PEP: 391 Title: Dictionary-Based Configuration For Logging Version: $Revision$ Last-Modified: $Date$ Author: Vinay Sajip Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 15-Oct-2009 Python-Version: 2.7, 3.2 Post-History: Abstract ======== This PEP describes a new way of configuring logging using a dictionary to hold configuration information. Rationale ========= The present means for configuring Python's logging package is either by using the logging API to configure logging programmatically, or else by means of ConfigParser-based configuration files. Programmatic configuration, while offering maximal control, fixes the configuration in Python code. This does not facilitate changing it easily at runtime, and, as a result, the ability to flexibly turn the verbosity of logging up and down for different parts of a using application is lost. This limits the usability of logging as an aid to diagnosing problems - and sometimes, logging is the only diagnostic aid available in production environments. The ConfigParser-based configuration system is usable, but does not allow its users to configure all aspects of the logging package. For example, Filters cannot be configured using this system. Furthermore, the ConfigParser format appears to engender dislike (sometimes strong dislike) in some quarters. Though it was chosen because it was the only configuration format supported in the Python standard at that time, many people regard it (or perhaps just the particular schema chosen for logging's configuration) as 'crufty' or 'ugly', in some cases apparently on purely aesthetic grounds. Recent versions of Python include JSON support in the standard library, and this is also usable as a configuration format. In other environments, such as Google App Engine, YAML is used to configure applications, and usually the configuration of logging would be considered an integral part of the application configuration. Although the standard library does not contain YAML support at present, support for both JSON and YAML can be provided in a common way because both of these serialization formats allow deserialization of Python dictionaries. By providing a way to configure logging by passing the configuration in a dictionary, logging will be easier to configure not only for users of JSON and/or YAML, but also for users of bespoke configuration methods, by providing a common format in which to describe the desired configuration. Another drawback of the current ConfigParser-based configuration system is that it does not support incremental configuration: a new configuration completely replaces the existing configuration. Although full flexibility for incremental configuration is difficult to provide in a multi-threaded environment, the new configuration mechanism will allow the provision of limited support for incremental configuration. Specification ============= The specification consists of two parts: the API and the format of the dictionary used to convey configuration information (i.e. the schema to which it must conform). Naming ------ Historically, the logging package has not been PEP 8 conformant [1]_. At some future time, this will be corrected by changing method and function names in the package in order to conform with PEP 8. However, in the interests of uniformity, the proposed additions to the API use a naming scheme which is consistent with the present scheme used by logging. API --- The logging.config module will have the following additions: * A class, called ``DictConfigurator``, whose constructor is passed the dictionary used for configuration, and which has a ``configure()`` method. * A callable, called ``dictConfigClass``, which will (by default) be set to ``DictConfigurator``. This is provided so that if desired, ``DictConfigurator`` can be replaced with a suitable user-defined implementation. * A function, called ``dictConfig()``, which takes a single argument - the dictionary holding the configuration. This function will call ``dictConfigClass`` passing the specified dictionary, and then call the ``configure()`` method on the returned object to actually put the configuration into effect:: def dictConfig(config): dictConfigClass(config).configure() Dictionary Schema - Overview ---------------------------- Before describing the schema in detail, it is worth saying a few words about object connections, support for user-defined objects and access to external and internal objects. Object connections '''''''''''''''''' The schema is intended to describe 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 an 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:: handlers: h1: #This is an id # configuration of handler with id h1 goes here h2: #This is another id # configuration of handler with id h2 goes here loggers: foo.bar.baz: # other configuration for logger 'foo.bar.baz' handlers: [h1, h2] (Note: YAML will be used in this document as it is 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 other objects can be any string value (such as ``h1``, ``h2`` 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``. User-defined objects '''''''''''''''''''' The schema should support 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 the configuration - for user-defined logger classes.) Objects to be configured will typically be 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 will need to provide a 'factory' - a callable which is called with a configuration dictionary and which returns the instantiated object. This will be signalled by 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 located using normal import mechanisms and 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 will need to refer to objects external to the configuration, for example ``sys.stderr``. If the configuration dict is constructed using Python code then 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 will look 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 will be done in a way analogous to protocol handling: there will be a generic mechanism to look for prefixes which match the regular expression ``^(?P[a-z]+)://(?P.*)$`` 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. The implementation will provide for a set of standard prefixes such as ``ext://`` but it will be possible to disable the mechanism completely or provide additional or different prefixes for special handling. 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 needs to be provided for the case of user-defined objects which are not known to logging. For example, take the instance of ``logging.handlers.MemoryHandler``, which takes a ``target`` 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 a ``alternate`` handler, the configuration system would not know that the ``alternate`` referred to a handler. To cater for this, a generic resolution system will be provided which allows the user to specify:: handlers: file: # configuration of file handler goes here custom: (): my.package.MyHandler alternate: int://handlers.file The literal string ``'int://handlers.file'`` will be resolved in an analogous way to the strings with the ``ext://`` prefix, but looking in the configuration itself rather than the import namespace. The mechanism will allow 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 ``'int://handlers'`` would resolve to the dict with key ``handlers``, the string ``'int://handlers.email`` would resolve to the dict with key ``email`` in the ``handlers`` dict, and so on. The string ``'int://handlers.email.toaddrs[1]`` would resolve to ``'dev_team.domain.tld'`` and the string ``'int://handlers.email.toaddrs[0]'`` would resolve to the value ``'support_team@domain.tld'``. The ``subject`` value could be accessed using either ``'int://handlers.email.subject'`` or, equivalently, ``'int://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. Dictionary Schema - Detail -------------------------- 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 above is used to instantiate; otherwise, the context is used to determine how 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 ``logging.Formatter`` instance. * `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 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`_, below. A Working Example ----------------- The following is an actual working configuration in YAML format (except that the email addresses are bogus):: formatters: brief: format: '%(levelname)-8s: %(name)-15s: %(message)s' precise: format: '%(asctime)s %(name)-15s %(levelname)-8s %(message)s' filters: allow_foo: name: foo handlers: console: class : logging.StreamHandler formatter: brief level : INFO stream : ext://sys.stdout filters: [allow_foo] file: class : logging.handlers.RotatingFileHandler formatter: precise filename: logconfig.log maxBytes: 1024 backupCount: 3 debugfile: class : logging.FileHandler formatter: precise filename: logconfig-detail.log mode: a 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. loggers: foo: level : ERROR handlers: [debugfile] spam: level : CRITICAL handlers: [debugfile] propagate: no bar.baz: level: WARNING root: level : DEBUG handlers : [console, file] Incremental Configuration ========================= It is difficult to provide complete flexibility for incremental configuration. For example, because objects such as handlers, 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. For example, if an initial call is made to configure the system where logger ``foo`` has a handler with id ``console`` attached, then a subsequent call to configure a logger ``bar`` with id ``console`` would create a new handler instance, as the id ``console`` from the first call isn't kept. 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 can be controlled just by setting levels (and perhaps propagation flags). Thus, when the ``incremental`` key of a configuration dict is present and is ``True``, the system will ignore any ``formatters``, ``filters``, ``handlers`` entries completely, and process only the ``level`` and ``propagate`` settings in the ``loggers`` and ``root`` entries. Configuration Errors ==================== If an error is encountered during configuration, the system 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 * An invalid logger name * Inability to resolve to an internal or external object References ========== .. [1] PEP 8, Style Guide for Python Code, van Rossum, Warsaw (http://www.python.org/dev/peps/pep-0008) Copyright ========= This document has been placed in the public domain. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End: