feat(logger): add new logic to sample debug logs

aws_lambda_powertools/logging/logger.py

@@ -220,6 +220,10 @@ def __init__(
         serialize_stacktrace: bool = True,
         **kwargs,
     ) -> None:
+
+        # Used in case of sampling
+        self.initial_log_level = self._determine_log_level(level)
+
         self.service = resolve_env_var_choice(
             choice=service,
             env=os.getenv(constants.SERVICE_NAME_ENV, "service_undefined"),
@@ -339,6 +343,17 @@ def _init_logger(
         self._logger.init = True  # type: ignore[attr-defined]
         self._logger.powertools_handler = self.logger_handler  # type: ignore[attr-defined]
 
+    def refresh_sample_rate_calculation(self) -> None:
+        """
+        Refreshes the sample rate calculation by reconfiguring logging settings.
+
+        Returns
+        -------
+            None
+        """
+        self._logger.setLevel(self.initial_log_level)
+        self._configure_sampling()
+
     def _configure_sampling(self) -> None:
         """Dynamically set log level based on sampling rate
 
@@ -347,15 +362,19 @@ def _configure_sampling(self) -> None:
         InvalidLoggerSamplingRateError
             When sampling rate provided is not a float
         """
+        if not self.sampling_rate:
+            return
+
         try:
-            if self.sampling_rate and random.random() <= float(self.sampling_rate):
-                logger.debug("Setting log level to Debug due to sampling rate")
+            # This is not testing < 0 or > 1 conditions
+            # Because I don't need other if condition here
+            if random.random() <= float(self.sampling_rate):
                 self._logger.setLevel(logging.DEBUG)
         except ValueError:
             raise InvalidLoggerSamplingRateError(
                 (
                     f"Expected a float value ranging 0 to 1, but received {self.sampling_rate} instead."
-                    "Please review POWERTOOLS_LOGGER_SAMPLE_RATE environment variable."
+                    "Please review POWERTOOLS_LOGGER_SAMPLE_RATE environment variable or `sampling_rate` parameter."
                 ),
             )
 
@@ -465,7 +484,14 @@ def decorate(event, context, *args, **kwargs):
                 logger.debug("Event received")
                 self.info(extract_event_from_common_models(event))
 
-            return lambda_handler(event, context, *args, **kwargs)
+            lambda_execution_return = lambda_handler(event, context, *args, **kwargs)
+
+            # Sampling rate is defined, so, we need to recalculate the sampling
+            # See: https://github.com/aws-powertools/powertools-lambda-python/issues/6141
+            if self.sampling_rate:
+                self.refresh_sample_rate_calculation()
+
+            return lambda_execution_return
 
         return decorate
 

docs/core/logger.md

@@ -627,24 +627,27 @@ Notice in the CloudWatch Logs output how `payment_id` appears as expected when l
 
 ### Sampling debug logs
 
-Use sampling when you want to dynamically change your log level to **DEBUG** based on a **percentage of your concurrent/cold start invocations**.
+Use sampling when you want to dynamically change your log level to **DEBUG** based on a **percentage of the Lambda function invocations**.
 
-You can use values ranging from `0.0` to `1` (100%) when setting `POWERTOOLS_LOGGER_SAMPLE_RATE` env var, or `sample_rate` parameter in Logger.
+You can use values ranging from `0.0` to `1` (100%) when setting `POWERTOOLS_LOGGER_SAMPLE_RATE` env var, or `sampling_rate` parameter in Logger.
 
 ???+ tip "Tip: When is this useful?"
-    Let's imagine a sudden spike increase in concurrency triggered a transient issue downstream. When looking into the logs you might not have enough information, and while you can adjust log levels it might not happen again.
+    Log sampling allows you to capture debug information for a fraction of your requests, helping you diagnose rare or intermittent issues without increasing the overall verbosity of your logs.
 
-    This feature takes into account transient issues where additional debugging information can be useful.
+    Example: Imagine an e-commerce checkout process where you want to understand rare payment gateway errors. With 10% sampling, you'll log detailed information for a small subset of transactions, making troubleshooting easier without generating excessive logs.
 
