of the handler, if it’s callable, passing the source and dest arguments to it. If the attribute isn’t callable (the default is None), the source is simply renamed to the destination. Parameters
source – The source filename. This is normally the base filename, e.g. ‘test.log’.
dest – The destination filename. This is normally what the source is rotated to, e.g. ‘test.log.1’. New in version 3.3.
The reason the attributes exist is to save you having to subclass - you can use the same callables for instances of RotatingFileHandler and TimedRotatingFileHandler. If either the namer or rotator callable raises an exception, this will be handled in the same way as any other exception during an emit() call, i.e. via the handleError() method of the handler. If you need to make more significant changes to rotation processing, you can override the methods. For an example, see Using a rotator and namer to customize log rotation processing. RotatingFileHandler The RotatingFileHandler class, located in the logging.handlers module, supports rotation of disk log files.
class logging.handlers.RotatingFileHandler(filename, mode='a', maxBytes=0, backupCount=0, encoding=None, delay=False, errors=None)
Returns a new instance of the RotatingFileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely. If errors is provided, it determines how encoding errors are handled. You can use the maxBytes and backupCount values to allow the file to rollover at a predetermined size. When the size is about to be exceeded, the file is closed and a new file is silently opened for output. Rollover occurs whenever the current log file is nearly maxBytes in length; but if either of maxBytes or backupCount is zero, rollover never occurs, so you generally want to set backupCount to at least 1, and have a non-zero maxBytes. When backupCount is non-zero, the system will save old log files by appending the extensions ‘.1’, ‘.2’ etc., to the filename. For example, with a backupCount of 5 and a base file name of app.log, you would get app.log, app.log.1, app.log.2, up to app.log.5. The file being written to is always app.log. When this file is filled, it is closed and renamed to app.log.1, and if files app.log.1, app.log.2, etc. exist, then they are renamed to app.log.2, app.log.3 etc. respectively. Changed in version 3.6: As well as string values, Path objects are also accepted for the filename argument. Changed in version 3.9: The errors parameter was added.
doRollover()
Does a rollover, as described above.
emit(record)
Outputs the record to the file, catering for rollover as described previously.
TimedRotatingFileHandler The TimedRotatingFileHandler class, located in the logging.handlers module, supports rotation of disk log files at certain timed intervals.
class logging.handlers.TimedRotatingFileHandler(filename, when='h', interval=1, backupCount=0, encoding=None, delay=False, utc=False, atTime=None, errors=None)
Returns a new instance of the TimedRotatingFileHandler class. The specified file is opened and used as the stream for logging. On rotating it also sets the filename suffix. Rotating happens based on the product of when and interval. You can use the when to specify the type of interval. The list of possible values is below. Note that they are not case sensitive.
Value Type of interval If/how atTime is used
'S' Seconds Ignored
'M' Minutes Ignored
'H' Hours Ignored
'D' Days Ignored
'W0'-'W6' Weekday (0=Monday) Used to compute initial rollover time
'midnight' Roll over at midnight, if atTime not specified, else at time atTime Used to compute initial rollover time When using weekday-based rotation, specify ‘W0’ for Monday, ‘W1’ for Tuesday, and so on up to ‘W6’ for Sunday. In this case, the value passed for interval isn’t used. The system will save old log files by appending extensions to the filename. The extensions are date-and-time based, using the strftime format %Y-%m-%d_%H-%M-%S or a leading portion thereof, depending on the rollover interval. When computing the next rollover time for the first time (when the handler is created), the last modification time of an existing log file, or else the current time, is used to compute when the next rotation will occur. If the utc argument is true, times in UTC will be used; otherwise local time is used. If backupCount is nonzero, at most backupCount files will be kept, and if more would be created when rollover occurs, the oldest one is deleted. The deletion logic uses the interval to determine which files to delete, so changing the interval may leave old files lying around. If delay is true, then file opening is deferred until the first call to emit(). If atTime is not None, it must be a datetime.time instance which specifies the time of day when rollover occurs, for the cases where rollover is set to happen “at midnight” or “on a particular weekday”. Note that in these cases, the atTime value is effectively used to compute the initial rollover, and subsequent rollovers would be calculated via the normal interval calculation. If errors is specified, it’s used to determine how encoding errors are handled. Note Calculation of the initial rollover time is done when the handler is initialised. Calculation of subsequent rollover times is done only when rollover occurs, and rollover occurs only when emitting output. If this is not kept in mind, it might lead to some confusion. For example, if an interval of “every minute” is set, that does not mean you will always see log files with times (in the filename) separated by a minute; if, during application execution, logging output is generated more frequently than once a minute, then you can expect to see log files with times separated by a minute. If, on the other hand, logging messages are only output once every five minutes (say), then there will be gaps in the file times corresponding to the minutes where no output (and hence no rollover) occurred. Changed in version 3.4: atTime parameter was added. Changed in version 3.6: As well as string values, Path objects are also accepted for the filename argument. Changed in version 3.9: The errors parameter was added.
doRollover()
Does a rollover, as described above.
emit(record)
Outputs the record to the file, catering for rollover as described above.
SocketHandler The SocketHandler class, located in the logging.handlers module, sends logging output to a network socket. The base class uses a TCP socket.
class logging.handlers.SocketHandler(host, port)
Returns a new instance of the SocketHandler class intended to communicate with a remote machine whose address is given by host and port. Changed in version 3.4: If port is specified as None, a Unix domain socket is created using the value in host - otherwise, a TCP socket is created.
close()
Closes the socket.
emit()
Pickles the record’s attribute dictionary and writes it to the socket in binary format. If there is an error with the socket, silently drops the packet. If the connection was previously lost, re-establishes the connection. To unpickle the record at the receiving end into a LogRecord, use the makeLogRecord() function.
handleError()
Handles an error which has occurred during emit(). The most likely cause is a lost connection. Closes the socket so that we can retry on the next event.
makeSocket()
This is a factory method which allows subclasses to define the precise type of socket they want. The default implementation creates a TCP socket (socket.SOCK_STREAM).
