| from __future__ import annotations |
|
|
| 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 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 |
|
|