-Sampling decision happens at the Logger initialization. This means sampling may happen significantly more or less than depending on your traffic patterns, for example a steady low number of invocations and thus few cold starts.
+The sampling decision happens automatically with each invocation when using `@logger.inject_lambda_context` decorator.  When not using the decorator, you're in charge of refreshing it via `refresh_sample_rate_calculation` method. Skipping both may lead to unexpected sampling results.
 
-???+ note
-	Open a [feature request](https://github.com/aws-powertools/powertools-lambda-python/issues/new?assignees=&labels=feature-request%2C+triage&template=feature_request.md&title=){target="_blank"} if you want Logger to calculate sampling for every invocation
+=== "sampling_debug_logs_with_decorator.py"
 
-=== "sampling_debug_logs.py"
+    ```python hl_lines="5 8"
+    --8<-- "examples/logger/src/sampling_debug_logs_with_decorator.py"
+    ```
 
-    ```python hl_lines="6 10"
-    --8<-- "examples/logger/src/sampling_debug_logs.py"
+=== "sampling_debug_logs_with_standalone_function.py"
+
+    ```python hl_lines="5 12"
+    --8<-- "examples/logger/src/sampling_debug_logs_with_standalone_function.py"
     ```
 
 === "sampling_debug_logs_output.json"

examples/logger/src/sampling_debug_logs.py → examples/logger/src/sampling_debug_logs_with_decorator.py

@@ -2,10 +2,10 @@
 from aws_lambda_powertools.utilities.typing import LambdaContext
 
 # Sample 10% of debug logs e.g. 0.1
-# NOTE: this evaluation will only occur at cold start
 logger = Logger(service="payment", sample_rate=0.1)
 
 
+@logger.inject_lambda_context
 def lambda_handler(event: dict, context: LambdaContext):
     logger.debug("Verifying whether order_id is present")
     logger.info("Collecting payment")

examples/logger/src/sampling_debug_logs_with_standalone_function.py

@@ -0,0 +1,14 @@
+from aws_lambda_powertools import Logger
+from aws_lambda_powertools.utilities.typing import LambdaContext
+
+# Sample 10% of debug logs e.g. 0.1
+logger = Logger(service="payment", sample_rate=0.1)
+
+
+def lambda_handler(event: dict, context: LambdaContext):
+    logger.debug("Verifying whether order_id is present")
+    logger.info("Collecting payment")
+
+    logger.refresh_sample_rate_calculation()
+
+    return "hello world"

tests/functional/logger/required_dependencies/test_logger.py

@@ -86,7 +86,7 @@ def test_setup_service_env_var(monkeypatch, stdout, service_name):
     assert service_name == log["service"]
 
 
-def test_setup_sampling_rate_env_var(monkeypatch, stdout, service_name):
+def test_setup_sampling_rate_env_var_with_100percent(monkeypatch, stdout, service_name):
     # GIVEN Logger is initialized
     # WHEN samping rate is explicitly set to 100% via POWERTOOLS_LOGGER_SAMPLE_RATE env
     sampling_rate = "1"
@@ -103,6 +103,129 @@ def test_setup_sampling_rate_env_var(monkeypatch, stdout, service_name):
     assert "I am being sampled" == log["message"]
 