makePickle(record)
Pickles the record’s attribute dictionary in binary format with a length prefix, and returns it ready for transmission across the socket. The details of this operation are equivalent to: data = pickle.dumps(record_attr_dict, 1)
datalen = struct.pack('>L', len(data))
return datalen + data
Note that pickles aren’t completely secure. If you are concerned about security, you may want to override this method to implement a more secure mechanism. For example, you can sign pickles using HMAC and then verify them on the receiving end, or alternatively you can disable unpickling of global objects on the receiving end.
send(packet)
Send a pickled byte-string packet to the socket. The format of the sent byte-string is as described in the documentation for makePickle(). This function allows for partial sends, which can happen when the network is busy.
createSocket()
Tries to create a socket; on failure, uses an exponential back-off algorithm. On initial failure, the handler will drop the message it was trying to send. When subsequent messages are handled by the same instance, it will not try connecting until some time has passed. The default parameters are such that the initial delay is one second, and if after that delay the connection still can’t be made, the handler will double the delay each time up to a maximum of 30 seconds. This behaviour is controlled by the following handler attributes:
retryStart (initial delay, defaulting to 1.0 seconds).
retryFactor (multiplier, defaulting to 2.0).
retryMax (maximum delay, defaulting to 30.0 seconds). This means that if the remote listener starts up after the handler has been used, you could lose messages (since the handler won’t even attempt a connection until the delay has elapsed, but just silently drop messages during the delay period).
DatagramHandler The DatagramHandler class, located in the logging.handlers module, inherits from SocketHandler to support sending logging messages over UDP sockets.
class logging.handlers.DatagramHandler(host, port)
Returns a new instance of the DatagramHandler class intended to communicate with a remote machine whose address is given by host and port. Changed in version 3.4: If port is specified as None, a Unix domain socket is created using the value in host - otherwise, a UDP socket is created.
emit()
Pickles the record’s attribute dictionary and writes it to the socket in binary format. If there is an error with the socket, silently drops the packet. To unpickle the record at the receiving end into a LogRecord, use the makeLogRecord() function.
makeSocket()
The factory method of SocketHandler is here overridden to create a UDP socket (socket.SOCK_DGRAM).
send(s)
Send a pickled byte-string to a socket. The format of the sent byte-string is as described in the documentation for SocketHandler.makePickle().
SysLogHandler The SysLogHandler class, located in the logging.handlers module, supports sending logging messages to a remote or local Unix syslog.
class logging.handlers.SysLogHandler(address=('localhost', SYSLOG_UDP_PORT), facility=LOG_USER, socktype=socket.SOCK_DGRAM)
Returns a new instance of the SysLogHandler class intended to communicate with a remote Unix machine whose address is given by address in the form of a (host, port) tuple. If address is not specified, ('localhost', 514) is used. The address is used to open a socket. An alternative to providing a (host, port) tuple is providing an address as a string, for example ‘/dev/log’. In this case, a Unix domain socket is used to send the message to the syslog. If facility is not specified, LOG_USER is used. The type of socket opened depends on the socktype argument, which defaults to socket.SOCK_DGRAM and thus opens a UDP socket. To open a TCP socket (for use with the newer syslog daemons such as rsyslog), specify a value of socket.SOCK_STREAM. Note that if your server is not listening on UDP port 514, SysLogHandler may appear not to work. In that case, check what address you should be using for a domain socket - it’s system dependent. For example, on Linux it’s usually ‘/dev/log’ but on OS/X it’s ‘/var/run/syslog’. You’ll need to check your platform and use the appropriate address (you may need to do this check at runtime if your application needs to run on several platforms). On Windows, you pretty much have to use the UDP option. Changed in version 3.2: socktype was added.
close()
Closes the socket to the remote host.
emit(record)
The record is formatted, and then sent to the syslog server. If exception information is present, it is not sent to the server. Changed in version 3.2.1: (See: bpo-12168.) In earlier versions, the message sent to the syslog daemons was always terminated with a NUL byte, because early versions of these daemons expected a NUL terminated message - even though it’s not in the relevant specification (RFC 5424). More recent versions of these daemons don’t expect the NUL byte but strip it off if it’s there, and even more recent daemons (which adhere more closely to RFC 5424) pass the NUL byte on as part of the message. To enable easier handling of syslog messages in the face of all these differing daemon behaviours, the appending of the NUL byte has been made configurable, through the use of a class-level attribute, append_nul. This defaults to True (preserving the existing behaviour) but can be set to False on a SysLogHandler instance in order for that instance to not append the NUL terminator. Changed in version 3.3: (See: bpo-12419.) In earlier versions, there was no facility for an “ident” or “tag” prefix to identify the source of the message. This can now be specified using a class-level attribute, defaulting to "" to preserve existing behaviour, but which can be overridden on a SysLogHandler instance in order for that instance to prepend the ident to every message handled. Note that the provided ident must be text, not bytes, and is prepended to the message exactly as is.
encodePriority(facility, priority)
Encodes the facility and priority into an integer. You can pass in strings or integers - if strings are passed, internal mapping dictionaries are used to convert them to integers. The symbolic LOG_ values are defined in SysLogHandler and mirror the values defined in the sys/syslog.h header file. Priorities
Name (string) Symbolic value
alert LOG_ALERT
crit or critical LOG_CRIT
debug LOG_DEBUG
emerg or panic LOG_EMERG
err or error LOG_ERR
info LOG_INFO
notice LOG_NOTICE
warn or warning LOG_WARNING Facilities
Name (string) Symbolic value
auth LOG_AUTH
authpriv LOG_AUTHPRIV
cron LOG_CRON
daemon LOG_DAEMON
ftp LOG_FTP
kern LOG_KERN
lpr LOG_LPR
mail LOG_MAIL
news LOG_NEWS
syslog LOG_SYSLOG
user LOG_USER
uucp LOG_UUCP
local0 LOG_LOCAL0
local1 LOG_LOCAL1
local2 LOG_LOCAL2
local3 LOG_LOCAL3
local4 LOG_LOCAL4
local5 LOG_LOCAL5
local6 LOG_LOCAL6
local7 LOG_LOCAL7
mapPriority(levelname)
Maps a logging level name to a syslog priority name. You may need to override this if you are using custom levels, or if the default algorithm is not suitable for your needs. The default algorithm maps DEBUG, INFO, WARNING, ERROR and CRITICAL to the equivalent syslog names, and all other level names to ‘warning’.
