Module aws_lambda_powertools.logging.logger
Expand source code
import functools
import inspect
import logging
import os
import random
import sys
from typing import IO, Any, Callable, Dict, Iterable, Optional, TypeVar, Union
import jmespath
from ..shared import constants
from ..shared.functions import resolve_env_var_choice, resolve_truthy_env_var_choice
from .exceptions import InvalidLoggerSamplingRateError
from .filters import SuppressFilter
from .formatter import BasePowertoolsFormatter, LambdaPowertoolsFormatter
from .lambda_context import build_lambda_context_model
logger = logging.getLogger(__name__)
is_cold_start = True
PowertoolsFormatter = TypeVar("PowertoolsFormatter", bound=BasePowertoolsFormatter)
def _is_cold_start() -> bool:
"""Verifies whether is cold start
Returns
-------
bool
cold start bool value
"""
cold_start = False
global is_cold_start
if is_cold_start:
cold_start = is_cold_start
is_cold_start = False
return cold_start
# PyCharm does not support autocomplete via getattr
# so we need to return to subclassing removed in #97
# All methods/properties continue to be proxied to inner logger
# https://github.com/awslabs/aws-lambda-powertools-python/issues/107
# noinspection PyRedeclaration
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
logger_formatter: PowertoolsFormatter, optional
custom logging formatter that implements PowertoolsFormatter
logger_handler: logging.Handler, optional
custom logging handler e.g. logging.FileHandler("file.log")
Parameters propagated to LambdaPowertoolsFormatter
--------------------------------------------------
datefmt: str, optional
String directives (strftime) to format log timestamp using `time`, by default it uses RFC
3339.
use_datetime_directive: str, optional
Interpret `datefmt` as a format string for `datetime.datetime.strftime`, rather than
`time.strftime`.
See https://docs.python.org/3/library/datetime.html#strftime-strptime-behavior . This
also supports a custom %F directive for milliseconds.
json_serializer : Callable, optional
function to serialize `obj` to a JSON formatted `str`, by default json.dumps
json_deserializer : Callable, optional
function to deserialize `str`, `bytes`, bytearray` containing a JSON document to a Python `obj`,
by default json.loads
json_default : Callable, optional
function to coerce unserializable values, by default `str()`
Only used when no custom formatter is set
utc : bool, optional
set logging timestamp to UTC, by default False to continue to use local time as per stdlib
log_record_order : list, optional
set order of log keys when logging, by default ["level", "location", "message", "timestamp"]
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.append_keys(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)
**Logging in UTC timezone**
>>> # app.py
>>> import logging
>>> from aws_lambda_powertools import Logger
>>>
>>> logger = Logger(service="payment", utc=True)
**Brings message as the first key in log statements**
>>> # app.py
>>> import logging
>>> from aws_lambda_powertools import Logger
>>>
>>> logger = Logger(service="payment", log_record_order=["message"])
**Logging to a file instead of standard output for testing**
>>> # app.py
>>> import logging
>>> from aws_lambda_powertools import Logger
>>>
>>> logger = Logger(service="payment", logger_handler=logging.FileHandler("log.json"))
Raises
------
InvalidLoggerSamplingRateError
When sampling rate provided is not a float
"""
def __init__(
self,
service: Optional[str] = None,
level: Union[str, int, None] = None,
child: bool = False,
sampling_rate: Optional[float] = None,
stream: Optional[IO[str]] = None,
logger_formatter: Optional[PowertoolsFormatter] = None,
logger_handler: Optional[logging.Handler] = 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)
)
self.child = child
self.logger_formatter = logger_formatter
self.logger_handler = logger_handler or logging.StreamHandler(stream)
self.log_level = self._get_log_level(level)
self._is_deduplication_disabled = resolve_truthy_env_var_choice(
env=os.getenv(constants.LOGGER_LOG_DEDUPLICATION_ENV, "false")
)
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.logger_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: Optional[Callable[[Dict, Any], Any]] = None,
log_event: Optional[bool] = None,
correlation_id_path: Optional[str] = None,
