feat: Redshift UNLOAD - add parallel parameter

awswrangler/redshift/_read.py

@@ -224,6 +224,7 @@ def unload_to_files(
     aws_session_token: Optional[str] = None,
     region: Optional[str] = None,
     unload_format: Optional[Literal["CSV", "PARQUET"]] = None,
+    parallel: bool = True,
     max_file_size: Optional[float] = None,
     kms_key_id: Optional[str] = None,
     manifest: bool = False,
@@ -265,6 +266,11 @@ def unload_to_files(
     unload_format: str, optional
         Format of the unloaded S3 objects from the query.
         Valid values: "CSV", "PARQUET". Case sensitive. Defaults to PARQUET.
+    parallel: bool
+        Whether to unload to multiple files in parallel. Defaults to True.
+        By default, UNLOAD writes data in parallel to multiple files, according to the number of
+        slices in the cluster. If parallel is False, UNLOAD writes to one or more data files serially,
+        sorted absolutely according to the ORDER BY clause, if one is used.
     max_file_size : float, optional
         Specifies the maximum size (MB) of files that UNLOAD creates in Amazon S3.
         Specify a decimal value between 5.0 MB and 6200.0 MB. If None, the default
@@ -305,6 +311,7 @@ def unload_to_files(
         partition_str: str = f"\nPARTITION BY ({','.join(partition_cols)})" if partition_cols else ""
         manifest_str: str = "\nmanifest" if manifest is True else ""
         region_str: str = f"\nREGION AS '{region}'" if region is not None else ""
+        parallel_str: str = "\nPARALLEL ON" if parallel else "\nPARALLEL OFF"
         if not max_file_size and engine.get() == EngineEnum.RAY:
             _logger.warning(
                 "Unload `MAXFILESIZE` is not specified. "
@@ -330,7 +337,7 @@ def unload_to_files(
             f"TO '{path}'\n"
             f"{auth_str}"
             "ALLOWOVERWRITE\n"
-            "PARALLEL ON\n"
+            f"{parallel_str}\n"
             f"FORMAT {format_str}\n"
             "ENCRYPTED"
             f"{kms_key_id_str}"
@@ -362,6 +369,7 @@ def unload(
     dtype_backend: Literal["numpy_nullable", "pyarrow"] = "numpy_nullable",
     chunked: Union[bool, int] = False,
     keep_files: bool = False,
+    parallel: bool = True,
     use_threads: Union[bool, int] = True,
     boto3_session: Optional[boto3.Session] = None,
     s3_additional_kwargs: Optional[Dict[str, str]] = None,
@@ -433,6 +441,11 @@ def unload(
         used to encrypt data files on Amazon S3.
     keep_files : bool
         Should keep stage files?
+    parallel: bool
+        Whether to unload to multiple files in parallel. Defaults to True.
+        By default, UNLOAD writes data in parallel to multiple files, according to the number of
+        slices in the cluster. If parallel is False, UNLOAD writes to one or more data files serially,
+        sorted absolutely according to the ORDER BY clause, if one is used.
     dtype_backend: str, optional
         Which dtype_backend to use, e.g. whether a DataFrame should have NumPy arrays,
         nullable dtypes are used for all dtypes that have a nullable implementation when
@@ -487,6 +500,7 @@ def unload(
         max_file_size=max_file_size,
         kms_key_id=kms_key_id,
         manifest=False,
+        parallel=parallel,
         boto3_session=boto3_session,
     )
     if chunked is False:

awswrangler/redshift/_read.pyi

@@ -100,6 +100,7 @@ def unload_to_files(
     aws_session_token: Optional[str] = ...,
     region: Optional[str] = ...,
     unload_format: Optional[Literal["CSV", "PARQUET"]] = ...,
+    parallel: bool = ...,
     max_file_size: Optional[float] = ...,
     kms_key_id: Optional[str] = ...,
     manifest: bool = ...,
@@ -121,6 +122,7 @@ def unload(
     dtype_backend: Literal["numpy_nullable", "pyarrow"] = ...,
     chunked: Literal[False] = ...,
     keep_files: bool = ...,
+    parallel: bool = ...,
     use_threads: Union[bool, int] = ...,
     boto3_session: Optional[boto3.Session] = ...,
     s3_additional_kwargs: Optional[Dict[str, str]] = ...,
@@ -142,6 +144,7 @@ def unload(
     dtype_backend: Literal["numpy_nullable", "pyarrow"] = ...,
     chunked: Literal[True],
     keep_files: bool = ...,
+    parallel: bool = ...,
     use_threads: Union[bool, int] = ...,
     boto3_session: Optional[boto3.Session] = ...,
     s3_additional_kwargs: Optional[Dict[str, str]] = ...,
@@ -163,6 +166,7 @@ def unload(
     dtype_backend: Literal["numpy_nullable", "pyarrow"] = ...,
     chunked: bool,
     keep_files: bool = ...,
+    parallel: bool = ...,
     use_threads: Union[bool, int] = ...,
     boto3_session: Optional[boto3.Session] = ...,
     s3_additional_kwargs: Optional[Dict[str, str]] = ...,
@@ -184,6 +188,7 @@ def unload(
     dtype_backend: Literal["numpy_nullable", "pyarrow"] = ...,
     chunked: Union[bool, int],
     keep_files: bool = ...,
+    parallel: bool = ...,
     use_threads: Union[bool, int] = ...,
     boto3_session: Optional[boto3.Session] = ...,
     s3_additional_kwargs: Optional[Dict[str, str]] = ...,

tests/unit/test_redshift.py

@@ -649,13 +649,15 @@ def test_spectrum_decimal_cast(
     [None, {"ServerSideEncryption": "AES256"}, {"ServerSideEncryption": "aws:kms", "SSEKMSKeyId": None}],
 )
 @pytest.mark.parametrize("use_threads", [True, False])
+@pytest.mark.parametrize("parallel", [True, False])
 def test_copy_unload_kms(
     path: str,
     redshift_table: str,
     redshift_con: redshift_connector.Connection,
     databases_parameters: Dict[str, Any],
     kms_key_id: str,
     use_threads: bool,
+    parallel: bool,
     s3_additional_kwargs: Optional[Dict[str, Any]],
 ) -> None:
     df = pd.DataFrame({"id": [1, 2, 3]})
@@ -678,6 +680,7 @@ def test_copy_unload_kms(
         iam_role=databases_parameters["redshift"]["role"],
         path=path,
         keep_files=False,
+        parallel=parallel,
         use_threads=use_threads,
         s3_additional_kwargs=s3_additional_kwargs,
     )