Logging

Autor:

Vinay Sajip <vinay_sajip at red-dove dot com>

Essa página contém informações relativas a tutoriais. Para links de informações de referência e logging cookbook, por favor veja Outros recursos.

Tutorial Básico de Logging

Logging é uma maneira de rastrear eventos que acontecem quando algum software executa. O desenvolvedor de software adiciona chamadas de logging no código para indicar que determinado evento ocorreu. Um evento é descrito por uma mensagem descritiva que pode opcionalmente conter o dado de uma variável (ex.: dado que é potencialmente diferente pra cada ocorrência do evento). Eventos também tem um peso que o desenvolvedor atribui para o evento; o peso pode também ser chamada de níveis ou severidade.

Quando usar logging

Você pode acessar a funcionalidade de logs criando um logger via logger = getLogger(__name__) e, em seguida, chamar os métodos debug(), info(), warning(), error() e critical(). Para determinar quando usar logging e ver quais métodos de logger utilizar e quando, consulte a tabela abaixo. Ele indica, para cada conjunto de tarefas comuns, a melhor ferramenta a ser usada para essa tarefa.

Tarefa que você quer performar	A melhor ferramenta para a tarefa
Exibir saída do console para uso ordinário de um script de linha de comando ou programa.	print()
Relata eventos que podem ocorrer durante a operação normal de um programa (ex: para monitoramento do status ou investigação de falha)	O método info() de um logger (ou debug() para saída muito detalhada para fins de diagnóstico)
Emite um aviso sobre um evento de tempo de execução específico	warnings.warn() na biblioteca de código se o problema é evitável e a aplicação cliente deve ser modificada para eliminar o alerta. O método warning() de um logger se não houver nada que a aplicação cliente possa fazer sobre a situação, mas o evento ainda deve ser registrado
Relata um erro sobre um evento de tempo de execução específico	Levantando uma exceção
Relatar supressão de um erro sem levantar uma exceção (ex: manipulador de erros em um processo de servidor em longa execução)	Um método error(), exception() ou critical() do logger, conforme apropriado para o erro específico e domínio da aplicação

Os métodos de logger são nomeados de acordo com o nível ou severidade dos eventos que eles são usados ​para rastrear. Os níveis padrão e sua aplicabilidade são descritos abaixo (em ordem crescente de severidade):

Nível	Quando é usado
DEBUG	Informação detalhada, tipicamente de interesse apenas quando diagnosticando problemas.
INFO	Confirmação de que as coisas estão funcionando como esperado.
WARNING	Uma indicação que algo inesperado aconteceu, ou um indicativo que algum problema em um futuro próximo (ex.: ‘pouco espaço em disco’). O software está ainda funcionando como esperado.
ERROR	Por conta de um problema mais grave, o software não conseguiu executar alguma função.
CRITICAL	Um erro grave, indicando que o programa pode não conseguir continuar rodando.

O nível padrão é WARNING, que significa que só eventos deste nível e acima serão rastreados, a não ser que o pacote logging esteja configurado para fazer de outra forma.

Eventos que são rastreados podem ser tratados de diferentes formas. O jeito mais simples de lidar com eventos rastreados é exibi-los no console. Outra maneira comum é gravá-los em um arquivo de disco.

Um exemplo simples

Um exemplo bastante simples é:

import logging
logging.warning('Watch out!')  # imprimirá uma mensagem no console
logging.info('I told you so')  # não imprimirá nada

Se você digitar essas linhas no script e executá-lo, você verá:

WARNING:root:Watch out!

impresso no console. A mensagem INFO não aparece porque o nível padrão é WARNING. A mensagem impressa inclui a indicação do nível e a descrição do evento fornecido na chamada de registro, ou seja, ‘Watch out!’. A saída real pode ser formatada de maneira bastante flexível, se necessário; as opções de formatação também serão explicadas mais tarde.

Observe que neste exemplo, usamos funções diretamente no módulo logging, como logging.debug, ao invés de criar um logger e chamar funções nele. Essas funções operam no logger raiz, mas podem ser úteis, pois chamarão basicConfig() para você se ele ainda não tiver sido chamado, como neste exemplo. Em programas maiores, você geralmente desejará controlar explicitamente a configuração de logging, no entanto - então, por esse motivo, assim como por outros, é melhor criar loggers e chamar seus métodos.