 
+def test_setup_sampling_rate_constructor_with_100percent(stdout, service_name):
+    # GIVEN Logger is initialized
+    # WHEN samping rate is explicitly set to 100% via constructor
+    sampling_rate = 1
+    logger = Logger(service=service_name, sampling_rate=sampling_rate, stream=stdout)
+    logger.debug("I am being sampled")
+
+    # THEN sampling rate should be equals sampling_rate value
+    # log level should be DEBUG
+    # and debug log statements should be in stdout
+    log = capture_logging_output(stdout)
+    assert sampling_rate == log["sampling_rate"]
+    assert "DEBUG" == log["level"]
+    assert "I am being sampled" == log["message"]
+
+
+def test_setup_sampling_rate_env_var_with_0percent(monkeypatch, stdout, service_name):
+    # GIVEN Logger is initialized
+    # WHEN samping rate is explicitly set to 0% via POWERTOOLS_LOGGER_SAMPLE_RATE env
+    sampling_rate = "0"
+    monkeypatch.setenv("POWERTOOLS_LOGGER_SAMPLE_RATE", sampling_rate)
+    logger = Logger(service=service_name, stream=stdout)
+    logger.debug("I am being sampled")
+
+    # THEN we should not log
+    logs = list(stdout.getvalue().strip())
+    assert not logs
+
+
+def test_setup_sampling_rate_constructor_with_0percent(stdout, service_name):
+    # GIVEN Logger is initialized
+    # WHEN samping rate is explicitly set to 100% via constructor
+    sampling_rate = 0
+    logger = Logger(service=service_name, sampling_rate=sampling_rate, stream=stdout)
+    logger.debug("I am being sampled")
+
+    # THEN we should not log
+    logs = list(stdout.getvalue().strip())
+    assert not logs
+
+
+@pytest.mark.parametrize(
+    "percent, minimum_logs, maximum_logs",
+    [
+        (0.5, 35, 65),
+        (0.1, 0, 20),
+        (0.9, 75, 115),
+    ],
+)
+def test_setup_sampling_rate_env_var_with_percent_and_decorator(
+    lambda_context,
+    stdout,
+    service_name,
+    percent,
+    minimum_logs,
+    maximum_logs,
+):
+    # GIVEN the Logger is initialized with a specific sampling rate
+    sampling_rate = percent
+    total_runs = 100
+    minimum_logs_excepted = minimum_logs
+    maximum_logs_excepted = maximum_logs
+    logger = Logger(service=service_name, level="INFO", sampling_rate=sampling_rate, stream=stdout)
+
+    @logger.inject_lambda_context
+    def handler(event, context):
+        logger.debug("test")
+
+    # WHEN A lambda handler is invoked multiple times with decorator
+    for _i in range(total_runs):
+        handler({}, lambda_context)
+
+    # THEN verify the number of logs falls within the expected range
+    logs = list(stdout.getvalue().strip().split("\n"))
+    assert (
+        len(logs) >= minimum_logs_excepted
+    ), f"Log count {len(logs)} should be at least {minimum_logs_excepted} for sampling rate {sampling_rate}"
+    assert (
+        len(logs) <= maximum_logs_excepted
+    ), f"Log count {len(logs)} should be at most {maximum_logs_excepted} for sampling rate {sampling_rate}"
+
+
+@pytest.mark.parametrize(
+    "percent, minimum_logs, maximum_logs",
+    [
+        (0.5, 35, 65),
+        (0.1, 0, 20),
+        (0.9, 75, 115),
+    ],
+)
+def test_setup_sampling_rate_env_var_with_percent_and_recalculate_manual_method(
+    lambda_context,
+    stdout,
+    service_name,
+    percent,
+    minimum_logs,
+    maximum_logs,
+):
+    # GIVEN the Logger is initialized with a specific sampling rate
+    sampling_rate = percent
+    total_runs = 100
+    minimum_logs_excepted = minimum_logs
+    maximum_logs_excepted = maximum_logs
+    logger = Logger(service=service_name, level="INFO", sampling_rate=sampling_rate, stream=stdout)
+
+    def handler(event, context):
+        logger.debug("test")
+        logger.refresh_sample_rate_calculation()
+
+    # WHEN A lambda handler is invoked multiple times with manual refresh_sample_rate_calculation()
+    for _i in range(total_runs):
+        handler({}, lambda_context)
+
+    # THEN verify the number of logs falls within the expected range
+    logs = list(stdout.getvalue().strip().split("\n"))
+    assert (
+        len(logs) >= minimum_logs_excepted
+    ), f"Log count {len(logs)} should be at least {minimum_logs_excepted} for sampling rate {sampling_rate}"
+    assert (
+        len(logs) <= maximum_logs_excepted
+    ), f"Log count {len(logs)} should be at most {maximum_logs_excepted} for sampling rate {sampling_rate}"
+
+
 def test_inject_lambda_context(lambda_context, stdout, service_name):
     # GIVEN Logger is initialized
     logger = Logger(service=service_name, stream=stdout)