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import functools
import logging
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Any
from aws_lambda_powertools.metrics.provider import cold_start
if TYPE_CHECKING:
from aws_lambda_powertools.shared.types import AnyCallableT
from aws_lambda_powertools.utilities.typing import LambdaContext
logger = logging.getLogger(__name__)
class BaseProvider(ABC):
"""
Interface to create a metrics provider.
BaseProvider implements `log_metrics` decorator for every provider as a value add feature.
Usage:
1. Inherit from this class.
2. Implement the required methods specific to your metric provider.
3. Customize the behavior and functionality of the metric provider in your subclass.
"""
@abstractmethod
def add_metric(self, *args: Any, **kwargs: Any) -> Any:
"""
Abstract method for adding a metric.
This method must be implemented in subclasses to add a metric and return a combined metrics dictionary.
Parameters
----------
*args:
Positional arguments.
*kwargs:
Keyword arguments.
Returns
----------
dict
A combined metrics dictionary.
Raises
----------
NotImplementedError
This method must be implemented in subclasses.
"""
raise NotImplementedError
@abstractmethod
def serialize_metric_set(self, *args: Any, **kwargs: Any) -> Any:
"""
Abstract method for serialize a metric.
This method must be implemented in subclasses to add a metric and return a combined metrics dictionary.
Parameters
----------
*args:
Positional arguments.
*kwargs:
Keyword arguments.
Returns
----------
dict
Serialized metrics
Raises
----------
NotImplementedError
This method must be implemented in subclasses.
"""
raise NotImplementedError
@abstractmethod
def flush_metrics(self, *args: Any, **kwargs) -> Any:
"""
Abstract method for flushing a metric.
This method must be implemented in subclasses to add a metric and return a combined metrics dictionary.
Parameters
----------
*args:
Positional arguments.
*kwargs:
Keyword arguments.
Raises
----------
NotImplementedError
This method must be implemented in subclasses.
"""
raise NotImplementedError
@abstractmethod
def clear_metrics(self, *args: Any, **kwargs) -> None:
"""
Abstract method for clear metric instance.
This method must be implemented in subclasses to clear the metric instance
Parameters
----------
*args:
Positional arguments.
*kwargs:
Keyword arguments.
Raises
----------
NotImplementedError
This method must be implemented in subclasses.
"""
raise NotImplementedError
@abstractmethod
def add_cold_start_metric(self, context: LambdaContext) -> Any:
"""
Abstract method for clear metric instance.
This method must be implemented in subclasses to add a metric and return a combined metrics dictionary.
Parameters
----------
*args:
Positional arguments.
*kwargs:
Keyword arguments.
Raises
----------
NotImplementedError
This method must be implemented in subclasses.
"""
raise NotImplementedError
def log_metrics(
self,
lambda_handler: AnyCallableT | None = None,
capture_cold_start_metric: bool = False,
raise_on_empty_metrics: bool = False,
**kwargs,
):
"""Decorator to serialize and publish metrics at the end of a function execution.
Be aware that the log_metrics **does call* the decorated function (e.g. lambda_handler).
Example
-------
**Lambda function using tracer and metrics decorators**
from aws_lambda_powertools import Metrics, Tracer
metrics = Metrics(service="payment")
tracer = Tracer(service="payment")
@tracer.capture_lambda_handler
@metrics.log_metrics
def handler(event, context):
...
Parameters
----------
lambda_handler : Callable[[Any, Any], Any], optional
lambda function handler, by default None
capture_cold_start_metric : bool, optional
captures cold start metric, by default False
raise_on_empty_metrics : bool, optional
raise exception if no metrics are emitted, by default False
default_dimensions: dict[str, str], optional
metric dimensions as key=value that will always be present
Raises
------
e
Propagate error received
"""
extra_args = {}
if kwargs.get("default_dimensions"):
extra_args.update({"default_dimensions": kwargs.get("default_dimensions")})
if kwargs.get("default_tags"):
extra_args.update({"default_tags": kwargs.get("default_tags")})
# 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.log_metrics,
capture_cold_start_metric=capture_cold_start_metric,
raise_on_empty_metrics=raise_on_empty_metrics,
**extra_args,
)
@functools.wraps(lambda_handler)
def decorate(event, context, *args, **kwargs):
try:
response = lambda_handler(event, context, *args, **kwargs)
if capture_cold_start_metric:
self._add_cold_start_metric(context=context)
finally:
self.flush_metrics(raise_on_empty_metrics=raise_on_empty_metrics)
return response
return decorate
def _add_cold_start_metric(self, context: Any) -> None:
"""
Add cold start metric
Parameters
----------
context : Any
Lambda context
"""
if not cold_start.is_cold_start:
return
logger.debug("Adding cold start metric and function_name dimension")
self.add_cold_start_metric(context=context)
cold_start.is_cold_start = False
def reset_cold_start_flag_provider():
if not cold_start.is_cold_start:
cold_start.is_cold_start = True
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