(enhancement): Add unload_approach to athena.read_sql_table (#1634)

Author: jaidisido

awswrangler/athena/_read.py

@@ -969,6 +969,8 @@ def read_sql_table(
     table: 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,
@@ -1090,6 +1092,11 @@ def read_sql_table(
     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.
@@ -1177,6 +1184,8 @@ def read_sql_table(
         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,

tests/test_athena_parquet.py

@@ -800,10 +800,10 @@ def test_read_sql_query_unload(path, glue_table, glue_database, file_format):
         table=glue_table,
         database=glue_database,
     )
-    df_out = wr.athena.read_sql_query(
-        sql=f"SELECT * FROM {glue_database}.{glue_table}",
-        s3_output=f"{path}unload/",
+    df_out = wr.athena.read_sql_table(
+        table=glue_table,
         database=glue_database,
+        s3_output=f"{path}unload/",
         ctas_approach=False,
         unload_approach=True,
         unload_parameters={"file_format": file_format},