[GitHub] [airflow] harishkrao commented on a diff in pull request #28950: Sensor for Databricks partition and table changes

["harishkrao (via GitHub)" <gi...@apache.org>]

harishkrao commented on code in PR #28950:
URL: https://github.com/apache/airflow/pull/28950#discussion_r1096915572


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airflow/providers/databricks/sensors/databricks_partition.py:
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@@ -0,0 +1,157 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+"""This module contains Databricks sensors."""
+
+from __future__ import annotations
+
+from typing import Any, Callable, Sequence
+
+from airflow.exceptions import AirflowException
+from airflow.providers.common.sql.hooks.sql import fetch_one_handler
+from airflow.providers.databricks.hooks.databricks_sql import DatabricksSqlHook
+from airflow.providers.databricks.sensors.databricks_sql import DatabricksSqlSensor
+from airflow.utils.context import Context
+
+template_fields: Sequence[str] = ("databricks_conn_id", "catalog", "schema", "table_name", "partition_name")
+
+
+class DatabricksPartitionSensor(DatabricksSqlSensor):
+    """Sensor to detect the existence of partitions in a Delta table.
+
+    :param databricks_conn_id: Reference to :ref:`Databricks
+        connection id<howto/connection:databricks>` (templated).
+    :param http_path: Optional string specifying HTTP path of Databricks SQL Endpoint or cluster.
+        If not specified, it should be either specified in the Databricks connection's extra parameters,
+        or ``sql_endpoint_name`` must be specified.
+    :param sql_endpoint_name: Optional name of Databricks SQL Endpoint.
+        If not specified, ``http_path`` must be provided as described above.
+    :param session_configuration: An optional dictionary of Spark session parameters. If not specified,
+        it could be specified in the Databricks connection's extra parameters.
+    :param http_headers: An optional list of (k, v) pairs that will be set
+        as HTTP headers on every request. (templated)
+    :param catalog: An optional initial catalog to use. Requires DBR version 9.0+ (templated)
+    :param schema: An optional initial schema to use. Requires DBR version 9.0+ (templated)
+    :param table_name: Table name to generate the SQL query.
+    :param partition_name: Partition to check.
+    :param handler: Handler for DbApiHook.run() to return results, defaults to fetch_one_handler
+    :param caller: String passed to name a hook to Databricks, defaults to "DatabricksPartitionSensor"
+    :param client_parameters: Additional parameters internal to Databricks SQL Connector parameters.
+    :param partition_operator: Comparison operator for partitions.
+    """
+
+    template_fields: Sequence[str] = (
+        "databricks_conn_id",
+        "schema",
+        "http_headers",
+        "catalog",
+        "table_name",
+        "partition_name",
+    )
+
+    def __init__(
+        self,
+        *,
+        databricks_conn_id: str = DatabricksSqlHook.default_conn_name,
+        http_path: str | None = None,
+        sql_endpoint_name: str | None = None,
+        session_configuration=None,
+        http_headers: list[tuple[str, str]] | None = None,
+        catalog: str = "",
+        schema: str = "default",
+        table_name: str = "",
+        partition_name: dict,
+        handler: Callable[[Any], Any] = fetch_one_handler,
+        caller: str = "DatabricksPartitionSensor",
+        client_parameters: dict[str, Any] | None = None,
+        partition_operator: str = "=",
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.databricks_conn_id = databricks_conn_id
+        self._http_path = http_path
+        self._sql_endpoint_name = sql_endpoint_name
+        self.session_config = session_configuration
+        self.http_headers = http_headers
+        self.catalog = catalog
+        self.schema = schema
+        self.table_name = table_name
+        self.partition_name = partition_name
+        self.caller = caller
+        self.client_parameters = client_parameters or {}
+        self.hook_params = kwargs.pop("hook_params", {})
+        self.handler = handler
+        self.partition_operator = partition_operator
+
+    def _get_hook(self) -> DatabricksSqlHook:
+        return DatabricksSqlHook(
+            self.databricks_conn_id,
+            self._http_path,
+            self._sql_endpoint_name,
+            self.session_config,
+            self.http_headers,
+            self.catalog,
+            self.schema,
+            self.caller,
+            **self.client_parameters,
+            **self.hook_params,
+        )
+
+    def _sql_sensor(self, sql):
+        hook = self._get_hook()
+        sql_result = hook.run(
+            sql,
+            handler=self.handler if self.do_xcom_push else None,
+        )
+        return sql_result
+
+    def _check_table_partitions(self) -> list:
+        if self.catalog is not None:
+            complete_table_name = str(self.catalog + "." + self.schema + "." + self.table_name)
+            self.log.debug("Table name generated from arguments: %s", complete_table_name)
+        else:
+            raise AirflowException("Catalog name not specified, aborting query execution.")
+        partition_columns = self._sql_sensor(f"describe detail {complete_table_name}")[7]
+        self.log.debug("table_info: %s", partition_columns)
+        if len(partition_columns) < 1:
+            raise AirflowException("Table %s does not have partitions", complete_table_name)
+        partitions_list = []
+        for partition_col, partition_value in self.partition_name.items():
+            if partition_col in partition_columns:
+                if isinstance(partition_value, list):
+                    partitions_list.append(f"""{partition_col} in {tuple(partition_value)}""")
+                if isinstance(partition_value, (int, float, complex)):
+                    partitions_list.append(f"""{partition_col}{self.partition_operator}{partition_value}""")
+                if isinstance(partition_value, str):
+                    partitions_list.append(
+                        f"""{partition_col}{self.partition_operator}\"{partition_value}\""""
+                    )

Review Comment:
   @alexott For this change, I am thinking of generating the SQL instead of receiving it as an argument, to prevent injections: `SELECT {expression_list, formatted — list} FROM {table_name} (generated by arguments) WHERE {filter_vals, formatted — dict} GROUP BY {group_by vals, formatted — list} ORDER BY {order_by vals, formatted — list}` where the where , group by and order by are optional, but will be validated by the formatter if it receives values for the arguments in the class.
   
   Thought of two options: Generating the SQL like the above, which has a downside of restricting the SQLs we can allow to be passed to the sensor, but it also prevents unexpected injections as a result.
   
   Another option is to breakdown the SQL received from the user and do the validation via the SQL formatter before executing it via the hook.
   
   I am leaning towards the first option, any thoughts/preferences or alternative suggestions? 



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