ERROR and one. Never done this with Python, but I don't see why it wouldn't work. As long as you flush your streams after writes, they should not interfere with one another, unless you are running co-routines, in which case you would need to program in some locks and such. As long as your program runs linearly you shouldn't have any worries. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow.
Learn more. Asked 8 years, 11 months ago. Active 5 years, 7 months ago. Viewed 3k times. What I want to achieve is the following: Anything equal or worse than WARNING goes to wherever the main program outside of my control has configured the logging to go usually stderr Everything else debug messages mainly goes to a log file Please note that the key issue here is that I do not want to modify the global logging settings, since those are controlled by the main app.
DEBUG logger. If I replace the above line with: logger. StreamHandler console. Improve this question. Add a comment. Active Oldest Votes. I simplified my source code whose original version is OOP and uses a configuration file , to give you an alternative solution to EliasStrehle's one, without using the dictConfig thus easiest to integrate with existing source code :.
Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. Making Python loggers output all messages to stdout in addition to log file Ask Question. Asked 9 years ago. Active 3 months ago. Viewed k times. This is to avoid duplicating messages like: mylogger.
Improve this question. Ben 1, 16 16 silver badges 33 33 bronze badges. Please check this answer stackoverflow. Add a comment. Active Oldest Votes. StreamHandler sys. Improve this answer. That's fine but if it's already redirected to a file how can I have it be printed to stdout in addition? New handlers do not replace the existing handlers, they also get to process the log entries. PrakharMohanSrivastava I'd guess you can just add it to the string passed into logging. The logger will handle messages of that level and higher, and the handler will handle messages of that level and higher.
It lets you differentiate between different loggers and different handlers. Show 7 more comments. The simplest way to log to stdout: import logging import sys logging. Stevoisiak Eyal Eyal 6, 1 1 gold badge 12 12 silver badges 7 7 bronze badges.
Hm, but this isn't logged to a file, right? The question was how to do logging to file and to console. Ref link: Python3 Docs: Logging. This final processing often translates into storing the log record, e. It can also translate it to communicate the log record data to specific entities e. Like loggers, handlers have a threshold logging level, which can be set via the Handler.
They also support filters via Handler. The handlers use their threshold logging level and filters to filter log records for processing. This additional filtering allows context-specific control over logging, e. While processing the log records, handlers format log records into log entries using their formatters. Clients can set the formatter for a handler via Handler.
If a handler does not have a formatter, then it uses the default formatter provided by the library. So, instantiating and configuring these handlers suffices in many situations. In situations that warrant custom handlers, developers can extend the Handler class or one of the pre-defined Handler classes by implementing the Handler.
The handlers use logging. Formatter objects to format a log record into a string-based log entry. So, beyond simple use cases, clients need to create new formatters by creating logging. Formatter objects with the necessary format strings.
The format string of a formatter can refer to any field of LogRecord objects, including the fields based on the keys of the extra argument of the logging method.
Before formatting a log record, the formatter uses the LogRecord. The formatter then combines the resulting log message with the data in the log record using the specified format string to create the log entry. To maintain a hierarchy of loggers, when a client uses the logging library, the library creates a root logger that serves as the root of the hierarchy of loggers.
The default threshold logging level of the root logger is logging. The module offers all of the logging methods offered by the Logger class as module-level functions with identical names and signature, e. Clients can use these functions to issue log requests without creating loggers, and the root logger services these requests. If the root logger has no handlers when servicing log requests issued via these methods, then the logging library adds a logging.
StreamHandler instance based on the sys. When loggers without handlers receive log requests, the logging library directs such log requests to the last resort handler , which is a logging. StreamHandler instance based on sys. This handler is accessible via the logging. By changing the handler created in step 3, we can redirect the log entries to different locations or processors. If a write fails, then the snippet logs an error message that includes the stack trace in which the exception occurred.
