Module aws_lambda_powertools.logging

Logging utility

"""Logging utility
"""
from .logger import Logger

__all__ = ["Logger"]

Sub-modules

aws_lambda_powertools.logging.correlation_paths

Built-in correlation paths

aws_lambda_powertools.logging.exceptions

aws_lambda_powertools.logging.filters

aws_lambda_powertools.logging.formatter

aws_lambda_powertools.logging.lambda_context

aws_lambda_powertools.logging.logger

Classes

class Logger (service: str = None, level: Union[str, int] = None, child: bool = False, sampling_rate: float = None, stream: <_io.TextIOWrapper name='' mode='w' encoding='utf-8'> = None, **kwargs)

Creates and setups a logger to format statements in JSON.

Includes service name and any additional key=value into logs It also accepts both service name or level explicitly via env vars

Environment Variables

POWERTOOLS_SERVICE_NAME : str service name LOG_LEVEL: str logging level (e.g. INFO, DEBUG) POWERTOOLS_LOGGER_SAMPLE_RATE: float sampling rate ranging from 0 to 1, 1 being 100% sampling

Parameters

service : str, optional

service name to be appended in logs, by default "service_undefined"

level : str, int optional

logging.level, by default "INFO"

child : bool, optional

create a child Logger named ., False by default

sample_rate : float, optional

sample rate for debug calls within execution context defaults to 0.0

stream : sys.stdout, optional

valid output for a logging stream, by default sys.stdout

Example

Setups structured logging in JSON for Lambda functions with explicit service name

>>> from aws_lambda_powertools import Logger
>>> logger = Logger(service="payment")
>>>
>>> def handler(event, context):
        logger.info("Hello")

Setups structured logging in JSON for Lambda functions using env vars

$ export POWERTOOLS_SERVICE_NAME="payment"
$ export POWERTOOLS_LOGGER_SAMPLE_RATE=0.01 # 1% debug sampling
>>> from aws_lambda_powertools import Logger
>>> logger = Logger()
>>>
>>> def handler(event, context):
        logger.info("Hello")

Append payment_id to previously setup logger

>>> from aws_lambda_powertools import Logger
>>> logger = Logger(service="payment")
>>>
>>> def handler(event, context):
        logger.structure_logs(append=True, payment_id=event["payment_id"])
        logger.info("Hello")

Create child Logger using logging inheritance via child param

>>> # app.py
>>> import another_file
>>> from aws_lambda_powertools import Logger
>>> logger = Logger(service="payment")
>>>
>>> # another_file.py
>>> from aws_lambda_powertools import Logger
>>> logger = Logger(service="payment", child=True)

Raises

InvalidLoggerSamplingRateError

When sampling rate provided is not a float

Initialize the logger with a name and an optional level.

Expand source code

class Logger(logging.Logger):  # lgtm [py/missing-call-to-init]
    """Creates and setups a logger to format statements in JSON.

    Includes service name and any additional key=value into logs
    It also accepts both service name or level explicitly via env vars

    Environment variables
    ---------------------
    POWERTOOLS_SERVICE_NAME : str
        service name
    LOG_LEVEL: str
        logging level (e.g. INFO, DEBUG)
    POWERTOOLS_LOGGER_SAMPLE_RATE: float
        sampling rate ranging from 0 to 1, 1 being 100% sampling

    Parameters
    ----------
    service : str, optional
        service name to be appended in logs, by default "service_undefined"
    level : str, int optional
        logging.level, by default "INFO"
    child: bool, optional
        create a child Logger named <service>.<caller_file_name>, False by default
    sample_rate: float, optional
        sample rate for debug calls within execution context defaults to 0.0
    stream: sys.stdout, optional
        valid output for a logging stream, by default sys.stdout

    Example
    -------
    **Setups structured logging in JSON for Lambda functions with explicit service name**

        >>> from aws_lambda_powertools import Logger
        >>> logger = Logger(service="payment")
        >>>
        >>> def handler(event, context):
                logger.info("Hello")

    **Setups structured logging in JSON for Lambda functions using env vars**

        $ export POWERTOOLS_SERVICE_NAME="payment"
        $ export POWERTOOLS_LOGGER_SAMPLE_RATE=0.01 # 1% debug sampling
        >>> from aws_lambda_powertools import Logger
        >>> logger = Logger()
        >>>
        >>> def handler(event, context):
                logger.info("Hello")