NTEventLogHandler The NTEventLogHandler class, located in the logging.handlers module, supports sending logging messages to a local Windows NT, Windows 2000 or Windows XP event log. Before you can use it, you need Mark Hammond’s Win32 extensions for Python installed.
class logging.handlers.NTEventLogHandler(appname, dllname=None, logtype='Application')
Returns a new instance of the NTEventLogHandler class. The appname is used to define the application name as it appears in the event log. An appropriate registry entry is created using this name. The dllname should give the fully qualified pathname of a .dll or .exe which contains message definitions to hold in the log (if not specified, 'win32service.pyd' is used - this is installed with the Win32 extensions and contains some basic placeholder message definitions. Note that use of these placeholders will make your event logs big, as the entire message source is held in the log. If you want slimmer logs, you have to pass in the name of your own .dll or .exe which contains the message definitions you want to use in the event log). The logtype is one of 'Application', 'System' or 'Security', and defaults to 'Application'.
close()
At this point, you can remove the application name from the registry as a source of event log entries. However, if you do this, you will not be able to see the events as you intended in the Event Log Viewer - it needs to be able to access the registry to get the .dll name. The current version does not do this.
emit(record)
Determines the message ID, event category and event type, and then logs the message in the NT event log.
getEventCategory(record)
Returns the event category for the record. Override this if you want to specify your own categories. This version returns 0.
getEventType(record)
Returns the event type for the record. Override this if you want to specify your own types. This version does a mapping using the handler’s typemap attribute, which is set up in __init__() to a dictionary which contains mappings for DEBUG, INFO, WARNING, ERROR and CRITICAL. If you are using your own levels, you will either need to override this method or place a suitable dictionary in the handler’s typemap attribute.
getMessageID(record)
Returns the message ID for the record. If you are using your own messages, you could do this by having the msg passed to the logger being an ID rather than a format string. Then, in here, you could use a dictionary lookup to get the message ID. This version returns 1, which is the base message ID in win32service.pyd.
SMTPHandler The SMTPHandler class, located in the logging.handlers module, supports sending logging messages to an email address via SMTP.
class logging.handlers.SMTPHandler(mailhost, fromaddr, toaddrs, subject, credentials=None, secure=None, timeout=1.0)
Returns a new instance of the SMTPHandler class. The instance is initialized with the from and to addresses and subject line of the email. The toaddrs should be a list of strings. To specify a non-standard SMTP port, use the (host, port) tuple format for the mailhost argument. If you use a string, the standard SMTP port is used. If your SMTP server requires authentication, you can specify a (username, password) tuple for the credentials argument. To specify the use of a secure protocol (TLS), pass in a tuple to the secure argument. This will only be used when authentication credentials are supplied. The tuple should be either an empty tuple, or a single-value tuple with the name of a keyfile, or a 2-value tuple with the names of the keyfile and certificate file. (This tuple is passed to the smtplib.SMTP.starttls() method.) A timeout can be specified for communication with the SMTP server using the timeout argument. New in version 3.3: The timeout argument was added.
emit(record)
Formats the record and sends it to the specified addressees.
getSubject(record)
If you want to specify a subject line which is record-dependent, override this method.
MemoryHandler The MemoryHandler class, located in the logging.handlers module, supports buffering of logging records in memory, periodically flushing them to a target handler. Flushing occurs whenever the buffer is full, or when an event of a certain severity or greater is seen. MemoryHandler is a subclass of the more general BufferingHandler, which is an abstract class. This buffers logging records in memory. Whenever each record is added to the buffer, a check is made by calling shouldFlush() to see if the buffer should be flushed. If it should, then flush() is expected to do the flushing.
class logging.handlers.BufferingHandler(capacity)
Initializes the handler with a buffer of the specified capacity. Here, capacity means the number of logging records buffered.
emit(record)
Append the record to the buffer. If shouldFlush() returns true, call flush() to process the buffer.
flush()
You can override this to implement custom flushing behavior. This version just zaps the buffer to empty.
shouldFlush(record)
Return True if the buffer is up to capacity. This method can be overridden to implement custom flushing strategies.
class logging.handlers.MemoryHandler(capacity, flushLevel=ERROR, target=None, flushOnClose=True)
Returns a new instance of the MemoryHandler class. The instance is initialized with a buffer size of capacity (number of records buffered). If flushLevel is not specified, ERROR is used. If no target is specified, the target will need to be set using setTarget() before this handler does anything useful. If flushOnClose is specified as False, then the buffer is not flushed when the handler is closed. If not specified or specified as True, the previous behaviour of flushing the buffer will occur when the handler is closed. Changed in version 3.6: The flushOnClose parameter was added.
close()
Calls flush(), sets the target to None and clears the buffer.
flush()
For a MemoryHandler, flushing means just sending the buffered records to the target, if there is one. The buffer is also cleared when this happens. Override if you want different behavior.
setTarget(target)
Sets the target handler for this handler.
shouldFlush(record)
Checks for buffer full or a record at the flushLevel or higher.
HTTPHandler The HTTPHandler class, located in the logging.handlers module, supports sending logging messages to a Web server, using either GET or POST semantics.
class logging.handlers.HTTPHandler(host, url, method='GET', secure=False, credentials=None, context=None)
Returns a new instance of the HTTPHandler class. The host can be of the form host:port, should you need to use a specific port number. If no method is specified, GET is used. If secure is true, a HTTPS connection will be used. The context parameter may be set to a ssl.SSLContext instance to configure the SSL settings used for the HTTPS connection. If credentials is specified, it should be a 2-tuple consisting of userid and password, which will be placed in a HTTP ‘Authorization’ header using Basic authentication. If you specify credentials, you should also specify secure=True so that your userid and password are not passed in cleartext across the wire. Changed in version 3.5: The context parameter was added.
mapLogRecord(record)
Provides a dictionary, based on record, which is to be URL-encoded and sent to the web server. The default implementation just returns record.__dict__. This method can be overridden if e.g. only a subset of LogRecord is to be sent to the web server, or if more specific customization of what’s sent to the server is required.
emit(record)
Sends the record to the Web server as a URL-encoded dictionary. The mapLogRecord() method is used to convert the record to the dictionary to be sent.
Note Since preparing a record for sending it to a Web server is not the same as a generic formatting operation, using setFormatter() to specify a Formatter for a HTTPHandler has no effect. Instead of calling format(), this handler calls mapLogRecord() and then urllib.parse.urlencode() to encode the dictionary in a form suitable for sending to a Web server.
QueueHandler New in version 3.2. The QueueHandler class, located in the logging.handlers module, supports sending logging messages to a queue, such as those implemented in the queue or multiprocessing modules. Along with the QueueListener class, QueueHandler can be used to let handlers do their work on a separate thread from the one which does the logging. This is important in Web applications and also other service applications where threads servicing clients need to respond as quickly as possible, while any potentially slow operations (such as sending an email via SMTPHandler) are done on a separate thread.
class logging.handlers.QueueHandler(queue)
Returns a new instance of the QueueHandler class. The instance is initialized with the queue to send messages to. The queue can be any queue-like object; it’s used as-is by the enqueue() method, which needs to know how to send messages to it. The queue is not required to have the task tracking API, which means that you can use SimpleQueue instances for queue.
emit(record)
Enqueues the result of preparing the LogRecord. Should an exception occur (e.g. because a bounded queue has filled up), the handleError() method is called to handle the error. This can result in the record silently being dropped (if logging.raiseExceptions is False) or a message printed to sys.stderr (if logging.raiseExceptions is True).
prepare(record)
Prepares a record for queuing. The object returned by this method is enqueued. The base implementation formats the record to merge the message, arguments, and exception information, if present. It also removes unpickleable items from the record in-place. You might want to override this method if you want to convert the record to a dict or JSON string, or send a modified copy of the record while leaving the original intact.
enqueue(record)
Enqueues the record on the queue using put_nowait(); you may want to override this if you want to use blocking behaviour, or a timeout, or a customized queue implementation.
QueueListener New in version 3.2. The QueueListener class, located in the logging.handlers module, supports receiving logging messages from a queue, such as those implemented in the queue or multiprocessing modules. The messages are received from a queue in an internal thread and passed, on the same thread, to one or more handlers for processing. While QueueListener is not itself a handler, it is documented here because it works hand-in-hand with QueueHandler. Along with the QueueHandler class, QueueListener can be used to let handlers do their work on a separate thread from the one which does the logging. This is important in Web applications and also other service applications where threads servicing clients need to respond as quickly as possible, while any potentially slow operations (such as sending an email via SMTPHandler) are done on a separate thread.
class logging.handlers.QueueListener(queue, *handlers, respect_handler_level=False)
Returns a new instance of the QueueListener class. The instance is initialized with the queue to send messages to and a list of handlers which will handle entries placed on the queue. The queue can be any queue-like object; it’s passed as-is to the dequeue() method, which needs to know how to get messages from it. The queue is not required to have the task tracking API (though it’s used if available), which means that you can use SimpleQueue instances for queue. If respect_handler_level is True, a handler’s level is respected (compared with the level for the message) when deciding whether to pass messages to that handler; otherwise, the behaviour is as in previous Python versions - to always pass each message to each handler. Changed in version 3.5: The respect_handler_level argument was added.
dequeue(block)
Dequeues a record and return it, optionally blocking. The base implementation uses get(). You may want to override this method if you want to use timeouts or work with custom queue implementations.
prepare(record)
Prepare a record for handling. This implementation just returns the passed-in record. You may want to override this method if you need to do any custom marshalling or manipulation of the record before passing it to the handlers.
handle(record)
Handle a record. This just loops through the handlers offering them the record to handle. The actual object passed to the handlers is that which is returned from prepare().
start()
Starts the listener. This starts up a background thread to monitor the queue for LogRecords to process.
stop()
Stops the listener. This asks the thread to terminate, and then waits for it to do so. Note that if you don’t call this before your application exits, there may be some records still left on the queue, which won’t be processed.
enqueue_sentinel()
Writes a sentinel to the queue to tell the listener to quit. This implementation uses put_nowait(). You may want to override this method if you want to use timeouts or work with custom queue implementations. New in version 3.3.
See also
Module logging
API reference for the logging module.
Module logging.config
Configuration API for the logging module. | python.library.logging.handlers |
class logging.handlers.BaseRotatingHandler(filename, mode, encoding=None, delay=False, errors=None)
The parameters are as for FileHandler. The attributes are:
namer
If this attribute is set to a callable, the rotation_filename() method delegates to this callable. The parameters passed to the callable are those passed to rotation_filename(). Note The namer function is called quite a few times during rollover, so it should be as simple and as fast as possible. It should also return the same output every time for a given input, otherwise the rollover behaviour may not work as expected. New in version 3.3.
rotator
If this attribute is set to a callable, the rotate() method delegates to this callable. The parameters passed to the callable are those passed to rotate(). New in version 3.3.
rotation_filename(default_name)
Modify the filename of a log file when rotating. This is provided so that a custom filename can be provided. The default implementation calls the ‘namer’ attribute of the handler, if it’s callable, passing the default name to it. If the attribute isn’t callable (the default is None), the name is returned unchanged. Parameters
default_name – The default name for the log file. New in version 3.3.
rotate(source, dest)
When rotating, rotate the current log. The default implementation calls the ‘rotator’ attribute of the handler, if it’s callable, passing the source and dest arguments to it. If the attribute isn’t callable (the default is None), the source is simply renamed to the destination. Parameters
source – The source filename. This is normally the base filename, e.g. ‘test.log’.
