Merge branch 'main' into upgrade-ray-2.30

awswrangler/athena/_executions.py

@@ -104,7 +104,7 @@ def start_query_execution(
         If cached results are valid, awswrangler ignores the `ctas_approach`, `s3_output`, `encryption`, `kms_key`,
         `keep_files` and `ctas_temp_table_name` params.
         If reading cached data fails for any reason, execution falls back to the usual query run path.
-    athena_query_wait_polling_delay: float, default: 0.25 seconds
+    athena_query_wait_polling_delay: float, default: 1.0 seconds
         Interval in seconds for how often the function will check if the Athena query has completed.
     data_source : str, optional
         Data Source / Catalog name. If None, 'AwsDataCatalog' will be used by default.
@@ -211,7 +211,7 @@ def wait_query(
         Athena query execution ID.
     boto3_session : boto3.Session(), optional
         Boto3 Session. The default boto3 session will be used if boto3_session receive None.
-    athena_query_wait_polling_delay: float, default: 0.25 seconds
+    athena_query_wait_polling_delay: float, default: 1.0 seconds
         Interval in seconds for how often the function will check if the Athena query has completed.
 
     Returns

awswrangler/athena/_read.py

@@ -705,7 +705,7 @@ def get_query_results(
         Forwarded to `to_pandas` method converting from PyArrow tables to Pandas DataFrame.
         Valid values include "split_blocks", "self_destruct", "ignore_metadata".
         e.g. pyarrow_additional_kwargs={'split_blocks': True}.
-    athena_query_wait_polling_delay: float, default: 0.25 seconds
+    athena_query_wait_polling_delay: float, default: 1.0 seconds
         Interval in seconds for how often the function will check if the Athena query has completed.
 
     Returns
@@ -960,7 +960,7 @@ def read_sql_query(
         If reading cached data fails for any reason, execution falls back to the usual query run path.
     data_source : str, optional
         Data Source / Catalog name. If None, 'AwsDataCatalog' will be used by default.
-    athena_query_wait_polling_delay: float, default: 0.25 seconds
+    athena_query_wait_polling_delay: float, default: 1.0 seconds
         Interval in seconds for how often the function will check if the Athena query has completed.
     params: Dict[str, any] | List[str], optional
         Parameters that will be used for constructing the SQL query.
@@ -1426,7 +1426,7 @@ def unload(
 
         - ``named``
         - ``qmark``
-    athena_query_wait_polling_delay: float, default: 0.25 seconds
+    athena_query_wait_polling_delay: float, default: 1.0 seconds
         Interval in seconds for how often the function will check if the Athena query has completed.
 
     Returns

awswrangler/athena/_utils.py

@@ -506,7 +506,7 @@ def repair_table(
         None, 'SSE_S3', 'SSE_KMS', 'CSE_KMS'.
     kms_key : str, optional
         For SSE-KMS and CSE-KMS , this is the KMS key ARN or ID.
-    athena_query_wait_polling_delay: float, default: 0.25 seconds
+    athena_query_wait_polling_delay: float, default: 1.0 seconds
         Interval in seconds for how often the function will check if the Athena query has completed.
     boto3_session : boto3.Session(), optional
         Boto3 Session. The default boto3 session will be used if boto3_session receive None.
@@ -582,7 +582,7 @@ def describe_table(
         None, 'SSE_S3', 'SSE_KMS', 'CSE_KMS'.
     kms_key : str, optional
         For SSE-KMS and CSE-KMS , this is the KMS key ARN or ID.
-    athena_query_wait_polling_delay: float, default: 0.25 seconds
+    athena_query_wait_polling_delay: float, default: 1.0 seconds
         Interval in seconds for how often the function will check if the Athena query has completed.
     s3_additional_kwargs : dict[str, Any], optional
         Forwarded to botocore requests.
@@ -700,7 +700,7 @@ def create_ctas_table(
         Recommended for memory restricted environments.
     wait : bool, default False
         Whether to wait for the query to finish and return a dictionary with the Query metadata.
-    athena_query_wait_polling_delay: float, default: 0.25 seconds
+    athena_query_wait_polling_delay: float, default: 1.0 seconds
         Interval in seconds for how often the function will check if the Athena query has completed.
     execution_params: List[str], optional [DEPRECATED]
         A list of values for the parameters that are used in the SQL query.
@@ -912,7 +912,7 @@ def show_create_table(
         None, 'SSE_S3', 'SSE_KMS', 'CSE_KMS'.
     kms_key : str, optional
         For SSE-KMS and CSE-KMS , this is the KMS key ARN or ID.
-    athena_query_wait_polling_delay: float, default: 0.25 seconds
+    athena_query_wait_polling_delay: float, default: 1.0 seconds
         Interval in seconds for how often the function will check if the Athena query has completed.
     s3_additional_kwargs: dict[str, Any]
         Forwarded to botocore requests.