Logging em um arquivo

Uma situação muito comum é a de registrar eventos de log em um arquivo, então vamos dar uma olhada nisso a seguir. Certifique-se de tentar o seguinte em um interpretador Python recém-iniciado, e não continue apenas a partir da sessão descrita acima:

import logging
logger = logging.getLogger(__name__)
logging.basicConfig(filename='example.log', encoding='utf-8', level=logging.DEBUG)
logger.debug('This message should go to the log file')
logger.info('So should this')
logger.warning('And this, too')
logger.error('And non-ASCII stuff, too, like Øresund and Malmö')

Alterado na versão 3.9: The encoding argument was added. In earlier Python versions, or if not specified, the encoding used is the default value used by open(). While not shown in the above example, an errors argument can also now be passed, which determines how encoding errors are handled. For available values and the default, see the documentation for open().

E agora se nós abrirmos o arquivo e olharmos o que temos, deveremos encontrar essas mensagens de log:

DEBUG:__main__:This message should go to the log file
INFO:__main__:So should this
WARNING:__main__:And this, too
ERROR:__main__:And non-ASCII stuff, too, like Øresund and Malmö

Este exemplo também mostra como você pode configurar o nível do logging que age como um limiar para o rastreamento. Neste caso, por causa que definimos o limiar como DEBUG, todas as mensagens foram exibidas.

Se você quer definir o nível de logging a partir de uma opção da linha de comando como:

--log=INFO

e você tem o valor do parâmetro passado pelo --log em alguma variável loglevel, você pode usar:

getattr(logging, loglevel.upper())

para obter o valor que você passará para a basicConfig() via o level argumento. Você pode querer verificar qualquer erros introduzidos pelo usuário, talvez como no exemplo a seguir:

# assuming loglevel is bound to the string value obtained from the
# command line argument. Convert to upper case to allow the user to
# specify --log=DEBUG or --log=debug
numeric_level = getattr(logging, loglevel.upper(), None)
if not isinstance(numeric_level, int):
    raise ValueError('Invalid log level: %s' % loglevel)
logging.basicConfig(level=numeric_level, ...)

The call to basicConfig() should come before any calls to a logger’s methods such as debug(), info(), etc. Otherwise, that logging event may not be handled in the desired manner.

Se você executar o script acima diversas vezes, as mensagens das sucessivas execuções serão acrescentadas ao arquivo example.log. Se você quer que cada execução seja iniciada novamente, não guardando as mensagens das execuções anteriores, você pode especificar o filemode argumento, mudando a chamada no exemplo acima:

logging.basicConfig(filename='example.log', filemode='w', level=logging.DEBUG)

A saída será a mesma de antes, mas o arquivo de log não será mais incrementado, desta forma as mensagens de execuções anteriores serão perdidas.

Logging dados de uma variável

Para logar o dado de uma variável, use o formato string para a mensagem descritiva do evento e adicione a variável como argumento. Exemplo:

import logging
logging.warning('%s before you %s', 'Look', 'leap!')

exibirá:

WARNING:root:Look before you leap!

Como você pode ver, para combinar uma variável de dados na mensagem descritiva do evento usamos o velho, %-s estilo de formatação de string. Isto é usado para garantir compatibilidade com as versões anteriores: o pacote logging pré-data novas opções de formatação como str.format() e string.Template. Estas novas opções de formatação são suportadas, mas explorá-las esta fora do escopo deste tutorial: veja Using particular formatting styles throughout your application  para mais informações.

Alterar o formato das mensagens exibidas

Para mudar o formato usado para exibir mensagens, você precisa especificar o formato que quer usar:

import logging
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)
logging.debug('This message should appear on the console')
logging.info('So should this')
logging.warning('And this, too')

que vai exibir:

DEBUG:This message should appear on the console
INFO:So should this
WARNING:And this, too

Note que a palavra ‘root’ que apareceu nos exemplos anteriores desapareceu. Para todas as configurações que possam aparecer na formatação de strings, você pode consultar a documentação Atributos LogRecord, mas para uso simples, você só precisa do levelname (severidade), message (descrição do evento, incluindo a variável com dados) e talvez exibir quando o evento ocorreu. Isto esta descrito na próxima seção.