    **Append payment_id to previously setup logger**

        >>> from aws_lambda_powertools import Logger
        >>> logger = Logger(service="payment")
        >>>
        >>> def handler(event, context):
                logger.structure_logs(append=True, payment_id=event["payment_id"])
                logger.info("Hello")

    **Create child Logger using logging inheritance via child param**

        >>> # app.py
        >>> import another_file
        >>> from aws_lambda_powertools import Logger
        >>> logger = Logger(service="payment")
        >>>
        >>> # another_file.py
        >>> from aws_lambda_powertools import Logger
        >>> logger = Logger(service="payment", child=True)

    Raises
    ------
    InvalidLoggerSamplingRateError
        When sampling rate provided is not a float
    """

    def __init__(
        self,
        service: str = None,
        level: Union[str, int] = None,
        child: bool = False,
        sampling_rate: float = None,
        stream: sys.stdout = None,
        **kwargs,
    ):
        self.service = resolve_env_var_choice(
            choice=service, env=os.getenv(constants.SERVICE_NAME_ENV, "service_undefined")
        )
        self.sampling_rate = resolve_env_var_choice(
            choice=sampling_rate, env=os.getenv(constants.LOGGER_LOG_SAMPLING_RATE, 0.0)
        )
        self._is_deduplication_disabled = resolve_truthy_env_var_choice(
            env=os.getenv(constants.LOGGER_LOG_DEDUPLICATION_ENV, "false")
        )
        self.log_level = self._get_log_level(level)
        self.child = child
        self._handler = logging.StreamHandler(stream) if stream is not None else logging.StreamHandler(sys.stdout)
        self._default_log_keys = {"service": self.service, "sampling_rate": self.sampling_rate}
        self._logger = self._get_logger()
        self._init_logger(**kwargs)

    def __getattr__(self, name):
        # Proxy attributes not found to actual logger to support backward compatibility
        # https://github.com/awslabs/aws-lambda-powertools-python/issues/97
        return getattr(self._logger, name)

    def _get_logger(self):
        """ Returns a Logger named {self.service}, or {self.service.filename} for child loggers"""
        logger_name = self.service
        if self.child:
            logger_name = f"{self.service}.{self._get_caller_filename()}"

        return logging.getLogger(logger_name)

    def _init_logger(self, **kwargs):
        """Configures new logger"""

        # Skip configuration if it's a child logger or a pre-configured logger
        # to prevent the following:
        #   a) multiple handlers being attached
        #   b) different sampling mechanisms
        #   c) multiple messages from being logged as handlers can be duplicated
        is_logger_preconfigured = getattr(self._logger, "init", False)
        if self.child or is_logger_preconfigured:
            return

        self._configure_sampling()
        self._logger.setLevel(self.log_level)
        self._logger.addHandler(self._handler)
        self.structure_logs(**kwargs)

        # Pytest Live Log feature duplicates log records for colored output
        # but we explicitly add a filter for log deduplication.
        # This flag disables this protection when you explicit want logs to be duplicated (#262)
        if not self._is_deduplication_disabled:
            logger.debug("Adding filter in root logger to suppress child logger records to bubble up")
            for handler in logging.root.handlers:
                # It'll add a filter to suppress any child logger from self.service
                # Example: `Logger(service="order")`, where service is Order
                # It'll reject all loggers starting with `order` e.g. order.checkout, order.shared
                handler.addFilter(SuppressFilter(self.service))

        # as per bug in #249, we should not be pre-configuring an existing logger
        # therefore we set a custom attribute in the Logger that will be returned
        # std logging will return the same Logger with our attribute if name is reused
        logger.debug(f"Marking logger {self.service} as preconfigured")
        self._logger.init = True

    def _configure_sampling(self):
        """Dynamically set log level based on sampling rate

        Raises
        ------
        InvalidLoggerSamplingRateError
            When sampling rate provided is not a float
        """
        try:
            if self.sampling_rate and random.random() <= float(self.sampling_rate):
                logger.debug("Setting log level to Debug due to sampling rate")
                self.log_level = logging.DEBUG
        except ValueError:
            raise InvalidLoggerSamplingRateError(
                f"Expected a float value ranging 0 to 1, but received {self.sampling_rate} instead."
                f"Please review POWERTOOLS_LOGGER_SAMPLE_RATE environment variable."
            )

    def inject_lambda_context(
        self, lambda_handler: Callable[[Dict, Any], Any] = None, log_event: bool = None, correlation_id_path: str = None
    ):
        """Decorator to capture Lambda contextual info and inject into logger

