Implement BigQueryStreamingBufferEmptySensor to handle DML operations on streaming tables

providers/google/docs/operators/cloud/bigquery.rst

@@ -518,6 +518,34 @@ Also you can use deferrable mode in this operator if you would like to free up t
     :start-after: [START howto_sensor_bigquery_table_partition_async]
     :end-before: [END howto_sensor_bigquery_table_partition_async]
 
+Check that the BigQuery Table Streaming Buffer is empty
+""""""""""""""""""""""""""""""""""""""""""""""""""""""""
+
+To check that the BigQuery streaming buffer of a table is empty you can use
+:class:`~airflow.providers.google.cloud.sensors.bigquery.BigQueryStreamingBufferEmptySensor`.
+This sensor is useful in ETL pipelines to ensure that recent streamed data has been fully
+processed before continuing downstream tasks.
+
+.. exampleinclude:: /../../google/tests/system/google/cloud/bigquery/example_bigquery_sensors.py
+    :language: python
+    :dedent: 4
+    :start-after: [START howto_sensor_bigquery_streaming_buffer_empty]
+    :end-before: [END howto_sensor_bigquery_streaming_buffer_empty]
+
+Also you can use deferrable mode in this operator if you would like to free up the worker slots while the sensor is running.
+
+.. exampleinclude:: /../../google/tests/system/google/cloud/bigquery/example_bigquery_sensors.py
+    :language: python
+    :dedent: 4
+    :start-after: [START howto_sensor_bigquery_streaming_buffer_empty_defered]
+    :end-before: [END howto_sensor_bigquery_streaming_buffer_empty_defered]
+
+.. exampleinclude:: /../../google/tests/system/google/cloud/bigquery/example_bigquery_sensors.py
+    :language: python
+    :dedent: 4
+    :start-after: [START howto_sensor_bigquery_streaming_buffer_empty_async]
+    :end-before: [END howto_sensor_bigquery_streaming_buffer_empty_async]
+
 Reference
 ^^^^^^^^^
 

providers/google/src/airflow/providers/google/cloud/sensors/bigquery.py

@@ -28,6 +28,7 @@
 from airflow.providers.common.compat.sdk import AirflowException, BaseSensorOperator, conf
 from airflow.providers.google.cloud.hooks.bigquery import BigQueryHook
 from airflow.providers.google.cloud.triggers.bigquery import (
+    BigQueryStreamingBufferEmptyTrigger,
     BigQueryTableExistenceTrigger,
     BigQueryTablePartitionExistenceTrigger,
 )
@@ -256,3 +257,133 @@ def execute_complete(self, context: dict[str, Any], event: dict[str, str] | None
 