Exibindo data/hora em mensagens

Para exibir a data e hora de um evento, você pode colocar ‘%(asctime)s’ no seu formato string:

import logging
logging.basicConfig(format='%(asctime)s %(message)s')
logging.warning('is when this event was logged.')

que deve exibir algo assim:

2010-12-12 11:41:42,612 is when this event was logged.

O formato padrão para data/hora (mostrado abaixo) é como a ISO8601 ou RFC 3339. Se você precisa de mais controle sobre a formatação de data/hora, informe o datefmt argumento para basicConfig, como neste exemplo:

import logging
logging.basicConfig(format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p')
logging.warning('is when this event was logged.')

que deve exibir algo assim:

12/12/2010 11:46:36 AM is when this event was logged.

O formato do argumento datefmt é o mesmo suportado por time.strftime().

Próximos Passos

Concluímos aqui o tutorial básico. Isto deve ser o bastante para você começar a trabalhar com logging. Existe muito mais que o pacote de logging pode oferecer, mas para ter o melhor disto, você precisará investir um pouco mais do seu tempo lendo as próximas seções. Se você está pronto para isso, pegue sua bebida favorita e continue.

If your logging needs are simple, then use the above examples to incorporate logging into your own scripts, and if you run into problems or don’t understand something, please post a question on the comp.lang.python Usenet group (available at https://groups.google.com/g/comp.lang.python) and you should receive help before too long.

Ainda por aqui? Você pode continuar lendo as seções seguintes, que tem um tutorial mais avançado que o básico acima. Depois disso, você pode dar uma olhada no Livro de Receitas do Logging.

Tutorial Avançado do Logging

A biblioteca de logging tem uma abordagem modular e oferece algumas categorias de componentes: loggers, handlers, filters, e formatters.

• Loggers expõem a interface que o código da aplicação usa diretamente.

• Handlers enviam os registros do evento (criados por loggers) aos destinos apropriados.

• Filters fornecem uma facilidade granular para determinar quais registros de eventos enviar à saída.

• Formatters especificam o layout dos registros de eventos na saída final.

Uma informação de um evento de log é passada entre loggers, handlers, filters e formatters em uma instância de uma LogRecord

Logging é executada chamando métodos nas instâncias da Logger classe, também chamado de loggers. Cada instância tem um nome, e eles são conceitualmente organizados em uma hierarquia de espaço de nomes usando pontos como separadores. Por exemplo, um logger nomeado com ‘scan’ é o pai do logger ‘scan.text’, ‘scan.html’ e ‘scan.pdf’. Você pode nomear o logger do jeito que preferir, e indicar a área de uma aplicação em que uma mensagem de log origina.

Uma boa convenção para usar quando nomear loggers é usar um módulo-nível logger, em cada módulo que usa o logging, nomeado como sugerido abaixo:

logger = logging.getLogger(__name__)

Isto significa que o nome de um logger rastreia a hierarquia do pacote/módulo, e isto é obviamente intuitivo onde os eventos estão sendo registrados apenas pelo nome do logger.

The root of the hierarchy of loggers is called the root logger. That’s the logger used by the functions debug(), info(), warning(), error() and critical(), which just call the same-named method of the root logger. The functions and the methods have the same signatures. The root logger’s name is printed as ‘root’ in the logged output.

It is, of course, possible to log messages to different destinations. Support is included in the package for writing log messages to files, HTTP GET/POST locations, email via SMTP, generic sockets, queues, or OS-specific logging mechanisms such as syslog or the Windows NT event log. Destinations are served by handler classes. You can create your own log destination class if you have special requirements not met by any of the built-in handler classes.

By default, no destination is set for any logging messages. You can specify a destination (such as console or file) by using basicConfig() as in the tutorial examples. If you call the functions debug(), info(), warning(), error() and critical(), they will check to see if no destination is set; and if one is not set, they will set a destination of the console (sys.stderr) and a default format for the displayed message before delegating to the root logger to do the actual message output.

O formato padrão definido por basicConfig() para mensagens é:

severity:logger name:message

You can change this by passing a format string to basicConfig() with the format keyword argument. For all options regarding how a format string is constructed, see Formatter Objects.

Fluxo de Logging

O fluxo dos eventos de log em loggers e handlers é ilustrado no diagrama a seguir.

