feat(batch): add support to SQS FIFO queues (SqsFifoPartialProcessor)

aws_lambda_powertools/utilities/batch/__init__.py

@@ -16,16 +16,22 @@
     batch_processor,
 )
 from aws_lambda_powertools.utilities.batch.exceptions import ExceptionInfo
+from aws_lambda_powertools.utilities.batch.sqs_fifo_partial_processor import (
+    SqsFifoPartialProcessor,
+)
+from aws_lambda_powertools.utilities.batch.types import BatchTypeModels
 
 __all__ = (
     "BatchProcessor",
     "AsyncBatchProcessor",
     "BasePartialProcessor",
     "BasePartialBatchProcessor",
+    "BatchTypeModels",
     "ExceptionInfo",
     "EventType",
     "FailureResponse",
     "SuccessResponse",
+    "SqsFifoPartialProcessor",
     "batch_processor",
     "async_batch_processor",
 )

aws_lambda_powertools/utilities/batch/base.py

@@ -19,7 +19,6 @@
     List,
     Optional,
     Tuple,
-    Type,
     Union,
     overload,
 )
@@ -30,6 +29,7 @@
     BatchProcessingError,
     ExceptionInfo,
 )
+from aws_lambda_powertools.utilities.batch.types import BatchTypeModels
 from aws_lambda_powertools.utilities.data_classes.dynamo_db_stream_event import (
     DynamoDBRecord,
 )
@@ -48,24 +48,6 @@ class EventType(Enum):
     DynamoDBStreams = "DynamoDBStreams"
 
 
-#
-# type specifics
-#
-has_pydantic = "pydantic" in sys.modules
-
-# For IntelliSense and Mypy to work, we need to account for possible SQS, Kinesis and DynamoDB subclasses
-# We need them as subclasses as we must access their message ID or sequence number metadata via dot notation
-if has_pydantic:
-    from aws_lambda_powertools.utilities.parser.models import DynamoDBStreamRecordModel
-    from aws_lambda_powertools.utilities.parser.models import (
-        KinesisDataStreamRecord as KinesisDataStreamRecordModel,
-    )
-    from aws_lambda_powertools.utilities.parser.models import SqsRecordModel
-
-    BatchTypeModels = Optional[
-        Union[Type[SqsRecordModel], Type[DynamoDBStreamRecordModel], Type[KinesisDataStreamRecordModel]]
-    ]
-
 # When using processor with default arguments, records will carry EventSourceDataClassTypes
 # and depending on what EventType it's passed it'll correctly map to the right record
 # When using Pydantic Models, it'll accept any subclass from SQS, DynamoDB and Kinesis

