Add Athena UNLOAD

awswrangler/athena/__init__.py

@@ -1,6 +1,6 @@
 """Amazon Athena Module."""
 
-from awswrangler.athena._read import read_sql_query, read_sql_table  # noqa
+from awswrangler.athena._read import read_sql_query, read_sql_table, unload  # noqa
 from awswrangler.athena._utils import (  # noqa
     create_athena_bucket,
     describe_table,
@@ -26,5 +26,6 @@
     "show_create_table",
     "start_query_execution",
     "stop_query_execution",
+    "unload",
     "wait_query",
 ]

awswrangler/athena/_read.py

@@ -339,6 +339,54 @@ def _resolve_query_without_cache_ctas(
     )
 
 
+def _resolve_query_without_cache_unload(
+    sql: str,
+    file_format: str,
+    compression: Optional[str],
+    field_delimiter: Optional[str],
+    partitioned_by: Optional[List[str]],
+    database: Optional[str],
+    data_source: Optional[str],
+    s3_output: str,
+    keep_files: bool,
+    chunksize: Union[int, bool, None],
+    categories: Optional[List[str]],
+    encryption: Optional[str],
+    kms_key: Optional[str],
+    wg_config: _WorkGroupConfig,
+    use_threads: Union[bool, int],
+    s3_additional_kwargs: Optional[Dict[str, Any]],
+    boto3_session: boto3.Session,
+    pyarrow_additional_kwargs: Optional[Dict[str, Any]] = None,
+) -> Union[pd.DataFrame, Iterator[pd.DataFrame]]:
+    query_metadata = _unload(
+        sql,
+        s3_output,
+        file_format,
+        compression,
+        field_delimiter,
+        partitioned_by,
+        wg_config,
+        database,
+        encryption,
+        kms_key,
+        boto3_session,
+        data_source,
+    )
+    if file_format == "PARQUET":
+        return _fetch_parquet_result(
+            query_metadata=query_metadata,
+            keep_files=keep_files,
+            categories=categories,
+            chunksize=chunksize,
+            use_threads=use_threads,
+            s3_additional_kwargs=s3_additional_kwargs,
+            boto3_session=boto3_session,
+            pyarrow_additional_kwargs=pyarrow_additional_kwargs,
+        )
+    raise exceptions.InvalidArgumentValue("Only PARQUET file format is supported when unload_approach=True.")
+
+
 def _resolve_query_without_cache_regular(
     sql: str,
     database: Optional[str],
@@ -390,6 +438,8 @@ def _resolve_query_without_cache(
     database: str,
     data_source: Optional[str],
     ctas_approach: bool,
+    unload_approach: bool,
+    unload_parameters: Optional[Dict[str, Any]],
     categories: Optional[List[str]],
     chunksize: Union[int, bool, None],
     s3_output: Optional[str],
@@ -443,6 +493,29 @@ def _resolve_query_without_cache(
             catalog.delete_table_if_exists(
                 database=ctas_database_name or database, table=name, boto3_session=boto3_session
             )
+    elif unload_approach is True:
+        if unload_parameters is None:
+            unload_parameters = {}
+        return _resolve_query_without_cache_unload(
+            sql=sql,
+            file_format=unload_parameters.get("file_format") or "PARQUET",
+            compression=unload_parameters.get("compression"),
+            field_delimiter=unload_parameters.get("field_delimiter"),
+            partitioned_by=unload_parameters.get("partitioned_by"),
+            database=database,
+            data_source=data_source,
+            s3_output=_s3_output,
+            keep_files=keep_files,
+            chunksize=chunksize,
+            categories=categories,
+            encryption=encryption,
+            kms_key=kms_key,
+            wg_config=wg_config,
+            use_threads=use_threads,
+            s3_additional_kwargs=s3_additional_kwargs,
+            boto3_session=boto3_session,
+            pyarrow_additional_kwargs=pyarrow_additional_kwargs,
+        )
     return _resolve_query_without_cache_regular(
         sql=sql,
         database=database,
@@ -461,11 +534,81 @@ def _resolve_query_without_cache(
     )
 
