Logging
*******

Autor:
   Vinay Sajip <vinay_sajip at red-dove dot com>


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
-------------------

Logging provê um conjunto de funções convenientes para o uso simples
de logging. Estas funções são "debug()", "info()", "warning()",
"error()" and "critical()". Para determinar quando usar logging,
consulte a tabela abaixo, qual estado, para cada conjunto de tarefas
comuns, qual a melhor ferramenta para usar.

+---------------------------------------+----------------------------------------+
| Tarefa que você quer performar        | A melhor ferramenta para a tarefa      |
|=======================================|========================================|
| Exibir saída do console para uso      | "print()"                              |
| ordinário de um script de linha de    |                                        |
| comando ou programa.                  |                                        |
+---------------------------------------+----------------------------------------+
| Relata eventos que podem ocorrer      | "logging.info()" (ou "logging.debug()" |
| durante a operação normal de um       | para output bastante detalhado para    |
| programa (ex: para monitoramento do   | fins diagnósticos)                     |
| status ou investigação de falha)      |                                        |
+---------------------------------------+----------------------------------------+
| Emite um aviso sobre um evento de     | "warnings.warn()" na biblioteca de     |
| tempo de execução específico          | código se o problema é evitável e a    |
|                                       | aplicação cliente deve ser modificada  |
|                                       | para eliminar o alerta.                |
|                                       | "logging.warning()" se nada pode ser   |
|                                       | feito pela aplicação cliente sobre o   |
|                                       | ocorrido, mas mesmo assim o evento     |
|                                       | deve ser notificado                    |
+---------------------------------------+----------------------------------------+
| Relata um erro sobre um evento de     | Levantando uma exceção                 |
| tempo de execução específico          |                                        |
+---------------------------------------+----------------------------------------+
| Relatar supressão de um erro sem      | "logging.error()",                     |
| levantar uma exceção (ex: manipulador | "logging.exception()" ou               |
| de erros em um processo de servidor   | "logging.critical()" conforme          |
| em longa execução)                    | apropriado para o erro especifico e    |
|                                       | domínio da aplicação                   |
+---------------------------------------+----------------------------------------+

As funções logging são nomeadas por nível ou severidade dos eventos
que eles costumam rastrear. Os níveis padrões e suas aplicações são
descritas 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!')  # will print a message to the console
   logging.info('I told you so')  # will not print anything

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

   WARNING:root:Watch out!

exibido no console. A mensagem "INFO" não aparece porque o nível
padrão é "WARNING". A mensagem exibida inclui a indicação do nível e
uma descrição do evento informado na chamada ao logging, ex.:
"Cuidado!". Não se preocupe sobre entender tudo agora. Isto será
explicado mais tarde. A saída pode ser formatada de forma bastante
flexível se você precisar; opções de formatação serão também
explicadas posteriormente.


Logging em um arquivo
---------------------

Um situação bem comum é gravar os eventos de logging em um arquivo,
portanto vamos dar um olhada nisto na sequência. Tenha certeza de
tentar os seguintes comandos em um novo interpretador Python, e não
apenas continuar da sessão que foi descrita acima:

   import logging
   logging.basicConfig(filename='example.log', encoding='utf-8', level=logging.DEBUG)
   logging.debug('This message should go to the log file')
   logging.info('So should this')
   logging.warning('And this, too')
   logging.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:root:This message should go to the log file
   INFO:root:So should this
   WARNING:root:And this, too
   ERROR:root: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, ...)

A chamada a "basicConfig()" deve vir *antes* de qualquer chamada para
"debug()", "info()" etc. Como isto pretende ser um simples facilitador
de configuração, apenas a primeira chamada irá realmente fazer algo:
As próximas chamadas não serão efetivamente operacionais.

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 de múltiplos módulos
----------------------------

Se seu programa tem múltiplos módulos, aqui está um exemplo de como
você pode organizar o logging nele:

   # myapp.py
   import logging
   import mylib

   def main():
       logging.basicConfig(filename='myapp.log', level=logging.INFO)
       logging.info('Started')
       mylib.do_something()
       logging.info('Finished')

   if __name__ == '__main__':
       main()

   # mylib.py
   import logging

   def do_something():
       logging.info('Doing something')

Se você executar *myapp.py*, deverá ver isso em *myapp.log*:

   INFO:root:Started
   INFO:root:Doing something
   INFO:root:Finished

que é com sorte o que você espera ver. Você pode generalizar isto para
múltiplos módulos, usando o padrão da *mylib.py*. Note que para este
uso deste simples padrão, você não saberá, olhando no arquivo de log,
onde na sua aplicação suas mensagens vieram, independente de olhar a
descrição do evento. Se você quer rastrear a localização das suas
mensagens, você precisará consultar a documentação além do tutorial de
níveis -- veja Tutorial Avançado do Logging.


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.

Se sua necessidade de logging é simples, então use os exemplos acima
para incorporar o logging nos seus scripts, se você encontrar algum
problema ou não entender algo, por favor envie um pergunta no
comp.lang.python Usenet grupo (disponível no
https://groups.google.com/forum/#!forum/comp.lang.python) e você
deverá receber ajuda assim que possível.

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.

[imagem]


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 Funções
de configuração.  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
   datefmt=

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 Funções de
configuração.


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 behaviour of the logging package
in these circumstances is dependent on the Python version.

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

* If *logging.raiseExceptions* is "False" (production mode), the event
  is silently dropped.

* If *logging.raiseExceptions* is "True" (development mode), a message
  'No handlers could be found for logger X.Y.Z' is printed once.

No Python 3.2 e posteriores, o comportamento é como o seguinte:

* The event is output using a 'handler of last resort', stored in
  "logging.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.

To obtain the pre-3.2 behaviour, "logging.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 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.

Novo na versão 3.1: A classe "NullHandler".

Novo 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 "0".         |
+-------------------------------------------------+------------------------------------------+
| Processar informação.                           | Set "logging.logProcesses" to "0".       |
+-------------------------------------------------+------------------------------------------+

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.

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"
     Tratadores úteis incluídos no módulo logging.

  Um livro de receitas do logging