Registradores

Logger objects have a threefold job. First, they expose several methods to application code so that applications can log messages at runtime. Second, logger objects determine which log messages to act upon based upon severity (the default filtering facility) or filter objects. Third, logger objects pass along relevant log messages to all interested log handlers.

Os métodos mais usados em objetos logger se enquadram em duas categorias: configuração e envio de mensagem.

Esses são os métodos de configuração mais comuns:

• Logger.setLevel() specifies the lowest-severity log message a logger will handle, where debug is the lowest built-in severity level and critical is the highest built-in severity. For example, if the severity level is INFO, the logger will handle only INFO, WARNING, ERROR, and CRITICAL messages and will ignore DEBUG messages.

• Logger.addHandler() and Logger.removeHandler() add and remove handler objects from the logger object. Handlers are covered in more detail in Manipuladores.

• Logger.addFilter() and Logger.removeFilter() add and remove filter objects from the logger object. Filters are covered in more detail in Filter Objects.

Você não precisa sempre invocar esses métodos em cada logger que criar. Veja os últimos dois paragrafos nessa seção.

Com o objeto logger configurado, os seguintes métodos criam mensagens de log:

• Logger.debug(), Logger.info(), Logger.warning(), Logger.error(), and Logger.critical() all create log records with a message and a level that corresponds to their respective method names. The message is actually a format string, which may contain the standard string substitution syntax of %s, %d, %f, and so on. The rest of their arguments is a list of objects that correspond with the substitution fields in the message. With regard to **kwargs, the logging methods care only about a keyword of exc_info and use it to determine whether to log exception information.

• Logger.exception() creates a log message similar to Logger.error(). The difference is that Logger.exception() dumps a stack trace along with it. Call this method only from an exception handler.

• Logger.log() takes a log level as an explicit argument. This is a little more verbose for logging messages than using the log level convenience methods listed above, but this is how to log at custom log levels.

getLogger() returns a reference to a logger instance with the specified name if it is provided, or root if not. The names are period-separated hierarchical structures. Multiple calls to getLogger() with the same name will return a reference to the same logger object. 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.

Loggers have a concept of effective level. If a level is not explicitly set on a logger, the level of its parent is used instead as its effective level. If the parent has no explicit level set, its parent is examined, and so on - all ancestors are searched until an explicitly set level is found. The root logger always has an explicit level set (WARNING by default). When deciding whether to process an event, the effective level of the logger is used to determine whether the event is passed to the logger’s handlers.

Child loggers propagate messages up to the handlers associated with their ancestor loggers. Because of this, it is unnecessary to define and configure handlers for all the loggers an application uses. It is sufficient to configure handlers for a top-level logger and create child loggers as needed. (You can, however, turn off propagation by setting the propagate attribute of a logger to False.)

Manipuladores

Handler objects are responsible for dispatching the appropriate log messages (based on the log messages’ severity) to the handler’s specified destination. Logger objects can add zero or more handler objects to themselves with an addHandler() method. As an example scenario, an application may want to send all log messages to a log file, all log messages of error or higher to stdout, and all messages of critical to an email address. This scenario requires three individual handlers where each handler is responsible for sending messages of a specific severity to a specific location.

A biblioteca padrão possui alguns tipos de handler (veja Useful Handlers); os tutoriais usam principalmente StreamHandler e FileHandler nos seus exemplos.

There are very few methods in a handler for application developers to concern themselves with. The only handler methods that seem relevant for application developers who are using the built-in handler objects (that is, not creating custom handlers) are the following configuration methods:

• The setLevel() method, just as in logger objects, specifies the lowest severity that will be dispatched to the appropriate destination. Why are there two setLevel() methods? The level set in the logger determines which severity of messages it will pass to its handlers. The level set in each handler determines which messages that handler will send on.

• setFormatter() selects a Formatter object for this handler to use.

• addFilter() and removeFilter() respectively configure and deconfigure filter objects on handlers.

Application code should not directly instantiate and use instances of Handler. Instead, the Handler class is a base class that defines the interface that all handlers should have and establishes some default behavior that child classes can use (or override).