dest – The destination filename. This is normally what the source is rotated to, e.g. ‘test.log.1’. New in version 3.3. | python.library.logging.handlers#logging.handlers.BaseRotatingHandler |
namer
If this attribute is set to a callable, the rotation_filename() method delegates to this callable. The parameters passed to the callable are those passed to rotation_filename(). Note The namer function is called quite a few times during rollover, so it should be as simple and as fast as possible. It should also return the same output every time for a given input, otherwise the rollover behaviour may not work as expected. New in version 3.3. | python.library.logging.handlers#logging.handlers.BaseRotatingHandler.namer |
rotate(source, dest)
When rotating, rotate the current log. The default implementation calls the ‘rotator’ attribute of the handler, if it’s callable, passing the source and dest arguments to it. If the attribute isn’t callable (the default is None), the source is simply renamed to the destination. Parameters
source – The source filename. This is normally the base filename, e.g. ‘test.log’.
dest – The destination filename. This is normally what the source is rotated to, e.g. ‘test.log.1’. New in version 3.3. | python.library.logging.handlers#logging.handlers.BaseRotatingHandler.rotate |
rotation_filename(default_name)
Modify the filename of a log file when rotating. This is provided so that a custom filename can be provided. The default implementation calls the ‘namer’ attribute of the handler, if it’s callable, passing the default name to it. If the attribute isn’t callable (the default is None), the name is returned unchanged. Parameters
default_name – The default name for the log file. New in version 3.3. | python.library.logging.handlers#logging.handlers.BaseRotatingHandler.rotation_filename |
rotator
If this attribute is set to a callable, the rotate() method delegates to this callable. The parameters passed to the callable are those passed to rotate(). New in version 3.3. | python.library.logging.handlers#logging.handlers.BaseRotatingHandler.rotator |
class logging.handlers.BufferingHandler(capacity)
Initializes the handler with a buffer of the specified capacity. Here, capacity means the number of logging records buffered.
emit(record)
Append the record to the buffer. If shouldFlush() returns true, call flush() to process the buffer.
flush()
You can override this to implement custom flushing behavior. This version just zaps the buffer to empty.
shouldFlush(record)
Return True if the buffer is up to capacity. This method can be overridden to implement custom flushing strategies. | python.library.logging.handlers#logging.handlers.BufferingHandler |
emit(record)
Append the record to the buffer. If shouldFlush() returns true, call flush() to process the buffer. | python.library.logging.handlers#logging.handlers.BufferingHandler.emit |
flush()
You can override this to implement custom flushing behavior. This version just zaps the buffer to empty. | python.library.logging.handlers#logging.handlers.BufferingHandler.flush |
shouldFlush(record)
Return True if the buffer is up to capacity. This method can be overridden to implement custom flushing strategies. | python.library.logging.handlers#logging.handlers.BufferingHandler.shouldFlush |
class logging.handlers.DatagramHandler(host, port)
Returns a new instance of the DatagramHandler class intended to communicate with a remote machine whose address is given by host and port. Changed in version 3.4: If port is specified as None, a Unix domain socket is created using the value in host - otherwise, a UDP socket is created.
emit()
Pickles the record’s attribute dictionary and writes it to the socket in binary format. If there is an error with the socket, silently drops the packet. To unpickle the record at the receiving end into a LogRecord, use the makeLogRecord() function.
makeSocket()
The factory method of SocketHandler is here overridden to create a UDP socket (socket.SOCK_DGRAM).
send(s)
Send a pickled byte-string to a socket. The format of the sent byte-string is as described in the documentation for SocketHandler.makePickle(). | python.library.logging.handlers#logging.handlers.DatagramHandler |
emit()
Pickles the record’s attribute dictionary and writes it to the socket in binary format. If there is an error with the socket, silently drops the packet. To unpickle the record at the receiving end into a LogRecord, use the makeLogRecord() function. | python.library.logging.handlers#logging.handlers.DatagramHandler.emit |
makeSocket()
The factory method of SocketHandler is here overridden to create a UDP socket (socket.SOCK_DGRAM). | python.library.logging.handlers#logging.handlers.DatagramHandler.makeSocket |
send(s)
Send a pickled byte-string to a socket. The format of the sent byte-string is as described in the documentation for SocketHandler.makePickle(). | python.library.logging.handlers#logging.handlers.DatagramHandler.send |
class logging.handlers.HTTPHandler(host, url, method='GET', secure=False, credentials=None, context=None)
Returns a new instance of the HTTPHandler class. The host can be of the form host:port, should you need to use a specific port number. If no method is specified, GET is used. If secure is true, a HTTPS connection will be used. The context parameter may be set to a ssl.SSLContext instance to configure the SSL settings used for the HTTPS connection. If credentials is specified, it should be a 2-tuple consisting of userid and password, which will be placed in a HTTP ‘Authorization’ header using Basic authentication. If you specify credentials, you should also specify secure=True so that your userid and password are not passed in cleartext across the wire. Changed in version 3.5: The context parameter was added.
mapLogRecord(record)
Provides a dictionary, based on record, which is to be URL-encoded and sent to the web server. The default implementation just returns record.__dict__. This method can be overridden if e.g. only a subset of LogRecord is to be sent to the web server, or if more specific customization of what’s sent to the server is required.
emit(record)
Sends the record to the Web server as a URL-encoded dictionary. The mapLogRecord() method is used to convert the record to the dictionary to be sent.