 
+def _unload(
+    sql: str,
+    path: str,
+    file_format: str,
+    compression: Optional[str],
+    field_delimiter: Optional[str],
+    partitioned_by: Optional[List[str]],
+    wg_config: _WorkGroupConfig,
+    database: Optional[str],
+    encryption: Optional[str],
+    kms_key: Optional[str],
+    boto3_session: boto3.Session,
+    data_source: Optional[str],
+) -> _QueryMetadata:
+    # Set UNLOAD parameters
+    unload_parameters = f"  format='{file_format}'"
+    if compression:
+        unload_parameters += f"  , compression='{compression}'"
+    if field_delimiter:
+        unload_parameters += f"  , field_delimiter='{field_delimiter}'"
+    if partitioned_by:
+        unload_parameters += f"  , partitioned_by=ARRAY{partitioned_by}"
+
+    sql = f"UNLOAD ({sql}) " f"TO '{path}' " f"WITH ({unload_parameters})"
+    _logger.debug("sql: %s", sql)
+    try:
+        query_id: str = _start_query_execution(
+            sql=sql,
+            wg_config=wg_config,
+            database=database,
+            data_source=data_source,
+            s3_output=path,
+            encryption=encryption,
+            kms_key=kms_key,
+            boto3_session=boto3_session,
+        )
+    except botocore.exceptions.ClientError as ex:
+        msg: str = str(ex)
+        error: Dict[str, Any] = ex.response["Error"]
+        if error["Code"] == "InvalidRequestException":
+            raise exceptions.InvalidArgumentValue(f"Exception parsing query. Root error message: {msg}")
+        raise ex
+    _logger.debug("query_id: %s", query_id)
+    try:
+        query_metadata: _QueryMetadata = _get_query_metadata(
+            query_execution_id=query_id,
+            boto3_session=boto3_session,
+            metadata_cache_manager=_cache_manager,
+        )
+    except exceptions.QueryFailed as ex:
+        msg = str(ex)
+        if "Column name" in msg and "specified more than once" in msg:
+            raise exceptions.InvalidArgumentValue(
+                f"Please, define distinct names for your columns. Root error message: {msg}"
+            )
+        if "Column name not specified" in msg:
+            raise exceptions.InvalidArgumentValue(
+                "Please, define all columns names in your query. (E.g. 'SELECT MAX(col1) AS max_col1, ...')"
+            )
+        if "Column type is unknown" in msg:
+            raise exceptions.InvalidArgumentValue(
+                "Please, don't leave undefined columns types in your query. You can cast to ensure it. "
+                "(E.g. 'SELECT CAST(NULL AS INTEGER) AS MY_COL, ...')"
+            )
+        raise ex
+    return query_metadata
+
+
 @apply_configs
 def read_sql_query(
     sql: str,
     database: str,
     ctas_approach: bool = True,
+    unload_approach: bool = False,
+    unload_parameters: Optional[Dict[str, Any]] = None,
     categories: Optional[List[str]] = None,
     chunksize: Optional[Union[int, bool]] = None,
     s3_output: Optional[str] = None,
@@ -498,7 +641,7 @@ def read_sql_query(
     - `Global Configurations <https://aws-data-wrangler.readthedocs.io/en/2.13.0/
       tutorials/021%20-%20Global%20Configurations.html>`_
 
-    **There are two approaches to be defined through ctas_approach parameter:**
+    **There are three approaches available through ctas_approach and unload_approach parameters:**
 
     **1** - ctas_approach=True (Default):
 
@@ -518,7 +661,25 @@ def read_sql_query(
     - A temporary table will be created and then deleted immediately.
     - Does not support custom data_source/catalog_id.
 