Formatadores

Formatter objects configure the final order, structure, and contents of the log message. Unlike the base logging.Handler class, application code may instantiate formatter classes, although you could likely subclass the formatter if your application needs special behavior. The constructor takes three optional arguments – a message format string, a date format string and a style indicator.

logging.Formatter.__init__(fmt=None, datefmt=None, style='%')

If there is no message format string, the default is to use the raw message. If there is no date format string, the default date format is:

%Y-%m-%d %H:%M:%S

with the milliseconds tacked on at the end. The style is one of '%', '{', or '$'. If one of these is not specified, then '%' will be used.

If the style is '%', the message format string uses %(<dictionary key>)s styled string substitution; the possible keys are documented in Atributos LogRecord. If the style is '{', the message format string is assumed to be compatible with str.format() (using keyword arguments), while if the style is '$' then the message format string should conform to what is expected by string.Template.substitute().

Alterado na versão 3.2: Adicionado o parâmetro style.

A string de formato a seguir vai escrever o tempo em um formato légivel para humanos, a severidade da mensagem, e o conteúdo da mensagem, nessa ordem:

'%(asctime)s - %(levelname)s - %(message)s'

Formatters use a user-configurable function to convert the creation time of a record to a tuple. By default, time.localtime() is used; to change this for a particular formatter instance, set the converter attribute of the instance 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 (to time.gmtime for GMT display).

Configurando Logging

Programadores podem configurar logging de três formas:

1. Creating loggers, handlers, and formatters explicitly using Python code that calls the configuration methods listed above.

2. Creating a logging config file and reading it using the fileConfig() function.

3. Creating a dictionary of configuration information and passing it to the dictConfig() function.

For the reference documentation on the last two options, see Configuration functions. The following example configures a very simple logger, a console handler, and a simple formatter using Python code:

import logging

# create logger
logger = logging.getLogger('simple_example')
logger.setLevel(logging.DEBUG)

# create console handler and set level to debug
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)

# create formatter
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')

# add formatter to ch
ch.setFormatter(formatter)

# add ch to logger
logger.addHandler(ch)

# 'application' code
logger.debug('debug message')
logger.info('info message')
logger.warning('warn message')
logger.error('error message')
logger.critical('critical message')

Running this module from the command line produces the following output:

$ python simple_logging_module.py
2005-03-19 15:10:26,618 - simple_example - DEBUG - debug message
2005-03-19 15:10:26,620 - simple_example - INFO - info message
2005-03-19 15:10:26,695 - simple_example - WARNING - warn message
2005-03-19 15:10:26,697 - simple_example - ERROR - error message
2005-03-19 15:10:26,773 - simple_example - CRITICAL - critical message

The following Python module creates a logger, handler, and formatter nearly identical to those in the example listed above, with the only difference being the names of the objects:

import logging
import logging.config

logging.config.fileConfig('logging.conf')

# create logger
logger = logging.getLogger('simpleExample')

# 'application' code
logger.debug('debug message')
logger.info('info message')
logger.warning('warn message')
logger.error('error message')
logger.critical('critical message')

Aqui está o arquivo logging.conf:

[loggers]
keys=root,simpleExample

[handlers]
keys=consoleHandler

[formatters]
keys=simpleFormatter

[logger_root]
level=DEBUG
handlers=consoleHandler

[logger_simpleExample]
level=DEBUG
handlers=consoleHandler
qualname=simpleExample
propagate=0

[handler_consoleHandler]
class=StreamHandler
level=DEBUG
formatter=simpleFormatter
args=(sys.stdout,)

[formatter_simpleFormatter]
format=%(asctime)s - %(name)s - %(levelname)s - %(message)s

The output is nearly identical to that of the non-config-file-based example:

$ python simple_logging_config.py
2005-03-19 15:38:55,977 - simpleExample - DEBUG - debug message
2005-03-19 15:38:55,979 - simpleExample - INFO - info message
2005-03-19 15:38:56,054 - simpleExample - WARNING - warn message
2005-03-19 15:38:56,055 - simpleExample - ERROR - error message
2005-03-19 15:38:56,130 - simpleExample - CRITICAL - critical message

You can see that the config file approach has a few advantages over the Python code approach, mainly separation of configuration and code and the ability of noncoders to easily modify the logging properties.