clear_state: Optional[bool] = False,
):
"""Decorator to capture Lambda contextual info and inject into logger
Parameters
----------
clear_state : bool, optional
Instructs logger to remove any custom keys previously added
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,
clear_state=clear_state,
)
log_event = resolve_truthy_env_var_choice(
env=os.getenv(constants.LOGGER_LOG_EVENT_ENV, "false"), choice=log_event
)
@functools.wraps(lambda_handler)
def decorate(event, context, **kwargs):
lambda_context = build_lambda_context_model(context)
cold_start = _is_cold_start()
if clear_state:
self.structure_logs(cold_start=cold_start, **lambda_context.__dict__)
else:
self.append_keys(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(getattr(event, "raw_event", event))
return lambda_handler(event, context)
return decorate
def append_keys(self, **additional_keys):
self.registered_formatter.append_keys(**additional_keys)
def remove_keys(self, keys: Iterable[str]):
self.registered_formatter.remove_keys(keys)
@property
def registered_handler(self) -> logging.Handler:
"""Convenience property to access logger handler"""
handlers = self._logger.parent.handlers if self.child else self._logger.handlers
return handlers[0]
@property
def registered_formatter(self) -> PowertoolsFormatter:
"""Convenience property to access logger formatter"""
return self.registered_handler.formatter # type: ignore
def structure_logs(self, append: bool = False, **keys):
"""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
append keys provided to logger formatter, by default False
"""
# There are 3 operational modes for this method
## 1. Register a Powertools Formatter for the first time
## 2. Append new keys to the current logger formatter; deprecated in favour of append_keys
## 3. Add new keys and discard existing to the registered formatter
# Mode 1
log_keys = {**self._default_log_keys, **keys}
is_logger_preconfigured = getattr(self._logger, "init", False)
if not is_logger_preconfigured:
formatter = self.logger_formatter or LambdaPowertoolsFormatter(**log_keys) # type: ignore
return self.registered_handler.setFormatter(formatter)
# Mode 2 (legacy)
if append:
# Maintenance: Add deprecation warning for major version
return self.append_keys(**keys)
# Mode 3
self.registered_formatter.clear_state()
self.registered_formatter.append_keys(**log_keys)
def set_correlation_id(self, value: Optional[str]):
"""Sets the correlation_id in the logging json
Parameters
----------
value : str, optional
Value for the correlation id. None will remove the correlation_id
"""
self.append_keys(correlation_id=value)
def get_correlation_id(self) -> Optional[str]:
"""Gets the correlation_id in the logging json
Returns
-------
str, optional
Value for the correlation id
"""
if isinstance(self.registered_formatter, LambdaPowertoolsFormatter):
return self.registered_formatter.log_format.get("correlation_id")
return None
@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__"]
def set_package_logger(
level: Union[str, int] = logging.DEBUG,
stream: Optional[IO[str]] = None,
formatter: Optional[logging.Formatter] = None,
):
"""Set an additional stream handler, formatter, and log level for aws_lambda_powertools package logger.
**Package log by default is suppressed (NullHandler), this should only used for debugging.
This is separate from application Logger class utility**
Example
-------
**Enables debug logging for AWS Lambda Powertools package**
>>> aws_lambda_powertools.logging.logger import set_package_logger
>>> set_package_logger()
Parameters
----------
level: str, int
log level, DEBUG by default
stream: sys.stdout
log stream, stdout by default
formatter: logging.Formatter
log formatter, "%(asctime)s %(name)s [%(levelname)s] %(message)s" by default
"""
if formatter is None:
formatter = logging.Formatter("%(asctime)s %(name)s [%(levelname)s] %(message)s")
if stream is None:
stream = sys.stdout
logger = logging.getLogger("aws_lambda_powertools")
logger.setLevel(level)
handler = logging.StreamHandler(stream)
handler.setFormatter(formatter)
logger.addHandler(handler)
Functions
def set_package_logger(level: Union[str, int] = 10, stream: Optional[IO[str]] = None, formatter: Optional[logging.Formatter] = None)
-
Set an additional stream handler, formatter, and log level for aws_lambda_powertools package logger.