aws_lambda_powertools/utilities/batch/sqs_fifo_partial_processor.py

@@ -0,0 +1,92 @@
+from typing import List, Optional, Tuple
+
+from aws_lambda_powertools.utilities.batch import BatchProcessor, EventType
+from aws_lambda_powertools.utilities.batch.types import BatchSqsTypeModel
+
+
+class SQSFifoCircuitBreakerError(Exception):
+    """
+    Signals a record not processed due to the SQS FIFO processing being interrupted
+    """
+
+    pass
+
+
+class SqsFifoPartialProcessor(BatchProcessor):
+    """Process native partial responses from SQS FIFO queues.
+
+    Stops processing records when the first record fails. The remaining records are reported as failed items.
+
+    Example
+    _______
+
+    ## Process batch triggered by a FIFO SQS
+
+    ```python
+    import json
+
+    from aws_lambda_powertools import Logger, Tracer
+    from aws_lambda_powertools.utilities.batch import SqsFifoPartialProcessor, EventType, batch_processor
+    from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
+    from aws_lambda_powertools.utilities.typing import LambdaContext
+
+
+    processor = SqsFifoPartialProcessor()
+    tracer = Tracer()
+    logger = Logger()
+
+
+    @tracer.capture_method
+    def record_handler(record: SQSRecord):
+        payload: str = record.body
+        if payload:
+            item: dict = json.loads(payload)
+        ...
+
+    @logger.inject_lambda_context
+    @tracer.capture_lambda_handler
+    @batch_processor(record_handler=record_handler, processor=processor)
+    def lambda_handler(event, context: LambdaContext):
+        return processor.response()
+    ```
+    """
+
+    circuit_breaker_exc = (
+        SQSFifoCircuitBreakerError,
+        SQSFifoCircuitBreakerError("A previous record failed processing"),
+        None,
+    )
+
+    def __init__(self, model: Optional["BatchSqsTypeModel"] = None):
+        super().__init__(EventType.SQS, model)
+
+    def process(self) -> List[Tuple]:
+        """
+        Call instance's handler for each record. When the first failed message is detected,
+        the process is short-circuited, and the remaining messages are reported as failed items.
+        """
+        result: List[Tuple] = []
+
+        for i, record in enumerate(self.records):
+            # If we have failed messages, it means that the last message failed.
+            # We then short circuit the process, failing the remaining messages
+            if self.fail_messages:
+                return self._short_circuit_processing(i, result)
+
+            # Otherwise, process the message normally
+            result.append(self._process_record(record))
+
+        return result
+
+    def _short_circuit_processing(self, first_failure_index: int, result: List[Tuple]) -> List[Tuple]:
+        """
+        Starting from the first failure index, fail all the remaining messages, and append them to the result list.
+        """
+        remaining_records = self.records[first_failure_index:]
+        for remaining_record in remaining_records:
+            data = self._to_batch_type(record=remaining_record, event_type=self.event_type, model=self.model)
+            result.append(self.failure_handler(record=data, exception=self.circuit_breaker_exc))
+        return result
+
+    async def _async_process_record(self, record: dict):
+        raise NotImplementedError()

aws_lambda_powertools/utilities/batch/types.py

@@ -0,0 +1,24 @@
+#
+# type specifics
+#
+import sys
+from typing import Optional, Type, Union
+
+has_pydantic = "pydantic" in sys.modules
+
+# For IntelliSense and Mypy to work, we need to account for possible SQS subclasses
+# We need them as subclasses as we must access their message ID or sequence number metadata via dot notation
+if has_pydantic:
+    from aws_lambda_powertools.utilities.parser.models import DynamoDBStreamRecordModel
+    from aws_lambda_powertools.utilities.parser.models import (
+        KinesisDataStreamRecord as KinesisDataStreamRecordModel,
+    )
+    from aws_lambda_powertools.utilities.parser.models import SqsRecordModel
+
+    BatchTypeModels = Optional[
+        Union[Type[SqsRecordModel], Type[DynamoDBStreamRecordModel], Type[KinesisDataStreamRecordModel]]
+    ]
+    BatchSqsTypeModel = Optional[Type[SqsRecordModel]]
+else:
+    BatchTypeModels = "BatchTypeModels"  # type: ignore
+    BatchSqsTypeModel = "BatchSqsTypeModel"  # type: ignore

docs/utilities/batch.md

@@ -347,6 +347,24 @@ Processing batches from SQS works in four stages:
     }
     ```
 
+#### FIFO queues
+
+When using [SQS FIFO queues](https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/FIFO-queues.html){target="_blank"},
+We will stop processing messages after the first failure, and return all failed and unprocessed messages in `batchItemFailures`.
+This helps preserve the ordering of messages in your queue.
+
+=== "As a decorator"
+
+    ```python hl_lines="5 11"
+    --8<-- "examples/batch_processing/src/sqs_fifo_batch_processor.py"
+    ```
+
+=== "As a context manager"
+
+    ```python hl_lines="4 8"
+    --8<-- "examples/batch_processing/src/sqs_fifo_batch_processor_context_manager.py"
+    ```
+
 ### Processing messages from Kinesis
 