Aviso

The fileConfig() function takes a default parameter, disable_existing_loggers, which defaults to True for reasons of backward compatibility. This may or may not be what you want, since it will cause any non-root loggers existing before the fileConfig() call to be disabled unless they (or an ancestor) are explicitly named in the configuration. Please refer to the reference documentation for more information, and specify False for this parameter if you wish.

The dictionary passed to dictConfig() can also specify a Boolean value with key disable_existing_loggers, which if not specified explicitly in the dictionary also defaults to being interpreted as True. This leads to the logger-disabling behaviour described above, which may not be what you want - in which case, provide the key explicitly with a value of False.

Note that the class names referenced in config files need to be either relative to the logging module, or absolute values which can be resolved using normal import mechanisms. Thus, you could use either WatchedFileHandler (relative to the logging module) or mypackage.mymodule.MyHandler (for a class defined in package mypackage and module mymodule, where mypackage is available on the Python import path).

In Python 3.2, a new means of configuring logging has been introduced, using dictionaries to hold configuration information. This provides a superset of the functionality of the config-file-based approach outlined above, and is the recommended configuration method for new applications and deployments. Because a Python dictionary is used to hold configuration information, and since you can populate that dictionary using different means, you have more options for configuration. For example, you can use a configuration file in JSON format, or, if you have access to YAML processing functionality, a file in YAML format, to populate the configuration dictionary. Or, of course, you can construct the dictionary in Python code, receive it in pickled form over a socket, or use whatever approach makes sense for your application.

Here’s an example of the same configuration as above, in YAML format for the new dictionary-based approach:

version: 1
formatters:
  simple:
    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
handlers:
  console:
    class: logging.StreamHandler
    level: DEBUG
    formatter: simple
    stream: ext://sys.stdout
loggers:
  simpleExample:
    level: DEBUG
    handlers: [console]
    propagate: no
root:
  level: DEBUG
  handlers: [console]

For more information about logging using a dictionary, see Configuration functions.

O que acontece se nenhuma configuração é fornecida

If no logging configuration is provided, it is possible to have a situation where a logging event needs to be output, but no handlers can be found to output the event.

The event is output using a ‘handler of last resort’, stored in lastResort. This internal handler is not associated with any logger, and acts like a StreamHandler which writes the event description message to the current value of sys.stderr (therefore respecting any redirections which may be in effect). No formatting is done on the message - just the bare event description message is printed. The handler’s level is set to WARNING, so all events at this and greater severities will be output.

Alterado na versão 3.2: Para versões do Python anteriores à 3.2, o comportamento é o seguinte:

• If raiseExceptions is False (production mode), the event is silently dropped.

• If raiseExceptions is True (development mode), a message ‘No handlers could be found for logger X.Y.Z’ is printed once.

To obtain the pre-3.2 behaviour, lastResort can be set to None.

Configuring Logging for a Library

When developing a library which uses logging, you should take care to document how the library uses logging - for example, the names of loggers used. Some consideration also needs to be given to its logging configuration. If the using application does not use logging, and library code makes logging calls, then (as described in the previous section) events of severity WARNING and greater will be printed to sys.stderr. This is regarded as the best default behaviour.

If for some reason you don’t want these messages printed in the absence of any logging configuration, you can attach a do-nothing handler to the top-level logger for your library. This avoids the message being printed, since a handler will always be found for the library’s events: it just doesn’t produce any output. If the library user configures logging for application use, presumably that configuration will add some handlers, and if levels are suitably configured then logging calls made in library code will send output to those handlers, as normal.

A do-nothing handler is included in the logging package: NullHandler (since Python 3.1). An instance of this handler could be added to the top-level logger of the logging namespace used by the library (if you want to prevent your library’s logged events being output to sys.stderr in the absence of logging configuration). If all logging by a library foo is done using loggers with names matching ‘foo.x’, ‘foo.x.y’, etc. then the code:

import logging
logging.getLogger('foo').addHandler(logging.NullHandler())

should have the desired effect. If an organisation produces a number of libraries, then the logger name specified can be ‘orgname.foo’ rather than just ‘foo’.

Nota

It is strongly advised that you do not log to the root logger in your library. Instead, use a logger with a unique and easily identifiable name, such as the __name__ for your library’s top-level package or module. Logging to the root logger will make it difficult or impossible for the application developer to configure the logging verbosity or handlers of your library as they wish.