         message = "No event received in trigger callback"
         raise AirflowException(message)
+
+
+class BigQueryStreamingBufferEmptySensor(BaseSensorOperator):
+    """
+    Sensor for checking whether the streaming buffer in a BigQuery table is empty.
+
+    The BigQueryStreamingBufferEmptySensor waits for the streaming buffer in a specified
+    BigQuery table to be empty before proceeding. It can be used in ETL pipelines to ensure
+    that recent streamed data has been processed before continuing downstream tasks.
+
+    :param project_id: The Google Cloud project ID where the BigQuery table resides.
+    :param dataset_id: The ID of the dataset containing the BigQuery table.
+    :param table_id: The ID of the BigQuery table to monitor.
+    :param gcp_conn_id: The Airflow connection ID for GCP. Defaults to "google_cloud_default".
+    :param impersonation_chain: Optional service account to impersonate using short-term
+        credentials, or chained list of accounts required to get the access_token
+        of the last account in the list, which will be impersonated in the request.
+        If set as a string, the account must grant the originating account
+        the Service Account Token Creator IAM role.
+        If set as a sequence, the identities from the list must grant
+        Service Account Token Creator IAM role to the directly preceding identity, with first
+        account from the list granting this role to the originating account (templated).
+    :param deferrable: Run sensor in deferrable mode. Defaults to False.
+    """
+
+    template_fields: Sequence[str] = (
+        "project_id",
+        "dataset_id",
+        "table_id",
+        "impersonation_chain",
+    )
+
+    ui_color = "#f0eee4"
+
+    def __init__(
+        self,
+        *,
+        project_id: str,
+        dataset_id: str,
+        table_id: str,
+        gcp_conn_id: str = "google_cloud_default",
+        impersonation_chain: str | Sequence[str] | None = None,
+        deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
+        **kwargs,
+    ) -> None:
+        if deferrable and "poke_interval" not in kwargs:
+            # TODO: Remove once deprecated
+            if "polling_interval" in kwargs:
+                kwargs["poke_interval"] = kwargs["polling_interval"]
+                warnings.warn(
+                    "Argument `polling_interval` is deprecated and will be removed "
+                    "in a future release.  Please use `poke_interval` instead.",
+                    AirflowProviderDeprecationWarning,
+                    stacklevel=2,
+                )
+            else:
+                kwargs["poke_interval"] = 30
+
+        super().__init__(**kwargs)
+
+        self.project_id = project_id
+        self.dataset_id = dataset_id
+        self.table_id = table_id
+        self.gcp_conn_id = gcp_conn_id
+        self.impersonation_chain = impersonation_chain
+        self.deferrable = deferrable
+
+    def execute(self, context: Context) -> None:
+        """Airflow runs this method on the worker and defers using the trigger if deferrable."""
+        if not self.deferrable:
+            super().execute(context)
+        else:
+            if not self.poke(context=context):
+                self.defer(
+                    timeout=timedelta(seconds=self.timeout),
+                    trigger=BigQueryStreamingBufferEmptyTrigger(
+                        project_id=self.project_id,
+                        dataset_id=self.dataset_id,
+                        table_id=self.table_id,
+                        poll_interval=self.poke_interval,
+                        gcp_conn_id=self.gcp_conn_id,
+                        hook_params={
+                            "impersonation_chain": self.impersonation_chain,
+                        },
+                    ),
+                    method_name="execute_complete",
+                )
+
+    def execute_complete(self, context: dict[str, Any], event: dict[str, str] | None = None) -> str:
+        """
+        Act as a callback for when the trigger fires - returns immediately.
+
+        Relies on trigger to throw an exception, otherwise it assumes execution was successful.
+        """
+        table_uri = f"{self.project_id}:{self.dataset_id}.{self.table_id}"
+        self.log.info("Checking streaming buffer state for table: %s", table_uri)
+        if event:
+            if event["status"] == "success":
+                return event["message"]
+            raise AirflowException(event["message"])
+
+        message = "No event received in trigger callback"
+        raise AirflowException(message)
+
+    def poke(self, context: Context) -> bool:
+        """
+        Check if the BigQuery streaming buffer is empty for the specified table.
+
+        This method periodically checks the status of the BigQuery table's streaming buffer
+        to determine if it is empty. It is useful for ensuring that recent streamed data
+        has been fully processed before continuing with downstream tasks.
+        """
+        table_uri = f"{self.project_id}:{self.dataset_id}.{self.table_id}"
+        self.log.info("Checking streaming buffer state for table: %s", table_uri)
+
+        hook = BigQueryHook(
+            gcp_conn_id=self.gcp_conn_id,
+            impersonation_chain=self.impersonation_chain,
+        )
+        try:
+            client = hook.get_client(project_id=self.project_id)
+            table_ref = f"{self.project_id}.{self.dataset_id}.{self.table_id}"
+            table = client.get_table(table_ref)
+            return table.streaming_buffer is None
+        except Exception as err:
+            if "not found" in str(err):
+                raise AirflowException(
+                    f"Table {self.project_id}.{self.dataset_id}.{self.table_id} not found"
+                ) from err
+            raise err