awswrangler/athena/_write_iceberg.py

@@ -515,7 +515,9 @@ def to_iceberg(
             sql_statement = f"""
                 MERGE INTO "{database}"."{table}" target
                 USING "{database}"."{temp_table}" source
-                ON {' AND '.join([f'target."{x}" = source."{x}"' for x in merge_cols])}
+                ON {' AND '.join([
+                    f'(target."{x}" = source."{x}" OR (target."{x}" IS NULL AND source."{x}" IS NULL))'
+                    for x in merge_cols])}
                 {match_condition}
                 WHEN NOT MATCHED THEN
                     INSERT ({', '.join([f'"{x}"' for x in df.columns])})

tests/unit/test_athena_iceberg.py

@@ -650,6 +650,74 @@ def test_athena_to_iceberg_merge_into(path: str, path2: str, glue_database: str,
     assert_pandas_equals(df_expected, df_out)
 
 
+def test_athena_to_iceberg_merge_into_nulls(path: str, path2: str, glue_database: str, glue_table: str) -> None:
+    df = pd.DataFrame(
+        {
+            "col1": ["a", "a", "a", np.nan],
+            "col2": [0.0, 1.1, np.nan, 2.2],
+            "action": ["insert", "insert", "insert", "insert"],
+        }
+    )
+    df["col1"] = df["col1"].astype("string")
+    df["col2"] = df["col2"].astype("float64")
+    df["action"] = df["action"].astype("string")
+
+    wr.athena.to_iceberg(
+        df=df,
+        database=glue_database,
+        table=glue_table,
+        table_location=path,
+        temp_path=path2,
+        keep_files=False,
+    )
+
+    # Perform MERGE INTO
+    df2 = pd.DataFrame(
+        {
+            "col1": ["a", "a", np.nan, "b"],
+            "col2": [1.1, np.nan, 2.2, 3.3],
+            "action": ["update", "update", "update", "insert"],
+        }
+    )
+    df2["col1"] = df2["col1"].astype("string")
+    df2["col2"] = df2["col2"].astype("float64")
+    df2["action"] = df2["action"].astype("string")
+
+    wr.athena.to_iceberg(
+        df=df2,
+        database=glue_database,
+        table=glue_table,
+        table_location=path,
+        temp_path=path2,
+        keep_files=False,
+        merge_cols=["col1", "col2"],
+    )
+
+    # Expected output
+    df_expected = pd.DataFrame(
+        {
+            "col1": ["a", "a", "a", np.nan, "b"],
+            "col2": [0.0, 1.1, np.nan, 2.2, 3.3],
+            "action": ["insert", "update", "update", "update", "insert"],
+        }
+    )
+    df_expected["col1"] = df_expected["col1"].astype("string")
+    df_expected["col2"] = df_expected["col2"].astype("float64")
+    df_expected["action"] = df_expected["action"].astype("string")
+
+    df_out = wr.athena.read_sql_query(
+        sql=f'SELECT * FROM "{glue_table}"',
+        database=glue_database,
+        ctas_approach=False,
+        unload_approach=False,
+    )
+
+    assert_pandas_equals(
+        df_out.sort_values(df_out.columns.to_list()).reset_index(drop=True),
+        df_expected.sort_values(df_expected.columns.to_list()).reset_index(drop=True),
+    )
+
+
 def test_athena_to_iceberg_merge_into_ignore(path: str, path2: str, glue_database: str, glue_table: str) -> None:
     df = pd.DataFrame({"title": ["Dune", "Fargo"], "year": ["1984", "1996"], "gross": [35_000_000, 60_000_000]})
     df["title"] = df["title"].astype("string")