-    **2** - ctas_approach=False:
+    **2** - unload_approach=True and ctas_approach=False:
+
+    Does an UNLOAD query on Athena and parse the Parquet result on s3.
+
+    PROS:
+
+    - Faster for mid and big result sizes.
+    - Can handle some level of nested types.
+    - Does not modify Glue Data Catalog
+
+    CONS:
+
+    - Output S3 path must be empty.
+    - Does not support timestamp with time zone.
+    - Does not support columns with repeated names.
+    - Does not support columns with undefined data types.
+    - Does not support custom data_source/catalog_id.
+
+    **3** - ctas_approach=False:
 
     Does a regular query on Athena and parse the regular CSV result on s3.
 
@@ -534,7 +695,6 @@ def read_sql_query(
     - Slower for big results (But stills faster than other libraries that uses the regular Athena's API)
     - Does not handle nested types at all.
 
-
     Note
     ----
     The resulting DataFrame (or every DataFrame in the returned Iterator for chunked queries) have a
@@ -574,7 +734,7 @@ def read_sql_query(
     `P.S.` `chunksize=True` is faster and uses less memory while `chunksize=INTEGER` is more precise
     in number of rows for each Dataframe.
 
-    `P.P.S.` If `ctas_approach=False` and `chunksize=True`, you will always receive an interador with a
+    `P.P.S.` If `ctas_approach=False` and `chunksize=True`, you will always receive an iterator with a
     single DataFrame because regular Athena queries only produces a single output file.
 