        Parameters
        ----------
        lambda_handler : Callable
            Method to inject the lambda context
        log_event : bool, optional
            Instructs logger to log Lambda Event, by default False
        correlation_id_path: str, optional
            Optional JMESPath for the correlation_id

        Environment variables
        ---------------------
        POWERTOOLS_LOGGER_LOG_EVENT : str
            instruct logger to log Lambda Event (e.g. `"true", "True", "TRUE"`)

        Example
        -------
        **Captures Lambda contextual runtime info (e.g memory, arn, req_id)**

            from aws_lambda_powertools import Logger

            logger = Logger(service="payment")

            @logger.inject_lambda_context
            def handler(event, context):
                logger.info("Hello")

        **Captures Lambda contextual runtime info and logs incoming request**

            from aws_lambda_powertools import Logger

            logger = Logger(service="payment")

            @logger.inject_lambda_context(log_event=True)
            def handler(event, context):
                logger.info("Hello")

        Returns
        -------
        decorate : Callable
            Decorated lambda handler
        """

        # If handler is None we've been called with parameters
        # Return a partial function with args filled
        if lambda_handler is None:
            logger.debug("Decorator called with parameters")
            return functools.partial(
                self.inject_lambda_context, log_event=log_event, correlation_id_path=correlation_id_path
            )

        log_event = resolve_truthy_env_var_choice(
            choice=log_event, env=os.getenv(constants.LOGGER_LOG_EVENT_ENV, "false")
        )

        @functools.wraps(lambda_handler)
        def decorate(event, context):
            lambda_context = build_lambda_context_model(context)
            cold_start = _is_cold_start()
            self.structure_logs(append=True, cold_start=cold_start, **lambda_context.__dict__)

            if correlation_id_path:
                self.set_correlation_id(jmespath.search(correlation_id_path, event))

            if log_event:
                logger.debug("Event received")
                self.info(event)

            return lambda_handler(event, context)

        return decorate

    def structure_logs(self, append: bool = False, **kwargs):
        """Sets logging formatting to JSON.

        Optionally, it can append keyword arguments
        to an existing logger so it is available
        across future log statements.

        Last keyword argument and value wins if duplicated.

        Parameters
        ----------
        append : bool, optional
            [description], by default False
        """

        # Child loggers don't have handlers attached, use its parent handlers
        handlers = self._logger.parent.handlers if self.child else self._logger.handlers
        for handler in handlers:
            if append:
                # Update existing formatter in an existing logger handler
                handler.formatter.update_formatter(**kwargs)
            else:
                # Set a new formatter for a logger handler
                handler.setFormatter(JsonFormatter(**self._default_log_keys, **kwargs))

    def set_correlation_id(self, value: str):
        """Sets the correlation_id in the logging json

        Parameters
        ----------
        value : str
            Value for the correlation id
        """
        self.structure_logs(append=True, correlation_id=value)

    @staticmethod
    def _get_log_level(level: Union[str, int, None]) -> Union[str, int]:
        """ Returns preferred log level set by the customer in upper case """
        if isinstance(level, int):
            return level

        log_level: Optional[str] = level or os.getenv("LOG_LEVEL")
        if log_level is None:
            return logging.INFO

        return log_level.upper()

    @staticmethod
    def _get_caller_filename():
        """ Return caller filename by finding the caller frame """
        # Current frame         => _get_logger()
        # Previous frame        => logger.py
        # Before previous frame => Caller
        frame = inspect.currentframe()
        caller_frame = frame.f_back.f_back.f_back
        return caller_frame.f_globals["__name__"]

Ancestors

• logging.Logger
• logging.Filterer

Methods

def inject_lambda_context(self, lambda_handler: Callable[[Dict, Any], Any] = None, log_event: bool = None, correlation_id_path: str = None)