providers/google/src/airflow/providers/google/cloud/triggers/bigquery.py

@@ -806,3 +806,158 @@ async def _partition_exists(self, hook: BigQueryAsyncHook, job_id: str | None, p
         if records:
             records = [row[0] for row in records]
             return self.partition_id in records
+
+
+class BigQueryStreamingBufferEmptyTrigger(BaseTrigger):
+    """
+    Trigger that periodically checks if a BigQuery table's streaming buffer is empty.
+
+    This trigger continuously polls a BigQuery table to determine if its streaming buffer
+    has been fully processed and is now empty. It's particularly useful before running
+    DML operations (UPDATE, DELETE, MERGE) on tables populated via streaming inserts.
+
+    :param project_id: The Google Cloud Project in which to look for the table.
+    :param dataset_id: The dataset ID of the table to check.
+    :param table_id: The table ID of the table to check.
+    :param gcp_conn_id: Reference to Google Cloud connection ID.
+    :param hook_params: Additional parameters for hook initialization.
+    :param poll_interval: Polling period in seconds to check the streaming buffer status.
+    :param impersonation_chain: Optional service account to impersonate using short-term
+        credentials, or chained list of accounts required to get the access_token
+        of the last account in the list, which will be impersonated in the request.
+        If set as a string, the account must grant the originating account
+        the Service Account Token Creator IAM role.
+        If set as a sequence, the identities from the list must grant
+        Service Account Token Creator IAM role to the directly preceding identity, with first
+        account from the list granting this role to the originating account.
+    """
+
+    def __init__(
+        self,
+        project_id: str,
+        dataset_id: str,
+        table_id: str,
+        gcp_conn_id: str,
+        hook_params: dict[str, Any],
+        poll_interval: float = 30.0,
+        impersonation_chain: str | Sequence[str] | None = None,
+    ):
+        super().__init__()
+        self.project_id = project_id
+        self.dataset_id = dataset_id
+        self.table_id = table_id
+        self.gcp_conn_id = gcp_conn_id
+        self.poll_interval = poll_interval
+        self.hook_params = hook_params
+        self.impersonation_chain = impersonation_chain
+
+    def serialize(self) -> tuple[str, dict[str, Any]]:
+        """Serialize BigQueryStreamingBufferEmptyTrigger arguments and classpath."""
+        return (
+            "airflow.providers.google.cloud.triggers.bigquery.BigQueryStreamingBufferEmptyTrigger",
+            {
+                "project_id": self.project_id,
+                "dataset_id": self.dataset_id,
+                "table_id": self.table_id,
+                "gcp_conn_id": self.gcp_conn_id,
+                "poll_interval": self.poll_interval,
+                "hook_params": self.hook_params,
+                "impersonation_chain": self.impersonation_chain,
+            },
+        )
+
+    def _get_async_hook(self) -> BigQueryTableAsyncHook:
+        """Get the async hook for BigQuery table operations."""
+        return BigQueryTableAsyncHook(
+            gcp_conn_id=self.gcp_conn_id,
+            impersonation_chain=self.impersonation_chain,
+        )
+
+    async def run(self) -> AsyncIterator[TriggerEvent]:
+        """
+        Continuously check if the streaming buffer is empty.
+
+        Yields a TriggerEvent when the streaming buffer becomes empty or if an error occurs.
+        """
+        try:
+            hook = self._get_async_hook()
+            while True:
+                self.log.info(
+                    "Checking streaming buffer for table %s.%s.%s",
+                    self.project_id,
+                    self.dataset_id,
+                    self.table_id,
+                )
+
+                is_buffer_empty = await self._is_streaming_buffer_empty(
+                    hook=hook,
+                    project_id=self.project_id,
+                    dataset_id=self.dataset_id,
+                    table_id=self.table_id,
+                )
+
+                if is_buffer_empty:
+                    table_uri = f"{self.project_id}:{self.dataset_id}.{self.table_id}"
+                    message = f"Streaming buffer is empty for table: {table_uri}"
+                    self.log.info(message)
+                    yield TriggerEvent(
+                        {
+                            "status": "success",
+                            "message": message,
+                        }
+                    )
+                    return
+                else:
+                    self.log.info(
+                        "Streaming buffer still has data. Sleeping for %s seconds.",
+                        self.poll_interval,
+                    )
+                    await asyncio.sleep(self.poll_interval)
+
+        except Exception as e:
+            self.log.exception(
+                "Exception occurred while checking streaming buffer for table %s.%s.%s",
+                self.project_id,
+                self.dataset_id,
+                self.table_id,
+            )
+            yield TriggerEvent({"status": "error", "message": str(e)})
+
+    async def _is_streaming_buffer_empty(
+        self,
+        hook: BigQueryTableAsyncHook,
+        project_id: str,
+        dataset_id: str,
+        table_id: str,
+    ) -> bool:
+        """
+        Check if the streaming buffer is empty for the specified table.
+
+        :param hook: BigQueryTableAsyncHook instance for async operations.
+        :param project_id: The Google Cloud Project ID.
+        :param dataset_id: The dataset ID containing the table.
+        :param table_id: The table ID to check.
+        :return: True if streaming buffer is empty or doesn't exist, False otherwise.
+        """
+        async with ClientSession() as session:
+            try:
+                client = await hook.get_table_client(
+                    dataset=dataset_id,
+                    table_id=table_id,
+                    project_id=project_id,
+                    session=session,
+                )
+                response = await client.get()
+
+                if not response:
+                    # Table doesn't exist
+                    raise AirflowException(f"Table {project_id}.{dataset_id}.{table_id} does not exist")
+
+                # Check if streamingBuffer exists in the response
+                streaming_buffer = response.get("streamingBuffer")
+                return streaming_buffer is None
+
+            except ClientResponseError as err:
+                if err.status == 404:
+                    raise AirflowException(f"Table {project_id}.{dataset_id}.{table_id} not found") from err
+                raise err