Note Since preparing a record for sending it to a Web server is not the same as a generic formatting operation, using setFormatter() to specify a Formatter for a HTTPHandler has no effect. Instead of calling format(), this handler calls mapLogRecord() and then urllib.parse.urlencode() to encode the dictionary in a form suitable for sending to a Web server. | python.library.logging.handlers#logging.handlers.HTTPHandler |
emit(record)
Sends the record to the Web server as a URL-encoded dictionary. The mapLogRecord() method is used to convert the record to the dictionary to be sent. | python.library.logging.handlers#logging.handlers.HTTPHandler.emit |
mapLogRecord(record)
Provides a dictionary, based on record, which is to be URL-encoded and sent to the web server. The default implementation just returns record.__dict__. This method can be overridden if e.g. only a subset of LogRecord is to be sent to the web server, or if more specific customization of what’s sent to the server is required. | python.library.logging.handlers#logging.handlers.HTTPHandler.mapLogRecord |
class logging.handlers.MemoryHandler(capacity, flushLevel=ERROR, target=None, flushOnClose=True)
Returns a new instance of the MemoryHandler class. The instance is initialized with a buffer size of capacity (number of records buffered). If flushLevel is not specified, ERROR is used. If no target is specified, the target will need to be set using setTarget() before this handler does anything useful. If flushOnClose is specified as False, then the buffer is not flushed when the handler is closed. If not specified or specified as True, the previous behaviour of flushing the buffer will occur when the handler is closed. Changed in version 3.6: The flushOnClose parameter was added.
close()
Calls flush(), sets the target to None and clears the buffer.
flush()
For a MemoryHandler, flushing means just sending the buffered records to the target, if there is one. The buffer is also cleared when this happens. Override if you want different behavior.
setTarget(target)
Sets the target handler for this handler.
shouldFlush(record)
Checks for buffer full or a record at the flushLevel or higher. | python.library.logging.handlers#logging.handlers.MemoryHandler |
close()
Calls flush(), sets the target to None and clears the buffer. | python.library.logging.handlers#logging.handlers.MemoryHandler.close |
flush()
For a MemoryHandler, flushing means just sending the buffered records to the target, if there is one. The buffer is also cleared when this happens. Override if you want different behavior. | python.library.logging.handlers#logging.handlers.MemoryHandler.flush |
setTarget(target)
Sets the target handler for this handler. | python.library.logging.handlers#logging.handlers.MemoryHandler.setTarget |
shouldFlush(record)
Checks for buffer full or a record at the flushLevel or higher. | python.library.logging.handlers#logging.handlers.MemoryHandler.shouldFlush |
class logging.handlers.NTEventLogHandler(appname, dllname=None, logtype='Application')
Returns a new instance of the NTEventLogHandler class. The appname is used to define the application name as it appears in the event log. An appropriate registry entry is created using this name. The dllname should give the fully qualified pathname of a .dll or .exe which contains message definitions to hold in the log (if not specified, 'win32service.pyd' is used - this is installed with the Win32 extensions and contains some basic placeholder message definitions. Note that use of these placeholders will make your event logs big, as the entire message source is held in the log. If you want slimmer logs, you have to pass in the name of your own .dll or .exe which contains the message definitions you want to use in the event log). The logtype is one of 'Application', 'System' or 'Security', and defaults to 'Application'.
close()
At this point, you can remove the application name from the registry as a source of event log entries. However, if you do this, you will not be able to see the events as you intended in the Event Log Viewer - it needs to be able to access the registry to get the .dll name. The current version does not do this.
emit(record)
Determines the message ID, event category and event type, and then logs the message in the NT event log.
getEventCategory(record)
Returns the event category for the record. Override this if you want to specify your own categories. This version returns 0.
getEventType(record)
Returns the event type for the record. Override this if you want to specify your own types. This version does a mapping using the handler’s typemap attribute, which is set up in __init__() to a dictionary which contains mappings for DEBUG, INFO, WARNING, ERROR and CRITICAL. If you are using your own levels, you will either need to override this method or place a suitable dictionary in the handler’s typemap attribute.
getMessageID(record)
Returns the message ID for the record. If you are using your own messages, you could do this by having the msg passed to the logger being an ID rather than a format string. Then, in here, you could use a dictionary lookup to get the message ID. This version returns 1, which is the base message ID in win32service.pyd. | python.library.logging.handlers#logging.handlers.NTEventLogHandler |
close()
At this point, you can remove the application name from the registry as a source of event log entries. However, if you do this, you will not be able to see the events as you intended in the Event Log Viewer - it needs to be able to access the registry to get the .dll name. The current version does not do this. | python.library.logging.handlers#logging.handlers.NTEventLogHandler.close |
emit(record)
Determines the message ID, event category and event type, and then logs the message in the NT event log. | python.library.logging.handlers#logging.handlers.NTEventLogHandler.emit |
getEventCategory(record)
Returns the event category for the record. Override this if you want to specify your own categories. This version returns 0. | python.library.logging.handlers#logging.handlers.NTEventLogHandler.getEventCategory |
getEventType(record)
Returns the event type for the record. Override this if you want to specify your own types. This version does a mapping using the handler’s typemap attribute, which is set up in __init__() to a dictionary which contains mappings for DEBUG, INFO, WARNING, ERROR and CRITICAL. If you are using your own levels, you will either need to override this method or place a suitable dictionary in the handler’s typemap attribute. | python.library.logging.handlers#logging.handlers.NTEventLogHandler.getEventType |
getMessageID(record)
Returns the message ID for the record. If you are using your own messages, you could do this by having the msg passed to the logger being an ID rather than a format string. Then, in here, you could use a dictionary lookup to get the message ID. This version returns 1, which is the base message ID in win32service.pyd. | python.library.logging.handlers#logging.handlers.NTEventLogHandler.getMessageID |
class logging.handlers.QueueHandler(queue)
Returns a new instance of the QueueHandler class. The instance is initialized with the queue to send messages to. The queue can be any queue-like object; it’s used as-is by the enqueue() method, which needs to know how to send messages to it. The queue is not required to have the task tracking API, which means that you can use SimpleQueue instances for queue.
emit(record)
Enqueues the result of preparing the LogRecord. Should an exception occur (e.g. because a bounded queue has filled up), the handleError() method is called to handle the error. This can result in the record silently being dropped (if logging.raiseExceptions is False) or a message printed to sys.stderr (if logging.raiseExceptions is True).
prepare(record)
Prepares a record for queuing. The object returned by this method is enqueued. The base implementation formats the record to merge the message, arguments, and exception information, if present. It also removes unpickleable items from the record in-place. You might want to override this method if you want to convert the record to a dict or JSON string, or send a modified copy of the record while leaving the original intact.