Package log by default is suppressed (NullHandler), this should only used for debugging. This is separate from application Logger class utility
Example
Enables debug logging for AWS Lambda Powertools package
>>> aws_lambda_powertools.logging.logger import set_package_logger >>> set_package_logger()
Parameters
level
:str, int
- log level, DEBUG by default
stream
:sys.stdout
- log stream, stdout by default
formatter
:logging.Formatter
- log formatter, "%(asctime)s %(name)s [%(levelname)s] %(message)s" by default
Expand source code
def set_package_logger( level: Union[str, int] = logging.DEBUG, stream: Optional[IO[str]] = None, formatter: Optional[logging.Formatter] = None, ): """Set an additional stream handler, formatter, and log level for aws_lambda_powertools package logger. **Package log by default is suppressed (NullHandler), this should only used for debugging. This is separate from application Logger class utility** Example ------- **Enables debug logging for AWS Lambda Powertools package** >>> aws_lambda_powertools.logging.logger import set_package_logger >>> set_package_logger() Parameters ---------- level: str, int log level, DEBUG by default stream: sys.stdout log stream, stdout by default formatter: logging.Formatter log formatter, "%(asctime)s %(name)s [%(levelname)s] %(message)s" by default """ if formatter is None: formatter = logging.Formatter("%(asctime)s %(name)s [%(levelname)s] %(message)s") if stream is None: stream = sys.stdout logger = logging.getLogger("aws_lambda_powertools") logger.setLevel(level) handler = logging.StreamHandler(stream) handler.setFormatter(formatter) logger.addHandler(handler)
Classes
class Logger (service: Optional[str] = None, level: Union[str, int, None] = None, child: bool = False, sampling_rate: Optional[float] = None, stream: Optional[IO[str]] = None, logger_formatter: Optional[~PowertoolsFormatter] = None, logger_handler: Optional[logging.Handler] = 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
logger_formatter
:PowertoolsFormatter
, optional- custom logging formatter that implements PowertoolsFormatter
logger_handler
:logging.Handler
, optional- custom logging handler e.g. logging.FileHandler("file.log")
Parameters Propagated To Lambdapowertoolsformatter
datefmt: str, optional String directives (strftime) to format log timestamp using
time
, by default it uses RFC 3339. use_datetime_directive: str, optional Interpretdatefmt
as a format string fordatetime.datetime.strftime
, rather thantime.strftime
.See <https://docs.python.org/3/library/datetime.html#strftime-strptime-behavior> . This also supports a custom %F directive for milliseconds.
json_serializer : Callable, optional function to serialize
obj
to a JSON formattedstr
, by default json.dumps json_deserializer : Callable, optional function to deserializestr
,bytes
, bytearraycontaining a JSON document to a Python
obj`, by default json.loads json_default : Callable, optional function to coerce unserializable values, by defaultstr()
Only used when no custom formatter is set
utc : bool, optional set logging timestamp to UTC, by default False to continue to use local time as per stdlib log_record_order : list, optional set order of log keys when logging, by default ["level", "location", "message", "timestamp"]
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.append_keys(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)
Logging in UTC timezone
>>> # app.py >>> import logging >>> from aws_lambda_powertools import Logger >>> >>> logger = Logger(service="payment", utc=True)
Brings message as the first key in log statements
>>> # app.py >>> import logging >>> from aws_lambda_powertools import Logger >>> >>> logger = Logger(service="payment", log_record_order=["message"])
Logging to a file instead of standard output for testing
>>> # app.py >>> import logging >>> from aws_lambda_powertools import Logger >>> >>> logger = Logger(service="payment", logger_handler=logging.FileHandler("log.json"))
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 logger_formatter: PowertoolsFormatter, optional custom logging formatter that implements PowertoolsFormatter logger_handler: logging.Handler, optional custom logging handler e.g. logging.FileHandler("file.log") Parameters propagated to LambdaPowertoolsFormatter -------------------------------------------------- datefmt: str, optional String directives (strftime) to format log timestamp using `time`, by default it uses RFC 3339. use_datetime_directive: str, optional Interpret `datefmt` as a format string for `datetime.datetime.strftime`, rather than `time.strftime`. See https://docs.python.org/3/library/datetime.html#strftime-strptime-behavior . This also supports a custom %F directive for milliseconds. json_serializer : Callable, optional function to serialize `obj` to a JSON