With the logger created using snippet 1, if the execution of app. If the execution of app. Instead of using positional arguments in the format string in the logging method, we could achieve the same output by using the arguments via their names as follows:. LoggerAdapter class provides a mechanism to inject contextual information into log records. We discuss other mechanisms to inject contextual information in the Good Practices and Gotchas section.
This snippet modifies Snippet 3 by wrapping the logger in a LoggerAdapter object to inject version information. All of the above changes affect the logging behavior of the app described in Snippet 2 and Snippet 3 as follows. What do you suppose would have happened if the filter was added to the logger instead of the handler? See Gotchas for the answer. The logging classes introduced in the previous section provide methods to configure their instances and, consequently, customize the use of the logging library.
Snippet 1 demonstrates how to use configuration methods. These methods are best used in simple single-file programs. When involved programs e.
Such externalization allows users to customize certain facets of logging in a program e. We refer to this approach to configuration as data-based approach. Clients can configure the logging library by invoking logging. The config argument is a dictionary and the following optional keys can be used to specify a configuration.
The strings serve as filter ids used to refer to filters in the configuration e. The string value of the name key in filter configurations is used to construct logging. Filter instances. The strings serve as formatter ids used to refer to formatters in the configuration e. The string values of the datefmt and format keys in formatter configurations are used as the date and log entry formatting strings, respectively, to construct logging.
Formatter instances. The boolean value of the optional validate key controls the validation of the format strings during the construction of a formatter.
This configuration snippet results in the creation of two formatters. A simple formatter with the specified log entry and date formatting strings and detailed formatter with specified log entry formatting string and default date formatting string. The strings serve as handler ids used to refer to handlers in the configuration e. The string value of the class key in a handler configuration names the class to instantiate to construct a handler.
The string value of the optional level key specifies the logging level of the instantiated handler. The string value of the optional formatter key specifies the id of the formatter of the handler. Likewise, the list of values of the optional filters key specifies the ids of the filters of the handler. The string value of the optional level key specifies the logging level of the logger. The boolean value of the optional propagate key specifies the propagation setting of the logger.
The list of values of the optional filters key specifies the ids of the filters of the logger. Likewise, the list of values of the optional handlers key specifies the ids of the handlers of the logger. This configuration snippet results in the creation of two loggers.
The first logger is named app , its threshold logging level is set to WARNING, and it is configured to forward log requests to stderr and alert handlers. The second logger is named app. Since a log request is propagated to the handlers associated with every ascendant logger, every log request with INFO or a higher logging level made via the app.
The format of the mapped dictionary is the same as the mapped dictionary for a logger. If True, then only logging levels and propagate options of loggers, handlers, and root loggers are processed, and all other bits of the configuration is ignored. This key is useful to alter existing logging configuration.
Refer to Incremental Configuration for more details. If False , then all existing non-root loggers are disabled as a result of processing this configuration.
The configuration schema for filters supports a pattern to specify a factory function to create a filter. In this pattern, a filter configuration maps the key to the fully qualified name of a filter creating factory function along with a set of keys and values to be passed as keyword arguments to the factory function. In addition, attributes and values can be added to custom filters by mapping the.
For example, the below configuration will cause the invocation of app. Configuration schemas for formatters, handlers, and loggers also support the above pattern. Refer to User-defined Objects for more details. While the above APIs can be used to update the logging configuration when the client is running e.
This function starts a socket server that accepts new configurations over the wire and loads them via dictConfig or fileConfig functions. Refer to logging. Since the configuration provided to dictConfig is nothing but a collection of nested dictionaries, a logging configuration can be easily represented in JSON and YAML format.
For example, the following snippet suffices to load the logging configuration stored in JSON format. In the supported configuration scheme, we cannot configure filters to filter beyond simple name-based filtering. For example, we cannot create a filter that admits only log requests created between 6 PM and 6 AM.
We need to program such filters in Python and add them to loggers and handlers via factory functions or the addFilter method.
0コメント