Decorator to capture Lambda contextual info and inject into logger

Parameters

lambda_handler : Callable

Method to inject the lambda context

log_event : bool, optional

Instructs logger to log Lambda Event, by default False

correlation_id_path : str, optional

Optional JMESPath for the correlation_id

Environment Variables

POWERTOOLS_LOGGER_LOG_EVENT : str instruct logger to log Lambda Event (e.g. "true", "True", "TRUE")

Example

Captures Lambda contextual runtime info (e.g memory, arn, req_id)

from aws_lambda_powertools import Logger

logger = Logger(service="payment")

@logger.inject_lambda_context
def handler(event, context):
    logger.info("Hello")

Captures Lambda contextual runtime info and logs incoming request

from aws_lambda_powertools import Logger

logger = Logger(service="payment")

@logger.inject_lambda_context(log_event=True)
def handler(event, context):
    logger.info("Hello")

Returns

decorate : Callable

Decorated lambda handler

Expand source code

def inject_lambda_context(
    self, lambda_handler: Callable[[Dict, Any], Any] = None, log_event: bool = None, correlation_id_path: str = None
):
    """Decorator to capture Lambda contextual info and inject into logger

    Parameters
    ----------
    lambda_handler : Callable
        Method to inject the lambda context
    log_event : bool, optional
        Instructs logger to log Lambda Event, by default False
    correlation_id_path: str, optional
        Optional JMESPath for the correlation_id

    Environment variables
    ---------------------
    POWERTOOLS_LOGGER_LOG_EVENT : str
        instruct logger to log Lambda Event (e.g. `"true", "True", "TRUE"`)

    Example
    -------
    **Captures Lambda contextual runtime info (e.g memory, arn, req_id)**

        from aws_lambda_powertools import Logger

        logger = Logger(service="payment")

        @logger.inject_lambda_context
        def handler(event, context):
            logger.info("Hello")

    **Captures Lambda contextual runtime info and logs incoming request**

        from aws_lambda_powertools import Logger

        logger = Logger(service="payment")

        @logger.inject_lambda_context(log_event=True)
        def handler(event, context):
            logger.info("Hello")

    Returns
    -------
    decorate : Callable
        Decorated lambda handler
    """

    # If handler is None we've been called with parameters
    # Return a partial function with args filled
    if lambda_handler is None:
        logger.debug("Decorator called with parameters")
        return functools.partial(
            self.inject_lambda_context, log_event=log_event, correlation_id_path=correlation_id_path
        )

    log_event = resolve_truthy_env_var_choice(
        choice=log_event, env=os.getenv(constants.LOGGER_LOG_EVENT_ENV, "false")
    )

    @functools.wraps(lambda_handler)
    def decorate(event, context):
        lambda_context = build_lambda_context_model(context)
        cold_start = _is_cold_start()
        self.structure_logs(append=True, cold_start=cold_start, **lambda_context.__dict__)

        if correlation_id_path:
            self.set_correlation_id(jmespath.search(correlation_id_path, event))

        if log_event:
            logger.debug("Event received")
            self.info(event)

        return lambda_handler(event, context)

    return decorate

def set_correlation_id(self, value: str)

Sets the correlation_id in the logging json

Parameters

value : str

Value for the correlation id

Expand source code

def set_correlation_id(self, value: str):
    """Sets the correlation_id in the logging json

    Parameters
    ----------
    value : str
        Value for the correlation id
    """
    self.structure_logs(append=True, correlation_id=value)

def structure_logs(self, append: bool = False, **kwargs)

Sets logging formatting to JSON.

Optionally, it can append keyword arguments to an existing logger so it is available across future log statements.

Last keyword argument and value wins if duplicated.

Parameters

append : bool, optional

[description], by default False

Expand source code

def structure_logs(self, append: bool = False, **kwargs):
    """Sets logging formatting to JSON.

    Optionally, it can append keyword arguments
    to an existing logger so it is available
    across future log statements.

    Last keyword argument and value wins if duplicated.

    Parameters
    ----------
    append : bool, optional
        [description], by default False
    """

    # Child loggers don't have handlers attached, use its parent handlers
    handlers = self._logger.parent.handlers if self.child else self._logger.handlers
    for handler in handlers:
        if append:
            # Update existing formatter in an existing logger handler
            handler.formatter.update_formatter(**kwargs)
        else:
            # Set a new formatter for a logger handler
            handler.setFormatter(JsonFormatter(**self._default_log_keys, **kwargs))