enqueue(record)
Enqueues the record on the queue using put_nowait(); you may want to override this if you want to use blocking behaviour, or a timeout, or a customized queue implementation. | python.library.logging.handlers#logging.handlers.QueueHandler |
emit(record)
Enqueues the result of preparing the LogRecord. Should an exception occur (e.g. because a bounded queue has filled up), the handleError() method is called to handle the error. This can result in the record silently being dropped (if logging.raiseExceptions is False) or a message printed to sys.stderr (if logging.raiseExceptions is True). | python.library.logging.handlers#logging.handlers.QueueHandler.emit |
enqueue(record)
Enqueues the record on the queue using put_nowait(); you may want to override this if you want to use blocking behaviour, or a timeout, or a customized queue implementation. | python.library.logging.handlers#logging.handlers.QueueHandler.enqueue |
prepare(record)
Prepares a record for queuing. The object returned by this method is enqueued. The base implementation formats the record to merge the message, arguments, and exception information, if present. It also removes unpickleable items from the record in-place. You might want to override this method if you want to convert the record to a dict or JSON string, or send a modified copy of the record while leaving the original intact. | python.library.logging.handlers#logging.handlers.QueueHandler.prepare |
class logging.handlers.QueueListener(queue, *handlers, respect_handler_level=False)
Returns a new instance of the QueueListener class. The instance is initialized with the queue to send messages to and a list of handlers which will handle entries placed on the queue. The queue can be any queue-like object; it’s passed as-is to the dequeue() method, which needs to know how to get messages from it. The queue is not required to have the task tracking API (though it’s used if available), which means that you can use SimpleQueue instances for queue. If respect_handler_level is True, a handler’s level is respected (compared with the level for the message) when deciding whether to pass messages to that handler; otherwise, the behaviour is as in previous Python versions - to always pass each message to each handler. Changed in version 3.5: The respect_handler_level argument was added.
dequeue(block)
Dequeues a record and return it, optionally blocking. The base implementation uses get(). You may want to override this method if you want to use timeouts or work with custom queue implementations.
prepare(record)
Prepare a record for handling. This implementation just returns the passed-in record. You may want to override this method if you need to do any custom marshalling or manipulation of the record before passing it to the handlers.
handle(record)
Handle a record. This just loops through the handlers offering them the record to handle. The actual object passed to the handlers is that which is returned from prepare().
start()
Starts the listener. This starts up a background thread to monitor the queue for LogRecords to process.
stop()
Stops the listener. This asks the thread to terminate, and then waits for it to do so. Note that if you don’t call this before your application exits, there may be some records still left on the queue, which won’t be processed.
enqueue_sentinel()
Writes a sentinel to the queue to tell the listener to quit. This implementation uses put_nowait(). You may want to override this method if you want to use timeouts or work with custom queue implementations. New in version 3.3. | python.library.logging.handlers#logging.handlers.QueueListener |
dequeue(block)
Dequeues a record and return it, optionally blocking. The base implementation uses get(). You may want to override this method if you want to use timeouts or work with custom queue implementations. | python.library.logging.handlers#logging.handlers.QueueListener.dequeue |
enqueue_sentinel()
Writes a sentinel to the queue to tell the listener to quit. This implementation uses put_nowait(). You may want to override this method if you want to use timeouts or work with custom queue implementations. New in version 3.3. | python.library.logging.handlers#logging.handlers.QueueListener.enqueue_sentinel |
handle(record)
Handle a record. This just loops through the handlers offering them the record to handle. The actual object passed to the handlers is that which is returned from prepare(). | python.library.logging.handlers#logging.handlers.QueueListener.handle |
prepare(record)
Prepare a record for handling. This implementation just returns the passed-in record. You may want to override this method if you need to do any custom marshalling or manipulation of the record before passing it to the handlers. | python.library.logging.handlers#logging.handlers.QueueListener.prepare |
start()
Starts the listener. This starts up a background thread to monitor the queue for LogRecords to process. | python.library.logging.handlers#logging.handlers.QueueListener.start |
stop()
Stops the listener. This asks the thread to terminate, and then waits for it to do so. Note that if you don’t call this before your application exits, there may be some records still left on the queue, which won’t be processed. | python.library.logging.handlers#logging.handlers.QueueListener.stop |
class logging.handlers.RotatingFileHandler(filename, mode='a', maxBytes=0, backupCount=0, encoding=None, delay=False, errors=None)
Returns a new instance of the RotatingFileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely. If errors is provided, it determines how encoding errors are handled. You can use the maxBytes and backupCount values to allow the file to rollover at a predetermined size. When the size is about to be exceeded, the file is closed and a new file is silently opened for output. Rollover occurs whenever the current log file is nearly maxBytes in length; but if either of maxBytes or backupCount is zero, rollover never occurs, so you generally want to set backupCount to at least 1, and have a non-zero maxBytes. When backupCount is non-zero, the system will save old log files by appending the extensions ‘.1’, ‘.2’ etc., to the filename. For example, with a backupCount of 5 and a base file name of app.log, you would get app.log, app.log.1, app.log.2, up to app.log.5. The file being written to is always app.log. When this file is filled, it is closed and renamed to app.log.1, and if files app.log.1, app.log.2, etc. exist, then they are renamed to app.log.2, app.log.3 etc. respectively. Changed in version 3.6: As well as string values, Path objects are also accepted for the filename argument. Changed in version 3.9: The errors parameter was added.
doRollover()
Does a rollover, as described above.
