Addressing Andrea's feedback

Author: leandrodamascena

aws_lambda_powertools/utilities/batch/base.py

@@ -226,7 +226,7 @@ def __init__(
         self,
         event_type: EventType,
         model: Optional["BatchTypeModels"] = None,
-        raise_on_entire_batch_fail: bool = True,
+        raise_on_entire_batch_failure: bool = True,
     ):
         """Process batch and partially report failed items
 
@@ -247,7 +247,7 @@ def __init__(
         """
         self.event_type = event_type
         self.model = model
-        self.raise_on_entire_batch_fail = raise_on_entire_batch_fail
+        self.raise_on_entire_batch_fail = raise_on_entire_batch_failure
         self.batch_response: PartialItemFailureResponse = copy.deepcopy(self.DEFAULT_RESPONSE)
         self._COLLECTOR_MAPPING = {
             EventType.SQS: self._collect_sqs_failures,
@@ -484,7 +484,7 @@ def lambda_handler(event, context: LambdaContext):
     Raises
     ------
     BatchProcessingError
-        When all batch records fail processing and raise_on_entire_batch_fail is True
+        When all batch records fail processing and raise_on_entire_batch_failure is True
 
     Limitations
     -----------
@@ -633,7 +633,7 @@ def lambda_handler(event, context: LambdaContext):
     Raises
     ------
     BatchProcessingError
-        When all batch records fail processing and raise_on_entire_batch_fail is True
+        When all batch records fail processing and raise_on_entire_batch_failure is True
 
     Limitations
     -----------

docs/utilities/batch.md

@@ -497,7 +497,7 @@ By default, the `BatchProcessor` will raise `BatchProcessingError` if all record
 
 When working with functions that handle batches with a small number of records, or when you use errors as a flow control mechanism, this behavior might not be desirable as your function might generate an unnaturally high number of errors. When this happens, the [Lambda service will scale down the concurrency of your function](https://docs.aws.amazon.com/lambda/latest/dg/services-sqs-errorhandling.html#services-sqs-backoff-strategy){target="_blank"}, potentially impacting performance.
 
-For these scenarios, you can set the `raise_on_entire_batch_fail` option to `False`.
+For these scenarios, you can set the `raise_on_entire_batch_failure` option to `False`.
 
 === "working_with_entire_batch_fail.py"
 

examples/batch_processing/src/working_with_entire_batch_fail.py

@@ -7,7 +7,7 @@
 from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
 from aws_lambda_powertools.utilities.typing import LambdaContext
 
-processor = BatchProcessor(event_type=EventType.SQS, raise_on_entire_batch_fail=False)
+processor = BatchProcessor(event_type=EventType.SQS, raise_on_entire_batch_failure=False)
 tracer = Tracer()
 logger = Logger()
 

tests/functional/batch/required_dependencies/test_utilities_batch.py

@@ -414,7 +414,7 @@ def test_batch_processor_not_raise_when_entire_batch_fails_sync(sqs_event_factor
     event = {"Records": [first_record.raw_event, second_record.raw_event]}
 
     # GIVEN the BatchProcessor constructor with raise_on_entire_batch_fail False
-    processor = BatchProcessor(event_type=EventType.SQS, raise_on_entire_batch_fail=False)
+    processor = BatchProcessor(event_type=EventType.SQS, raise_on_entire_batch_failure=False)
 
     # WHEN processing the messages
     @batch_processor(record_handler=record_handler, processor=processor)
@@ -435,7 +435,7 @@ def test_batch_processor_not_raise_when_entire_batch_fails_async(sqs_event_facto
     event = {"Records": [first_record.raw_event, second_record.raw_event]}
 
     # GIVEN the BatchProcessor constructor with raise_on_entire_batch_fail False
-    processor = AsyncBatchProcessor(event_type=EventType.SQS, raise_on_entire_batch_fail=False)
+    processor = AsyncBatchProcessor(event_type=EventType.SQS, raise_on_entire_batch_failure=False)
 
     # WHEN processing the messages
     @async_batch_processor(record_handler=record_handler, processor=processor)