     Note
@@ -593,6 +753,11 @@ def read_sql_query(
     ctas_approach: bool
         Wraps the query using a CTAS, and read the resulted parquet data on S3.
         If false, read the regular CSV on S3.
+    unload_approach: bool
+        Wraps the query using UNLOAD, and read the results from S3.
+        Only PARQUET format is supported.
+    unload_parameters : Optional[Dict[str, Any]]
+        Params of the UNLOAD such as format, compression, field_delimiter, and partitioned_by.
     categories: List[str], optional
         List of columns names that should be returned as pandas.Categorical.
         Recommended for memory restricted environments.
@@ -691,6 +856,10 @@ def read_sql_query(
             "(https://github.com/awslabs/aws-data-wrangler/blob/main/"
             "tutorials/006%20-%20Amazon%20Athena.ipynb)"
         )
+    if ctas_approach and unload_approach:
+        raise exceptions.InvalidArgumentCombination("Only one of ctas_approach=True or unload_approach=True is allowed")
+    if unload_parameters and unload_parameters.get("file_format") not in (None, "PARQUET"):
+        raise exceptions.InvalidArgumentCombination("Only PARQUET file format is supported if unload_approach=True")
     chunksize = sys.maxsize if ctas_approach is False and chunksize is True else chunksize
     session: boto3.Session = _utils.ensure_session(session=boto3_session)
     if params is None:
@@ -730,6 +899,8 @@ def read_sql_query(
         database=database,
         data_source=data_source,
         ctas_approach=ctas_approach,
+        unload_approach=unload_approach,
+        unload_parameters=unload_parameters,
         categories=categories,
         chunksize=chunksize,
         s3_output=s3_output,
@@ -979,3 +1150,95 @@ def read_sql_table(
         s3_additional_kwargs=s3_additional_kwargs,
         pyarrow_additional_kwargs=pyarrow_additional_kwargs,
     )
+
+
+@apply_configs
+def unload(
+    sql: str,
+    path: str,
+    database: str,
+    file_format: str = "PARQUET",
+    compression: Optional[str] = None,
+    field_delimiter: Optional[str] = None,
+    partitioned_by: Optional[List[str]] = None,
+    workgroup: Optional[str] = None,
+    encryption: Optional[str] = None,
+    kms_key: Optional[str] = None,
+    boto3_session: Optional[boto3.Session] = None,
+    data_source: Optional[str] = None,
+    params: Optional[Dict[str, Any]] = None,
+) -> _QueryMetadata:
+    """Write query results from a SELECT statement to the specified data format using UNLOAD.
+
+    https://docs.aws.amazon.com/athena/latest/ug/unload.html
+
+    Parameters
+    ----------
+    sql : str
+        SQL query.
+    path : str, optional
+        Amazon S3 path.
+    database : str
+        AWS Glue/Athena database name - It is only the origin database from where the query will be launched.
+        You can still using and mixing several databases writing the full table name within the sql
+        (e.g. `database.table`).
+    file_format : str
+        File format of the output. Possible values are ORC, PARQUET, AVRO, JSON, or TEXTFILE
+    compression : Optional[str]
+        This option is specific to the ORC and Parquet formats. For ORC, possible values are lz4, snappy, zlib, or zstd.
+        For Parquet, possible values are gzip or snappy. For ORC, the default is zlib, and for Parquet,
+        the default is gzip.
+    field_delimiter : str
+        A single-character field delimiter for files in CSV, TSV, and other text formats.
+    partitioned_by : Optional[List[str]]
+        An array list of columns by which the output is partitioned.
+    workgroup : str, optional
+        Athena workgroup.
+    encryption : str, optional
+        Valid values: [None, 'SSE_S3', 'SSE_KMS']. Notice: 'CSE_KMS' is not supported.
+    kms_key : str, optional
+        For SSE-KMS, this is the KMS key ARN or ID.
+    boto3_session : boto3.Session(), optional
+        Boto3 Session. The default boto3 session will be used if boto3_session receive None.
+    data_source : str, optional
+        Data Source / Catalog name. If None, 'AwsDataCatalog' will be used by default.
+    params: Dict[str, any], optional
+        Dict of parameters that will be used for constructing the SQL query. Only named parameters are supported.
+        The dict needs to contain the information in the form {'name': 'value'} and the SQL query needs to contain
+        `:name;`. Note that for varchar columns and similar, you must surround the value in single quotes.
+
+    Returns
+    -------
+    _QueryMetadata
+        Query metadata including query execution id, dtypes, manifest & output location.
+
+    Examples
+    --------
+    >>> import awswrangler as wr
+    >>> res = wr.athena.unload(
+    ...     sql="SELECT * FROM my_table WHERE name=:name; AND city=:city;",
+    ...     params={"name": "'filtered_name'", "city": "'filtered_city'"}
+    ... )
+
+    """
+    session: boto3.Session = _utils.ensure_session(session=boto3_session)
+    wg_config: _WorkGroupConfig = _get_workgroup_config(session=session, workgroup=workgroup)
+    # Substitute query parameters
+    if params is None:
+        params = {}
+    for key, value in params.items():
+        sql = sql.replace(f":{key};", str(value))
+    return _unload(
+        sql,
+        path,
+        file_format,
+        compression,
+        field_delimiter,
+        partitioned_by,
+        wg_config,
+        database,
+        encryption,
+        kms_key,
+        session,
+        data_source,
+    )

awswrangler/athena/_utils.py

@@ -208,13 +208,13 @@ def _get_query_metadata(  # pylint: disable=too-many-statements
     for col_name, col_type in cols_types.items():
         if col_type == "array":
             raise exceptions.UnsupportedType(
-                "List data type is not support with ctas_approach=False. "
-                "Please use ctas_approach=True for List columns."
+                "List data type is not supported with regular (non-CTAS and non-UNLOAD) queries. "
+                "Please use ctas_approach=True or unload_approach=True for List columns."
             )
         if col_type == "row":
             raise exceptions.UnsupportedType(
-                "Struct data type is not support with ctas_approach=False. "
-                "Please use ctas_approach=True for Struct columns."
+                "Struct data type is not supported with regular (non-CTAS and non-UNLOAD) queries. "
+                "Please use ctas_approach=True or unload_approach=True for Struct columns."
             )
         pandas_type: str = _data_types.athena2pandas(dtype=col_type)
         if (categories is not None) and (col_name in categories):