formatted `str`, by default json.dumps json_deserializer : Callable, optional function to deserialize `str`, `bytes`, bytearray` containing a JSON document to a Python `obj`, by default json.loads json_default : Callable, optional function to coerce unserializable values, by default `str()` Only used when no custom formatter is set utc : bool, optional set logging timestamp to UTC, by default False to continue to use local time as per stdlib log_record_order : list, optional set order of log keys when logging, by default ["level", "location", "message", "timestamp"] 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.append_keys(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) **Logging in UTC timezone** >>> # app.py >>> import logging >>> from aws_lambda_powertools import Logger >>> >>> logger = Logger(service="payment", utc=True) **Brings message as the first key in log statements** >>> # app.py >>> import logging >>> from aws_lambda_powertools import Logger >>> >>> logger = Logger(service="payment", log_record_order=["message"]) **Logging to a file instead of standard output for testing** >>> # app.py >>> import logging >>> from aws_lambda_powertools import Logger >>> >>> logger = Logger(service="payment", logger_handler=logging.FileHandler("log.json")) Raises ------ InvalidLoggerSamplingRateError When sampling rate provided is not a float """ def __init__( self, service: Optional[str] = None, level: Union[str, int, None] = None, child: bool = False, sampling_rate: Optional[float] = None, stream: Optional[IO[str]] = None, logger_formatter: Optional[PowertoolsFormatter] = None, logger_handler: Optional[logging.Handler] = 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) ) self.child = child self.logger_formatter = logger_formatter self.logger_handler = logger_handler or logging.StreamHandler(stream) self.log_level = self._get_log_level(level) self._is_deduplication_disabled = resolve_truthy_env_var_choice( env=os.getenv(constants.LOGGER_LOG_DEDUPLICATION_ENV, "false") ) 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.logger_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: Optional[Callable[[Dict, Any], Any]] = None, log_event: Optional[bool] = None, correlation_id_path: Optional[str] = None, clear_state: Optional[bool] = False, ): """Decorator to capture Lambda contextual info and inject into logger Parameters ---------- clear_state : bool, optional Instructs logger to remove any custom keys previously added 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, clear_state=clear_state, ) log_event = resolve_truthy_env_var_choice( env=os.getenv(constants.LOGGER_LOG_EVENT_ENV, "false"), choice=log_event ) @functools.wraps(lambda_handler) def decorate(event, context, **kwargs): lambda_context = build_lambda_context_model(context) cold_start = _is_cold_start() if clear_state: self.structure_logs(cold_start=cold_start, **lambda_context.__dict__) else: self.append_keys(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(getattr(event, "raw_event", event)) return lambda_handler(event, context) return decorate def append_keys(self, **additional_keys): self.registered_formatter.append_keys(**additional_keys) def remove_keys(self, keys: Iterable[str]): self.registered_formatter.remove_keys(keys) @property def registered_handler(self) -> logging.Handler: """Convenience property to access logger handler""" handlers = self._logger.parent.handlers if self.child else self._logger.handlers return handlers[0] @property def registered_formatter(self) -> PowertoolsFormatter: """Convenience property to access logger formatter""" return self.registered_handler.formatter # type: ignore def structure_logs(self, append: bool = False, **keys): """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 append keys provided to logger formatter, by default False """ # There are 3 operational modes for this method ## 1. Register a Powertools Formatter for the first time ## 2. Append new keys to the current logger formatter; deprecated in favour of append_keys ## 3. Add new keys and discard existing to the registered formatter # Mode 1 log_keys = {**self._default_log_keys, **keys} is_logger_preconfigured = getattr(self._logger, "init", False) if not is_logger_preconfigured: formatter = self.logger_formatter or LambdaPowertoolsFormatter(**log_keys) # type: ignore return self.registered_handler.setFormatter(formatter) # Mode 2 (legacy) if append: # Maintenance: Add deprecation warning for major version return self.append_keys(**keys) # Mode 3 self.registered_formatter.clear_state() self.registered_formatter.append_keys(**log_keys) def set_correlation_id(self, value: Optional[str]): """Sets the correlation_id in the logging json Parameters ---------- value : str, optional Value for the correlation id. None will remove the correlation_id """ self.append_keys(correlation_id=value) def get_correlation_id(self) -> Optional[str]: """Gets the correlation_id in the logging json Returns ------- str, optional Value for the correlation id """ if isinstance(self.registered_formatter, LambdaPowertoolsFormatter): return self.registered_formatter.log_format.get("correlation_id") return None @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