Nota

It is strongly advised that you do not add any handlers other than NullHandler to your library’s loggers. This is because the configuration of handlers is the prerogative of the application developer who uses your library. The application developer knows their target audience and what handlers are most appropriate for their application: if you add handlers ‘under the hood’, you might well interfere with their ability to carry out unit tests and deliver logs which suit their requirements.

Níveis de Logging

Os valores númericos dos níveis de logging estão listados na tabela abaixo. Eles são principalmente de interesse se você quiser definir seus próprios níveis, e precisa deles para definir seus valores específicos relativos aos níveis predefinidos. Se você define um nível com o mesmo valor númerico, ele sobreescreve o valor predefinido; o nome predefinido é perdido.

Nível	Valor numérico
CRITICAL	50
ERROR	40
WARNING	30
INFO	20
DEBUG	10
NOTSET	0

Levels can also be associated with loggers, being set either by the developer or through loading a saved logging configuration. When a logging method is called on a logger, the logger compares its own level with the level associated with the method call. If the logger’s level is higher than the method call’s, no logging message is actually generated. This is the basic mechanism controlling the verbosity of logging output.

Logging messages are encoded as instances of the LogRecord class. When a logger decides to actually log an event, a LogRecord instance is created from the logging message.

Logging messages are subjected to a dispatch mechanism through the use of handlers, which are instances of subclasses of the Handler class. Handlers are responsible for ensuring that a logged message (in the form of a LogRecord) ends up in a particular location (or set of locations) which is useful for the target audience for that message (such as end users, support desk staff, system administrators, developers). Handlers are passed LogRecord instances intended for particular destinations. Each logger can have zero, one or more handlers associated with it (via the addHandler() method of Logger). In addition to any handlers directly associated with a logger, all handlers associated with all ancestors of the logger are called to dispatch the message (unless the propagate flag for a logger is set to a false value, at which point the passing to ancestor handlers stops).

Just as for loggers, handlers can have levels associated with them. A handler’s level acts as a filter in the same way as a logger’s level does. If a handler decides to actually dispatch an event, the emit() method is used to send the message to its destination. Most user-defined subclasses of Handler will need to override this emit().

Custom Levels

Defining your own levels is possible, but should not be necessary, as the existing levels have been chosen on the basis of practical experience. However, if you are convinced that you need custom levels, great care should be exercised when doing this, and it is possibly a very bad idea to define custom levels if you are developing a library. That’s because if multiple library authors all define their own custom levels, there is a chance that the logging output from such multiple libraries used together will be difficult for the using developer to control and/or interpret, because a given numeric value might mean different things for different libraries.

Useful Handlers

Em adição à classe base Handler, muitas subclasses úteis são fornecidas:

1. StreamHandler instances send messages to streams (file-like objects).

2. FileHandler instances send messages to disk files.

3. BaseRotatingHandler is the base class for handlers that rotate log files at a certain point. It is not meant to be instantiated directly. Instead, use RotatingFileHandler or TimedRotatingFileHandler.

4. RotatingFileHandler instances send messages to disk files, with support for maximum log file sizes and log file rotation.

5. TimedRotatingFileHandler instances send messages to disk files, rotating the log file at certain timed intervals.

6. SocketHandler instances send messages to TCP/IP sockets. Since 3.4, Unix domain sockets are also supported.

7. DatagramHandler instances send messages to UDP sockets. Since 3.4, Unix domain sockets are also supported.

8. SMTPHandler instances send messages to a designated email address.

9. SysLogHandler instances send messages to a Unix syslog daemon, possibly on a remote machine.

10. NTEventLogHandler instances send messages to a Windows NT/2000/XP event log.

11. MemoryHandler instances send messages to a buffer in memory, which is flushed whenever specific criteria are met.

12. HTTPHandler instances send messages to an HTTP server using either GET or POST semantics.

13. WatchedFileHandler instances watch the file they are logging to. If the file changes, it is closed and reopened using the file name. This handler is only useful on Unix-like systems; Windows does not support the underlying mechanism used.

14. QueueHandler instances send messages to a queue, such as those implemented in the queue or multiprocessing modules.

15. NullHandler instances do nothing with error messages. They are used by library developers who want to use logging, but want to avoid the ‘No handlers could be found for logger XXX’ message which can be displayed if the library user has not configured logging. See Configuring Logging for a Library for more information.