 Processing batches from Kinesis works in four stages:

examples/batch_processing/src/sqs_fifo_batch_processor.py

@@ -0,0 +1,23 @@
+from aws_lambda_powertools import Logger, Tracer
+from aws_lambda_powertools.utilities.batch import (
+    SqsFifoPartialProcessor,
+    batch_processor,
+)
+from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
+from aws_lambda_powertools.utilities.typing import LambdaContext
+
+processor = SqsFifoPartialProcessor()
+tracer = Tracer()
+logger = Logger()
+
+
+@tracer.capture_method
+def record_handler(record: SQSRecord):
+    ...
+
+
+@logger.inject_lambda_context
+@tracer.capture_lambda_handler
+@batch_processor(record_handler=record_handler, processor=processor)
+def lambda_handler(event, context: LambdaContext):
+    return processor.response()

examples/batch_processing/src/sqs_fifo_batch_processor_context_manager.py

@@ -0,0 +1,23 @@
+from aws_lambda_powertools import Logger, Tracer
+from aws_lambda_powertools.utilities.batch import SqsFifoPartialProcessor
+from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
+from aws_lambda_powertools.utilities.typing import LambdaContext
+
+processor = SqsFifoPartialProcessor()
+tracer = Tracer()
+logger = Logger()
+
+
+@tracer.capture_method
+def record_handler(record: SQSRecord):
+    ...
+
+
+@logger.inject_lambda_context
+@tracer.capture_lambda_handler
+def lambda_handler(event, context: LambdaContext):
+    batch = event["Records"]
+    with processor(records=batch, handler=record_handler):
+        processor.process()  # kick off processing, return List[Tuple]
+
+    return processor.response()

tests/functional/test_utilities_batch.py

@@ -1,4 +1,5 @@
 import json
+import uuid
 from random import randint
 from typing import Any, Awaitable, Callable, Dict, Optional
 
@@ -9,6 +10,7 @@
     AsyncBatchProcessor,
     BatchProcessor,
     EventType,
+    SqsFifoPartialProcessor,
     async_batch_processor,
     batch_processor,
 )
@@ -40,7 +42,7 @@
 def sqs_event_factory() -> Callable:
     def factory(body: str):
         return {
-            "messageId": "059f36b4-87a3-44ab-83d2-661975830a7d",
+            "messageId": f"{uuid.uuid4()}",
             "receiptHandle": "AQEBwJnKyrHigUMZj6rYigCgxlaS3SLy0a",
             "body": body,
             "attributes": {
@@ -654,6 +656,48 @@ def lambda_handler(event, context):
     assert "All records failed processing. " in str(e.value)
 
 
+def test_sqs_fifo_batch_processor_middleware_success_only(sqs_event_factory, record_handler):
+    # GIVEN
+    first_record = SQSRecord(sqs_event_factory("success"))
+    second_record = SQSRecord(sqs_event_factory("success"))
+    event = {"Records": [first_record.raw_event, second_record.raw_event]}
+
+    processor = SqsFifoPartialProcessor()
+
+    @batch_processor(record_handler=record_handler, processor=processor)
+    def lambda_handler(event, context):
+        return processor.response()
+
+    # WHEN
+    result = lambda_handler(event, {})
+
+    # THEN
+    assert result["batchItemFailures"] == []
+
+
+def test_sqs_fifo_batch_processor_middleware_with_failure(sqs_event_factory, record_handler):
+    # GIVEN
+    first_record = SQSRecord(sqs_event_factory("success"))
+    second_record = SQSRecord(sqs_event_factory("fail"))
+    # this would normally succeed, but since it's a FIFO queue, it will be marked as failure
+    third_record = SQSRecord(sqs_event_factory("success"))
+    event = {"Records": [first_record.raw_event, second_record.raw_event, third_record.raw_event]}
+
+    processor = SqsFifoPartialProcessor()
+
+    @batch_processor(record_handler=record_handler, processor=processor)
+    def lambda_handler(event, context):
+        return processor.response()
+
+    # WHEN
+    result = lambda_handler(event, {})
+
+    # THEN
+    assert len(result["batchItemFailures"]) == 2
+    assert result["batchItemFailures"][0]["itemIdentifier"] == second_record.message_id
+    assert result["batchItemFailures"][1]["itemIdentifier"] == third_record.message_id
+
+
 def test_async_batch_processor_middleware_success_only(sqs_event_factory, async_record_handler):
     # GIVEN
     first_record = SQSRecord(sqs_event_factory("success"))