Instance variables
var registered_formatter : ~PowertoolsFormatter
-
Convenience property to access logger formatter
Expand source code
@property def registered_formatter(self) -> PowertoolsFormatter: """Convenience property to access logger formatter""" return self.registered_handler.formatter # type: ignore
var registered_handler : logging.Handler
-
Convenience property to access logger handler
Expand source code
@property def registered_handler(self) -> logging.Handler: """Convenience property to access logger handler""" handlers = self._logger.parent.handlers if self.child else self._logger.handlers return handlers[0]
Methods
def append_keys(self, **additional_keys)
-
Expand source code
def append_keys(self, **additional_keys): self.registered_formatter.append_keys(**additional_keys)
def get_correlation_id(self) ‑> Optional[str]
-
Gets the correlation_id in the logging json
Returns
str
, optional- Value for the correlation id
Expand source code
def get_correlation_id(self) -> Optional[str]: """Gets the correlation_id in the logging json Returns ------- str, optional Value for the correlation id """ if isinstance(self.registered_formatter, LambdaPowertoolsFormatter): return self.registered_formatter.log_format.get("correlation_id") return None
def inject_lambda_context(self, lambda_handler: Optional[Callable[[Dict[~KT, ~VT], Any], Any]] = None, log_event: Optional[bool] = None, correlation_id_path: Optional[str] = None, clear_state: Optional[bool] = False)
-
Decorator to capture Lambda contextual info and inject into logger
Parameters
clear_state
:bool
, optional- Instructs logger to remove any custom keys previously added
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: Optional[Callable[[Dict, Any], Any]] = None, log_event: Optional[bool] = None, correlation_id_path: Optional[str] = None, clear_state: Optional[bool] = False, ): """Decorator to capture Lambda contextual info and inject into logger Parameters ---------- clear_state : bool, optional Instructs logger to remove any custom keys previously added 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, clear_state=clear_state, ) log_event = resolve_truthy_env_var_choice( env=os.getenv(constants.LOGGER_LOG_EVENT_ENV, "false"), choice=log_event ) @functools.wraps(lambda_handler) def decorate(event, context, **kwargs): lambda_context = build_lambda_context_model(context) cold_start = _is_cold_start() if clear_state: self.structure_logs(cold_start=cold_start, **lambda_context.__dict__) else: self.append_keys(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(getattr(event, "raw_event", event)) return lambda_handler(event, context) return decorate
def remove_keys(self, keys: Iterable[str])
-
Expand source code
def remove_keys(self, keys: Iterable[str]): self.registered_formatter.remove_keys(keys)
def set_correlation_id(self, value: Optional[str])
-
Sets the correlation_id in the logging json
Parameters
value
:str
, optional- Value for the correlation id. None will remove the correlation_id
Expand source code
def set_correlation_id(self, value: Optional[str]): """Sets the correlation_id in the logging json Parameters ---------- value : str, optional Value for the correlation id. None will remove the correlation_id """ self.append_keys(correlation_id=value)
def structure_logs(self, append: bool = False, **keys)
-
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- append keys provided to logger formatter, by default False
Expand source code
def structure_logs(self, append: bool = False, **keys): """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 append keys provided to logger formatter, by default False """ # There are 3 operational modes for this method ## 1. Register a Powertools Formatter for the first time ## 2. Append new keys to the current logger formatter; deprecated in favour of append_keys ## 3. Add new keys and discard existing to the registered formatter # Mode 1 log_keys = {**self._default_log_keys, **keys} is_logger_preconfigured = getattr(self._logger, "init", False) if not is_logger_preconfigured: formatter = self.logger_formatter or LambdaPowertoolsFormatter(**log_keys) # type: ignore return self.registered_handler.setFormatter(formatter) # Mode 2 (legacy) if append: # Maintenance: Add deprecation warning for major version return self.append_keys(**keys) # Mode 3 self.registered_formatter.clear_state() self.registered_formatter.append_keys(**log_keys)