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Source code for dagster._core.definitions.logger_definition

from typing import TYPE_CHECKING, Any, Callable, Optional, Union, cast, overload

import dagster._check as check
from dagster._annotations import public
from dagster._core.decorator_utils import get_function_params
from dagster._core.definitions.config import is_callable_valid_config_arg
from dagster._core.definitions.configurable import AnonymousConfigurableDefinition
from dagster._core.definitions.definition_config_schema import (
    CoercableToConfigSchema,
    convert_user_facing_definition_config_schema,
)
from dagster._core.errors import DagsterInvalidInvocationError

if TYPE_CHECKING:
    import logging

    from dagster._core.definitions import JobDefinition
    from dagster._core.execution.context.logger import InitLoggerContext, UnboundInitLoggerContext

    InitLoggerFunction = Callable[[InitLoggerContext], logging.Logger]


[docs] class LoggerDefinition(AnonymousConfigurableDefinition): """Core class for defining loggers. Loggers are job-scoped logging handlers, which will be automatically invoked whenever dagster messages are logged from within a job. Args: logger_fn (Callable[[InitLoggerContext], logging.Logger]): User-provided function to instantiate the logger. This logger will be automatically invoked whenever the methods on ``context.log`` are called from within job compute logic. config_schema (Optional[ConfigSchema]): The schema for the config. Configuration data available in `init_context.logger_config`. If not set, Dagster will accept any config provided. description (Optional[str]): A human-readable description of this logger. """ def __init__( self, logger_fn: "InitLoggerFunction", config_schema: Any = None, description: Optional[str] = None, ): self._logger_fn = check.callable_param(logger_fn, "logger_fn") self._config_schema = convert_user_facing_definition_config_schema(config_schema) self._description = check.opt_str_param(description, "description") def __call__(self, *args, **kwargs): from dagster._core.definitions.logger_invocation import logger_invocation_result from dagster._core.execution.context.logger import UnboundInitLoggerContext if len(args) == 0 and len(kwargs) == 0: raise DagsterInvalidInvocationError( "Logger initialization function has context argument, but no context argument was " "provided when invoking." ) if len(args) + len(kwargs) > 1: raise DagsterInvalidInvocationError( "Initialization of logger received multiple arguments. Only a first " "positional context parameter should be provided when invoking." ) context_param_name = get_function_params(self.logger_fn)[0].name if args: context = check.opt_inst_param( args[0], context_param_name, UnboundInitLoggerContext, default=UnboundInitLoggerContext(logger_config=None, job_def=None), ) return logger_invocation_result(self, context) else: if context_param_name not in kwargs: raise DagsterInvalidInvocationError( f"Logger initialization expected argument '{context_param_name}'." ) context = check.opt_inst_param( kwargs[context_param_name], context_param_name, UnboundInitLoggerContext, default=UnboundInitLoggerContext(logger_config=None, job_def=None), ) return logger_invocation_result(self, context) @public @property def logger_fn(self) -> "InitLoggerFunction": """Callable[[InitLoggerContext], logging.Logger]: The function that will be invoked to instantiate the logger. """ return self._logger_fn @public @property def config_schema(self) -> Any: """Any: The schema for the logger's config. Configuration data available in `init_context.logger_config`.""" return self._config_schema @public @property def description(self) -> Optional[str]: """Optional[str]: A human-readable description of the logger.""" return self._description def copy_for_configured( self, description: Optional[str], config_schema: Any, ) -> "LoggerDefinition": return LoggerDefinition( config_schema=config_schema, description=description or self.description, logger_fn=self.logger_fn, )
@overload def logger( config_schema: CoercableToConfigSchema, description: Optional[str] = ... ) -> Callable[["InitLoggerFunction"], "LoggerDefinition"]: ... @overload def logger( config_schema: "InitLoggerFunction", description: Optional[str] = ... ) -> "LoggerDefinition": ...
[docs] def logger( config_schema: Union[CoercableToConfigSchema, "InitLoggerFunction"] = None, description: Optional[str] = None, ) -> Union["LoggerDefinition", Callable[["InitLoggerFunction"], "LoggerDefinition"]]: """Define a logger. The decorated function should accept an :py:class:`InitLoggerContext` and return an instance of :py:class:`python:logging.Logger`. This function will become the ``logger_fn`` of an underlying :py:class:`LoggerDefinition`. Args: config_schema (Optional[ConfigSchema]): The schema for the config. Configuration data available in `init_context.logger_config`. If not set, Dagster will accept any config provided. description (Optional[str]): A human-readable description of the logger. """ # This case is for when decorator is used bare, without arguments. # E.g. @logger versus @logger() if callable(config_schema) and not is_callable_valid_config_arg(config_schema): return LoggerDefinition(logger_fn=cast("InitLoggerFunction", config_schema)) def _wrap(logger_fn: "InitLoggerFunction") -> "LoggerDefinition": return LoggerDefinition( logger_fn=logger_fn, config_schema=config_schema, description=description, ) return _wrap
[docs] def build_init_logger_context( logger_config: Any = None, job_def: Optional["JobDefinition"] = None, ) -> "UnboundInitLoggerContext": """Builds logger initialization context from provided parameters. This function can be used to provide the context argument to the invocation of a logger definition. Note that you may only specify one of pipeline_def and job_def. Args: logger_config (Any): The config to provide during initialization of logger. job_def (Optional[JobDefinition]): The job definition that the logger will be used with. Examples: .. code-block:: python context = build_init_logger_context() logger_to_init(context) """ from dagster._core.definitions import JobDefinition from dagster._core.execution.context.logger import UnboundInitLoggerContext check.opt_inst_param(job_def, "job_def", JobDefinition) return UnboundInitLoggerContext(logger_config=logger_config, job_def=job_def)