jason810496 commented on code in PR #61681: URL: https://github.com/apache/airflow/pull/61681#discussion_r2877332859
########## providers/apache/spark/src/airflow/providers/apache/spark/operators/spark_pipelines.py: ########## @@ -0,0 +1,151 @@ +# +# 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. +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any + +from airflow.providers.apache.spark.hooks.spark_pipelines import SparkPipelinesHook +from airflow.providers.common.compat.openlineage.utils.spark import ( + inject_parent_job_information_into_spark_properties, + inject_transport_information_into_spark_properties, +) +from airflow.providers.common.compat.sdk import BaseOperator, conf + +if TYPE_CHECKING: + from airflow.providers.common.compat.sdk import Context + + +class SparkPipelinesOperator(BaseOperator): + """ + Execute Spark Declarative Pipelines using the spark-pipelines CLI. + + This operator wraps the spark-pipelines binary to execute declarative data pipelines. + It supports running pipelines, dry-runs for validation, and initializing new pipeline projects. + + .. seealso:: + For more information on Spark Declarative Pipelines, see the guide: + https://spark.apache.org/docs/latest/declarative-pipelines-programming-guide.html + + :param pipeline_spec: Path to the pipeline specification file (YAML). (templated) + :param pipeline_command: The spark-pipelines command to execute ('run', 'dry-run'). Default is 'run'. + :param conf: Arbitrary Spark configuration properties (templated) + :param conn_id: The :ref:`spark connection id <howto/connection:spark-submit>` as configured + in Airflow administration. When an invalid connection_id is supplied, it will default to yarn. + :param num_executors: Number of executors to launch + :param executor_cores: Number of cores per executor (Default: 2) + :param executor_memory: Memory per executor (e.g. 1000M, 2G) (Default: 1G) + :param driver_memory: Memory allocated to the driver (e.g. 1000M, 2G) (Default: 1G) + :param verbose: Whether to pass the verbose flag to spark-pipelines process for debugging + :param env_vars: Environment variables for spark-pipelines. (templated) + :param deploy_mode: Whether to deploy your driver on the worker nodes (cluster) or locally as a client. + :param yarn_queue: The name of the YARN queue to which the application is submitted. + :param keytab: Full path to the file that contains the keytab (templated) + :param principal: The name of the kerberos principal used for keytab (templated) + :param openlineage_inject_parent_job_info: Whether to inject OpenLineage parent job information + :param openlineage_inject_transport_info: Whether to inject OpenLineage transport information + """ + + template_fields: Sequence[str] = ( + "pipeline_spec", + "conf", + "env_vars", + "keytab", + "principal", + ) + + def __init__( + self, + *, + pipeline_spec: str | None = None, + pipeline_command: str = "run", + conf: dict[Any, Any] | None = None, + conn_id: str = "spark_default", + num_executors: int | None = None, + executor_cores: int | None = None, + executor_memory: str | None = None, + driver_memory: str | None = None, + verbose: bool = False, + env_vars: dict[str, Any] | None = None, + deploy_mode: str | None = None, + yarn_queue: str | None = None, + keytab: str | None = None, + principal: str | None = None, + openlineage_inject_parent_job_info: bool = conf.getboolean( + "openlineage", "spark_inject_parent_job_info", fallback=False + ), + openlineage_inject_transport_info: bool = conf.getboolean( + "openlineage", "spark_inject_transport_info", fallback=False + ), + **kwargs: Any, + ) -> None: + super().__init__(**kwargs) + self.pipeline_spec = pipeline_spec + self.pipeline_command = pipeline_command + self.conf = conf + self.num_executors = num_executors + self.executor_cores = executor_cores + self.executor_memory = executor_memory + self.driver_memory = driver_memory + self.verbose = verbose + self.env_vars = env_vars + self.deploy_mode = deploy_mode + self.yarn_queue = yarn_queue + self.keytab = keytab + self.principal = principal + self._hook: SparkPipelinesHook | None = None + self._conn_id = conn_id + self._openlineage_inject_parent_job_info = openlineage_inject_parent_job_info + self._openlineage_inject_transport_info = openlineage_inject_transport_info + + def execute(self, context: Context) -> None: + """Execute the SparkPipelinesHook to run the specified pipeline command.""" + self.conf = self.conf or {} + if self._openlineage_inject_parent_job_info: + self.log.debug("Injecting OpenLineage parent job information into Spark properties.") + self.conf = inject_parent_job_information_into_spark_properties(self.conf, context) + if self._openlineage_inject_transport_info: + self.log.debug("Injecting OpenLineage transport information into Spark properties.") + self.conf = inject_transport_information_into_spark_properties(self.conf, context) + + if self._hook is None: + self._hook = self._get_hook() + self._hook.submit_pipeline() + + def on_kill(self) -> None: + if self._hook is None: + self._hook = self._get_hook() + self._hook.on_kill() Review Comment: Then all the `self._hook` can be replaced with `hook` cached property. ```suggestion self.hook.submit_pipeline() def on_kill(self) -> None: self.hook.on_kill() ``` ########## providers/apache/spark/src/airflow/providers/apache/spark/hooks/spark_pipelines.py: ########## @@ -0,0 +1,108 @@ +# +# 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. +from __future__ import annotations + +import subprocess +from typing import Any + +from airflow.providers.apache.spark.hooks.spark_submit import SparkSubmitHook +from airflow.providers.common.compat.sdk import AirflowException + + +class SparkPipelinesHook(SparkSubmitHook): + """ + Hook for interacting with Spark Declarative Pipelines via the spark-pipelines CLI. + + Extends SparkSubmitHook to leverage existing connection management while providing + pipeline-specific functionality. + + :param pipeline_spec: Path to the pipeline specification file (YAML) + :param pipeline_command: The spark-pipelines command to run ('run', 'dry-run') + """ + + def __init__( + self, + pipeline_spec: str | None = None, + pipeline_command: str = "run", + **kwargs: Any, + ) -> None: + super().__init__(**kwargs) + self.pipeline_spec = pipeline_spec + self.pipeline_command = pipeline_command + + if pipeline_command not in ["run", "dry-run"]: + raise ValueError(f"Invalid pipeline command: {pipeline_command}. Must be 'run' or 'dry-run'") + + def _get_spark_binary_path(self) -> list[str]: + return ["spark-pipelines"] + + def _build_spark_pipelines_command(self) -> list[str]: + """ + Construct the spark-pipelines command to execute. + + :return: full command to be executed + """ + # Start with spark-pipelines binary and command + connection_cmd = self._get_spark_binary_path() + connection_cmd.append(self.pipeline_command) + + # Add pipeline spec if provided + if self.pipeline_spec: + connection_cmd.extend(["--spec", self.pipeline_spec]) + + # Reuse parent's common spark argument building logic + connection_cmd.extend(self._build_spark_common_args()) + + self.log.info("Spark-Pipelines cmd: %s", self._mask_cmd(connection_cmd)) + return connection_cmd + + def submit_pipeline(self, **kwargs: Any) -> None: + """ + Execute the spark-pipelines command. + + :param kwargs: extra arguments to Popen (see subprocess.Popen) + """ + pipelines_cmd = self._build_spark_pipelines_command() + + if self._env: + import os + + env = os.environ.copy() + env.update(self._env) + kwargs["env"] = env + + self._submit_sp = subprocess.Popen( + pipelines_cmd, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + bufsize=-1, + universal_newlines=True, + **kwargs, + ) + + self._process_spark_submit_log(iter(self._submit_sp.stdout)) + returncode = self._submit_sp.wait() + + if returncode: + raise AirflowException( + f"Cannot execute: {self._mask_cmd(pipelines_cmd)}. Error code is: {returncode}." + ) Review Comment: We're avoiding the too board `AirflowException`. It would be nice to add the exception subclass, thanks! ########## providers/apache/spark/src/airflow/providers/apache/spark/hooks/spark_pipelines.py: ########## @@ -0,0 +1,108 @@ +# +# 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. +from __future__ import annotations + +import subprocess +from typing import Any + +from airflow.providers.apache.spark.hooks.spark_submit import SparkSubmitHook +from airflow.providers.common.compat.sdk import AirflowException + + +class SparkPipelinesHook(SparkSubmitHook): + """ + Hook for interacting with Spark Declarative Pipelines via the spark-pipelines CLI. + + Extends SparkSubmitHook to leverage existing connection management while providing + pipeline-specific functionality. + + :param pipeline_spec: Path to the pipeline specification file (YAML) + :param pipeline_command: The spark-pipelines command to run ('run', 'dry-run') + """ + + def __init__( + self, + pipeline_spec: str | None = None, + pipeline_command: str = "run", + **kwargs: Any, + ) -> None: + super().__init__(**kwargs) + self.pipeline_spec = pipeline_spec + self.pipeline_command = pipeline_command + + if pipeline_command not in ["run", "dry-run"]: + raise ValueError(f"Invalid pipeline command: {pipeline_command}. Must be 'run' or 'dry-run'") + + def _get_spark_binary_path(self) -> list[str]: Review Comment: Non-blocking nit: How about defining as property? ```suggestion @property def _spark_binary_path(self) -> list[str]: ``` EDIT: Feel free to leave it as is, as the `SparkSubmitHook._build_spark_submit_command` is still referencing `_get_spark_binary_path` method. ########## providers/apache/spark/src/airflow/providers/apache/spark/operators/spark_pipelines.py: ########## @@ -0,0 +1,151 @@ +# +# 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. +from __future__ import annotations + +from collections.abc import Sequence +from typing import TYPE_CHECKING, Any + +from