jroachgolf84 commented on code in PR #68136: URL: https://github.com/apache/airflow/pull/68136#discussion_r3367364175
########## airflow-core/docs/core-concepts/resumable-tasks.rst: ########## @@ -0,0 +1,187 @@ + .. 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. + +.. _concepts-resumable-tasks: + +Resumable Tasks +=============== + +.. versionadded:: 3.3.0 + +Many data engineering workflows involve submitting work to an external system and waiting for it +to complete. A Spark job, a BigQuery query, a Kubernetes batch pod, an EMR step: these are all +tasks where the real work happens outside Airflow, and the operator's job is mostly to submit, +poll, and collect the result. + +These tasks share a common failure mode. In classic operator cases, the worker slot is held for the +entire polling duration, and if the worker process is restarted or the host is preempted, the task +retries from scratch, losing all the progress made. Depending on the operator, that means the external +job is submitted again, creating a duplicate run in context of the external system. + +Airflow recommends three approaches for handling long-running external work. Understanding the trade-offs +between them helps you choose the right one for your situation. + +.. _concepts-resumable-tasks-deferrable: + +Deferrable Operators +-------------------- + +A deferrable operator pauses itself at the point where it would otherwise start polling, hands +the polling work to the Triggerer component, and releases its worker slot. When the external +condition is met, the Triggerer wakes the task and the worker resumes from where the operator +left off. + +This is the most resource-efficient option. A single Triggerer process can concurrently watch +thousands of conditions, so the rest of the worker pool stays free for other tasks. + +The trade-offs are: + +* A Triggerer component must be running. Deployments that do not include a Triggerer cannot use this pattern. +* Writing a custom deferrable operator requires implementing a separate ``Trigger`` class in Review Comment: ```suggestion * Writing a custom deferrable operator requires implementing a ``Trigger`` class in ``` ########## task-sdk/docs/resumable-job-mixin.rst: ########## @@ -0,0 +1,167 @@ + .. 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. + +.. _sdk-resumable-job-mixin: + +ResumableJobMixin +================= + +.. versionadded:: 3.3.0 + +:class:`~airflow.sdk.ResumableJobMixin` is a mixin for operators that submit long-running jobs +to an external system and poll for its completion. It makes the operator crash-safe by persisting +the external job identifier to the task state store before polling begins. If the worker is restarted Review Comment: ```suggestion the external job identifier to task state store before polling begins. If the worker is restarted ``` ########## airflow-core/docs/core-concepts/resumable-tasks.rst: ########## @@ -0,0 +1,187 @@ + .. 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. + +.. _concepts-resumable-tasks: + +Resumable Tasks +=============== + +.. versionadded:: 3.3.0 + +Many data engineering workflows involve submitting work to an external system and waiting for it +to complete. A Spark job, a BigQuery query, a Kubernetes batch pod, an EMR step: these are all +tasks where the real work happens outside Airflow, and the operator's job is mostly to submit, +poll, and collect the result. + +These tasks share a common failure mode. In classic operator cases, the worker slot is held for the +entire polling duration, and if the worker process is restarted or the host is preempted, the task +retries from scratch, losing all the progress made. Depending on the operator, that means the external +job is submitted again, creating a duplicate run in context of the external system. + +Airflow recommends three approaches for handling long-running external work. Understanding the trade-offs +between them helps you choose the right one for your situation. + +.. _concepts-resumable-tasks-deferrable: + +Deferrable Operators +-------------------- + +A deferrable operator pauses itself at the point where it would otherwise start polling, hands +the polling work to the Triggerer component, and releases its worker slot. When the external +condition is met, the Triggerer wakes the task and the worker resumes from where the operator +left off. + +This is the most resource-efficient option. A single Triggerer process can concurrently watch +thousands of conditions, so the rest of the worker pool stays free for other tasks. + +The trade-offs are: + +* A Triggerer component must be running. Deployments that do not include a Triggerer cannot use this pattern. +* Writing a custom deferrable operator requires implementing a separate ``Trigger`` class in + addition to the operator itself. +* The polling logic runs inside the Triggerer's async event loop. Blocking calls inside a + Trigger stall the entire Triggerer process. + +If a deferrable operator already exists for your use case, or your team is comfortable +implementing one, this is the recommended path considering its resource efficiency. + +For more details, see :doc:`../authoring-and-scheduling/deferring`. + +.. _concepts-resumable-tasks-resumable: + +Resumable Tasks +--------------- + +A resumable task uses the task state store to persist a checkpoint before +it would otherwise lose progress. On retry, the task reads that checkpoint and continues from +where it left off rather than starting over. + +The worker slot is held for the full duration of the task, the same as a standard synchronous +operator. The benefit is crash safety and continuity, not resource efficiency. + +Resumable tasks are useful when: + +* No deferrable operator exists for your external system and writing one is not practical. +* You want crash recovery without operating a Triggerer. +* The task processes work incrementally (for example, reading files from a list or paginating + through API results) and should be able to resume from its last completed batch. + +**General pattern** + +The task reads a checkpoint from ``task_store`` at the start, does some work, writes an updated +checkpoint, and either continues or finishes. On the next run (whether due to a retry after a +crash or a deliberate reschedule), it reads the checkpoint again and picks up from there. + +.. code-block:: python + + from airflow.sdk import dag, task + + + @dag(schedule=None) + def process_files_dag(): + + @task(retries=5) + def process_files(context=None): + task_store = context["task_store"] + files = ["a.csv", "b.csv", "c.csv", "d.csv"] + + last_processed = task_store.get("last_processed") + start_index = 0 + if last_processed is not None: + start_index = files.index(last_processed) + 1 + + for file in files[start_index:]: + # ... process the file ... + task_store.set("last_processed", file) + + process_files() + + + process_files_dag() + +This pattern works without any additional work, just plain old context. The state store is just Review Comment: ```suggestion This pattern works without any additional work, relying only on ``context``. The state store is just ``` -- This is an automated message from the Apache Git Service. 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