Adicionado na versão 3.1: A classe NullHandler.

Adicionado na versão 3.2: A classe QueueHandler.

The NullHandler, StreamHandler and FileHandler classes are defined in the core logging package. The other handlers are defined in a sub-module, logging.handlers. (There is also another sub-module, logging.config, for configuration functionality.)

Logged messages are formatted for presentation through instances of the Formatter class. They are initialized with a format string suitable for use with the % operator and a dictionary.

For formatting multiple messages in a batch, instances of BufferingFormatter can be used. In addition to the format string (which is applied to each message in the batch), there is provision for header and trailer format strings.

When filtering based on logger level and/or handler level is not enough, instances of Filter can be added to both Logger and Handler instances (through their addFilter() method). Before deciding to process a message further, both loggers and handlers consult all their filters for permission. If any filter returns a false value, the message is not processed further.

The basic Filter functionality allows filtering by specific logger name. If this feature is used, messages sent to the named logger and its children are allowed through the filter, and all others dropped.

Exceções levantadas durante logging

The logging package is designed to swallow exceptions which occur while logging in production. This is so that errors which occur while handling logging events - such as logging misconfiguration, network or other similar errors - do not cause the application using logging to terminate prematurely.

SystemExit and KeyboardInterrupt exceptions are never swallowed. Other exceptions which occur during the emit() method of a Handler subclass are passed to its handleError() method.

The default implementation of handleError() in Handler checks to see if a module-level variable, raiseExceptions, is set. If set, a traceback is printed to sys.stderr. If not set, the exception is swallowed.

Nota

The default value of raiseExceptions is True. This is because during development, you typically want to be notified of any exceptions that occur. It’s advised that you set raiseExceptions to False for production usage.

Usando objetos arbitrários como mensagens

In the preceding sections and examples, it has been assumed that the message passed when logging the event is a string. However, this is not the only possibility. You can pass an arbitrary object as a message, and its __str__() method will be called when the logging system needs to convert it to a string representation. In fact, if you want to, you can avoid computing a string representation altogether - for example, the SocketHandler emits an event by pickling it and sending it over the wire.

Optimização

Formatting of message arguments is deferred until it cannot be avoided. However, computing the arguments passed to the logging method can also be expensive, and you may want to avoid doing it if the logger will just throw away your event. To decide what to do, you can call the isEnabledFor() method which takes a level argument and returns true if the event would be created by the Logger for that level of call. You can write code like this:

if logger.isEnabledFor(logging.DEBUG):
    logger.debug('Message with %s, %s', expensive_func1(),
                                        expensive_func2())

so that if the logger’s threshold is set above DEBUG, the calls to expensive_func1 and expensive_func2 are never made.

Nota

In some cases, isEnabledFor() can itself be more expensive than you’d like (e.g. for deeply nested loggers where an explicit level is only set high up in the logger hierarchy). In such cases (or if you want to avoid calling a method in tight loops), you can cache the result of a call to isEnabledFor() in a local or instance variable, and use that instead of calling the method each time. Such a cached value would only need to be recomputed when the logging configuration changes dynamically while the application is running (which is not all that common).

There are other optimizations which can be made for specific applications which need more precise control over what logging information is collected. Here’s a list of things you can do to avoid processing during logging which you don’t need:

O que você não quer coletar	How to avoid collecting it
Information about where calls were made from.	Set logging._srcfile to None. This avoids calling sys._getframe(), which may help to speed up your code in environments like PyPy (which can’t speed up code that uses sys._getframe()).
Threading information.	Set logging.logThreads to False.
Current process ID (os.getpid())	Set logging.logProcesses to False.
Current process name when using multiprocessing to manage multiple processes.	Set logging.logMultiprocessing to False.
Current asyncio.Task name when using asyncio.	Set logging.logAsyncioTasks to False.

Also note that the core logging module only includes the basic handlers. If you don’t import logging.handlers and logging.config, they won’t take up any memory.

Outros recursos

Ver também

Módulo logging

Referência da API para o módulo de logging.

Módulo logging.config

API de configuração para o módulo logging.

Módulo logging.handlers

Manipuladores úteis incluídos no módulo logging.

Um livro de receitas do logging