airflow.providers.apache.spark.hooks.spark_pipelines import SparkPipelinesHook +from airflow.providers.common.compat.openlineage.utils.spark import ( + inject_parent_job_information_into_spark_properties, + inject_transport_information_into_spark_properties, +) +from airflow.providers.common.compat.sdk import BaseOperator, conf + +if TYPE_CHECKING: + from airflow.providers.common.compat.sdk import Context + + +class SparkPipelinesOperator(BaseOperator): + """ + Execute Spark Declarative Pipelines using the spark-pipelines CLI. + + This operator wraps the spark-pipelines binary to execute declarative data pipelines. + It supports running pipelines, dry-runs for validation, and initializing new pipeline projects. + + .. seealso:: + For more information on Spark Declarative Pipelines, see the guide: + https://spark.apache.org/docs/latest/declarative-pipelines-programming-guide.html + + :param pipeline_spec: Path to the pipeline specification file (YAML). (templated) + :param pipeline_command: The spark-pipelines command to execute ('run', 'dry-run'). Default is 'run'. + :param conf: Arbitrary Spark configuration properties (templated) + :param conn_id: The :ref:`spark connection id <howto/connection:spark-submit>` as configured + in Airflow administration. When an invalid connection_id is supplied, it will default to yarn. + :param num_executors: Number of executors to launch + :param executor_cores: Number of cores per executor (Default: 2) + :param executor_memory: Memory per executor (e.g. 1000M, 2G) (Default: 1G) + :param driver_memory: Memory allocated to the driver (e.g. 1000M, 2G) (Default: 1G) + :param verbose: Whether to pass the verbose flag to spark-pipelines process for debugging + :param env_vars: Environment variables for spark-pipelines. (templated) + :param deploy_mode: Whether to deploy your driver on the worker nodes (cluster) or locally as a client. + :param yarn_queue: The name of the YARN queue to which the application is submitted. + :param keytab: Full path to the file that contains the keytab (templated) + :param principal: The name of the kerberos principal used for keytab (templated) + :param openlineage_inject_parent_job_info: Whether to inject OpenLineage parent job information + :param openlineage_inject_transport_info: Whether to inject OpenLineage transport information + """ + + template_fields: Sequence[str] = ( + "pipeline_spec", + "conf", + "env_vars", + "keytab", + "principal", + ) + + def __init__( + self, + *, + pipeline_spec: str | None = None, + pipeline_command: str = "run", + conf: dict[Any, Any] | None = None, + conn_id: str = "spark_default", + num_executors: int | None = None, + executor_cores: int | None = None, + executor_memory: str | None = None, + driver_memory: str | None = None, + verbose: bool = False, + env_vars: dict[str, Any] | None = None, + deploy_mode: str | None = None, + yarn_queue: str | None = None, + keytab: str | None = None, + principal: str | None = None, + openlineage_inject_parent_job_info: bool = conf.getboolean( + "openlineage", "spark_inject_parent_job_info", fallback=False + ), + openlineage_inject_transport_info: bool = conf.getboolean( + "openlineage", "spark_inject_transport_info", fallback=False + ), + **kwargs: Any, + ) -> None: + super().__init__(**kwargs) + self.pipeline_spec = pipeline_spec + self.pipeline_command = pipeline_command + self.conf = conf + self.num_executors = num_executors + self.executor_cores = executor_cores + self.executor_memory = executor_memory + self.driver_memory = driver_memory + self.verbose = verbose + self.env_vars = env_vars + self.deploy_mode = deploy_mode + self.yarn_queue = yarn_queue + self.keytab = keytab + self.principal = principal + self._hook: SparkPipelinesHook | None = None + self._conn_id = conn_id + self._openlineage_inject_parent_job_info = openlineage_inject_parent_job_info + self._openlineage_inject_transport_info = openlineage_inject_transport_info + + def execute(self, context: Context) -> None: + """Execute the SparkPipelinesHook to run the specified pipeline command.""" + self.conf = self.conf or {} + if self._openlineage_inject_parent_job_info: + self.log.debug("Injecting OpenLineage parent job information into Spark properties.") + self.conf = inject_parent_job_information_into_spark_properties(self.conf, context) + if self._openlineage_inject_transport_info: + self.log.debug("Injecting OpenLineage transport information into Spark properties.") + self.conf = inject_transport_information_into_spark_properties(self.conf, context) + + if self._hook is None: + self._hook = self._get_hook() + self._hook.submit_pipeline() + + def on_kill(self) -> None: + if self._hook is None: + self._hook = self._get_hook() + self._hook.on_kill() + + def _get_hook(self) -> SparkPipelinesHook: Review Comment: ```suggestion @cached_property def hook(self) -> SparkPipelinesHook: ``` -- This is an automated message from the Apache Git Service. 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