docs(batch): improved the example demonstrating how to create a custom partial processor.

docs/utilities/batch.md

@@ -604,12 +604,28 @@ classDiagram
 * **`_prepare()`** – called once as part of the processor initialization
 * **`_clean()`** – teardown logic called once after `_process_record` completes
 * **`_async_process_record()`** – If you need to implement asynchronous logic, use this method, otherwise define it in your class with empty logic
+* **`response()`** - called upon completion of processing
 
-You can then use this class as a context manager, or pass it to `batch_processor` to use as a decorator on your Lambda handler function.
+You can utilize this class to instantiate a new processor and then pass it to the `process_partial_response` function.
+
+=== "Creating a custom batch processor"
+
+    ```python hl_lines="10-13 21 37 43 46 53 64 69 73"
+    --8<-- "examples/batch_processing/src/custom_partial_processor.py"
+    ```
+
+=== "DynamoDB table used for storing processed records."
+
+    ```yaml
+    --8<-- "examples/batch_processing/sam/custom_partial_processor_dynamodb_table.yaml"
+    ```
+
+=== "Sample event"
+
+    ```json
+    --8<-- "examples/batch_processing/src/custom_partial_processor_payload.json"
+    ```
 
-```python hl_lines="9-11 19 33 39 46 57 62 66 74" title="Creating a custom batch processor"
---8<-- "examples/batch_processing/src/custom_partial_processor.py"
-```
 
 ### Caveats
 

examples/batch_processing/sam/custom_partial_processor_dynamodb_table.yaml

@@ -0,0 +1,15 @@
+Transform: AWS::Serverless-2016-10-31
+Resources:
+  IdempotencyTable:
+    Type: AWS::DynamoDB::Table
+    Properties:
+      AttributeDefinitions:
+        - AttributeName: messageId
+          AttributeType: S
+      KeySchema:
+        - AttributeName: messageId
+          KeyType: HASH
+      TimeToLiveSpecification:
+        AttributeName: expiration
+        Enabled: true
+      BillingMode: PAY_PER_REQUEST

examples/batch_processing/src/custom_partial_processor.py

@@ -1,3 +1,4 @@
+import copy
 import os
 import sys
 from random import randint
@@ -10,13 +11,15 @@
     BasePartialProcessor,
     process_partial_response,
 )
+from aws_lambda_powertools.utilities.batch.types import PartialItemFailureResponse
 
-table_name = os.getenv("TABLE_NAME", "table_not_found")
+table_name = os.getenv("TABLE_NAME", "table_store_batch")
 
 logger = Logger()
 
 
 class MyPartialProcessor(BasePartialProcessor):
+    DEFAULT_RESPONSE: PartialItemFailureResponse = {"batchItemFailures": []}
     """
     Process a record and stores successful results at a Amazon DynamoDB Table
 
@@ -28,6 +31,7 @@ class MyPartialProcessor(BasePartialProcessor):
 
     def __init__(self, table_name: str):
         self.table_name = table_name
+        self.batch_response: PartialItemFailureResponse = copy.deepcopy(self.DEFAULT_RESPONSE)
         super().__init__()
 
     def _prepare(self):
@@ -36,6 +40,9 @@ def _prepare(self):
         self.ddb_table = boto3.resource("dynamodb").Table(self.table_name)
         self.success_messages.clear()
 
+    def response(self) -> PartialItemFailureResponse:
+        return self.batch_response
+
     def _clean(self):
         # It's called once, *after* closing processing all records (closing the context manager)
         # Here we're sending, at once, all successful messages to a ddb table

examples/batch_processing/src/custom_partial_processor_payload.json

@@ -0,0 +1,14 @@
+{
+    "Records": [
+      {
+        "messageId": "059f36b4-87a3-44ab-83d2-661975830a7d",
+        "receiptHandle": "AQEBwJnKyrHigUMZj6rYigCgxlaS3SLy0a",
+        "body": "{\"Message\": \"success\"}"
+      },
+      {
+        "messageId": "244fc6b4-87a3-44ab-83d2-361172410c3a",
+        "receiptHandle": "AQEBwJnKyrHigUMZj6rYigCgxlaS3SLy0a",
+        "body": "SGVsbG8sIHRoaXMgaXMgYSB0ZXN0Lg=="
+      }
+    ]
+  }