emit(record)
Outputs the record to the file, catering for rollover as described previously. | python.library.logging.handlers#logging.handlers.RotatingFileHandler |
doRollover()
Does a rollover, as described above. | python.library.logging.handlers#logging.handlers.RotatingFileHandler.doRollover |
emit(record)
Outputs the record to the file, catering for rollover as described previously. | python.library.logging.handlers#logging.handlers.RotatingFileHandler.emit |
class logging.handlers.SMTPHandler(mailhost, fromaddr, toaddrs, subject, credentials=None, secure=None, timeout=1.0)
Returns a new instance of the SMTPHandler class. The instance is initialized with the from and to addresses and subject line of the email. The toaddrs should be a list of strings. To specify a non-standard SMTP port, use the (host, port) tuple format for the mailhost argument. If you use a string, the standard SMTP port is used. If your SMTP server requires authentication, you can specify a (username, password) tuple for the credentials argument. To specify the use of a secure protocol (TLS), pass in a tuple to the secure argument. This will only be used when authentication credentials are supplied. The tuple should be either an empty tuple, or a single-value tuple with the name of a keyfile, or a 2-value tuple with the names of the keyfile and certificate file. (This tuple is passed to the smtplib.SMTP.starttls() method.) A timeout can be specified for communication with the SMTP server using the timeout argument. New in version 3.3: The timeout argument was added.
emit(record)
Formats the record and sends it to the specified addressees.
getSubject(record)
If you want to specify a subject line which is record-dependent, override this method. | python.library.logging.handlers#logging.handlers.SMTPHandler |
emit(record)
Formats the record and sends it to the specified addressees. | python.library.logging.handlers#logging.handlers.SMTPHandler.emit |
getSubject(record)
If you want to specify a subject line which is record-dependent, override this method. | python.library.logging.handlers#logging.handlers.SMTPHandler.getSubject |
class logging.handlers.SocketHandler(host, port)
Returns a new instance of the SocketHandler class intended to communicate with a remote machine whose address is given by host and port. Changed in version 3.4: If port is specified as None, a Unix domain socket is created using the value in host - otherwise, a TCP socket is created.
close()
Closes the socket.
emit()
Pickles the record’s attribute dictionary and writes it to the socket in binary format. If there is an error with the socket, silently drops the packet. If the connection was previously lost, re-establishes the connection. To unpickle the record at the receiving end into a LogRecord, use the makeLogRecord() function.
handleError()
Handles an error which has occurred during emit(). The most likely cause is a lost connection. Closes the socket so that we can retry on the next event.
makeSocket()
This is a factory method which allows subclasses to define the precise type of socket they want. The default implementation creates a TCP socket (socket.SOCK_STREAM).
makePickle(record)
Pickles the record’s attribute dictionary in binary format with a length prefix, and returns it ready for transmission across the socket. The details of this operation are equivalent to: data = pickle.dumps(record_attr_dict, 1)
datalen = struct.pack('>L', len(data))
return datalen + data
Note that pickles aren’t completely secure. If you are concerned about security, you may want to override this method to implement a more secure mechanism. For example, you can sign pickles using HMAC and then verify them on the receiving end, or alternatively you can disable unpickling of global objects on the receiving end.
send(packet)
Send a pickled byte-string packet to the socket. The format of the sent byte-string is as described in the documentation for makePickle(). This function allows for partial sends, which can happen when the network is busy.
createSocket()
Tries to create a socket; on failure, uses an exponential back-off algorithm. On initial failure, the handler will drop the message it was trying to send. When subsequent messages are handled by the same instance, it will not try connecting until some time has passed. The default parameters are such that the initial delay is one second, and if after that delay the connection still can’t be made, the handler will double the delay each time up to a maximum of 30 seconds. This behaviour is controlled by the following handler attributes:
retryStart (initial delay, defaulting to 1.0 seconds).
retryFactor (multiplier, defaulting to 2.0).
retryMax (maximum delay, defaulting to 30.0 seconds). This means that if the remote listener starts up after the handler has been used, you could lose messages (since the handler won’t even attempt a connection until the delay has elapsed, but just silently drop messages during the delay period). | python.library.logging.handlers#logging.handlers.SocketHandler |
close()
Closes the socket. | python.library.logging.handlers#logging.handlers.SocketHandler.close |
createSocket()
Tries to create a socket; on failure, uses an exponential back-off algorithm. On initial failure, the handler will drop the message it was trying to send. When subsequent messages are handled by the same instance, it will not try connecting until some time has passed. The default parameters are such that the initial delay is one second, and if after that delay the connection still can’t be made, the handler will double the delay each time up to a maximum of 30 seconds. This behaviour is controlled by the following handler attributes:
retryStart (initial delay, defaulting to 1.0 seconds).
retryFactor (multiplier, defaulting to 2.0).
retryMax (maximum delay, defaulting to 30.0 seconds). This means that if the remote listener starts up after the handler has been used, you could lose messages (since the handler won’t even attempt a connection until the delay has elapsed, but just silently drop messages during the delay period). | python.library.logging.handlers#logging.handlers.SocketHandler.createSocket |
emit()
Pickles the record’s attribute dictionary and writes it to the socket in binary format. If there is an error with the socket, silently drops the packet. If the connection was previously lost, re-establishes the connection. To unpickle the record at the receiving end into a LogRecord, use the makeLogRecord() function. | python.library.logging.handlers#logging.handlers.SocketHandler.emit |
handleError()
Handles an error which has occurred during emit(). The most likely cause is a lost connection. Closes the socket so that we can retry on the next event. | python.library.logging.handlers#logging.handlers.SocketHandler.handleError |
makePickle(record)
Pickles the record’s attribute dictionary in binary format with a length prefix, and returns it ready for transmission across the socket. The details of this operation are equivalent to: data = pickle.dumps(record_attr_dict, 1)
datalen = struct.pack('>L', len(data))
return datalen + data
Note that pickles aren’t completely secure. If you are concerned about security, you may want to override this method to implement a more secure mechanism. For example, you can sign pickles using HMAC and then verify them on the receiving end, or alternatively you can disable unpickling of global objects on the receiving end. | python.library.logging.handlers#logging.handlers.SocketHandler.makePickle |
makeSocket()
This is a factory method which allows subclasses to define the precise type of socket they want. The default implementation creates a TCP socket (socket.SOCK_STREAM). | python.library.logging.handlers#logging.handlers.SocketHandler.makeSocket |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.