jigneshjain25 commented on code in PR #74753:
URL: https://github.com/apache/airflow/pull/74753#discussion_r4327415162
##########
providers/google/docs/operators/cloud/dataproc.rst:
##########
@@ -186,6 +186,44 @@ You can use deferrable mode for this action in order to
run the operator asynchr
:start-after: [START how_to_cloud_dataproc_create_cluster_operator_async]
:end-before: [END how_to_cloud_dataproc_create_cluster_operator_async]
+High Obtainability (HO) mode clusters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+High Obtainability (HO) mode enables creating Dataproc clusters using abstract
hardware requirements
Review Comment:
Added direct links in `dataproc.rst` to the official Google Cloud
documentation for [Dataproc High Obtainability
mode](https://cloud.google.com/dataproc/docs/concepts/compute/high-obtainability)
and the [FlexibleInstanceGroupConfig API
reference](https://cloud.google.com/dataproc/docs/reference/rest/v1/ClusterConfig#FlexibleInstanceGroupConfig).
##########
providers/google/pyproject.toml:
##########
@@ -104,7 +104,7 @@ dependencies = [
"google-cloud-dataflow-client>=0.12.0",
"google-cloud-dataform>=0.10.0",
"google-cloud-dataplex>=2.18.0",
- "google-cloud-dataproc>=5.27.0",
+ "google-cloud-dataproc>=5.32.0",
Review Comment:
Updated `uv.lock` with the `google-cloud-dataproc>=5.32.0` dependency bump
and exact release package metadata and PyPI hashes.
##########
providers/google/tests/system/google/cloud/dataproc/example_dataproc_cluster_high_obtainability.py:
##########
@@ -0,0 +1,172 @@
+#
+# 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.
+"""Example Airflow DAG for a Dataproc High Obtainability (HO) mode cluster.
+
+In High Obtainability mode node groups are described by abstract requirements
+(``FlexibleInstanceGroupConfig``: number of instances, core count and hardware
+class) instead of concrete machine types. Dataproc resolves them into a ranked
+list of widely available machine families to minimize stockouts. HO clusters
+are always auto-zoned.
+
+The DAG creates an HO cluster, scales its primary workers through the
+authoritative ``config.worker_flexible_config.num_instances`` update path,
+scales the secondary workers through the legacy
+``config.secondary_worker_config.num_instances`` compatibility path (which the
+API keeps accepting for HO clusters), submits a Spark job and deletes the
+cluster.
+"""
+
+from __future__ import annotations
+
+import os
+from datetime import datetime
+
+from airflow.models.dag import DAG
+from airflow.providers.google.cloud.operators.dataproc import (
+ DataprocCreateClusterOperator,
+ DataprocDeleteClusterOperator,
+ DataprocSubmitJobOperator,
+ DataprocUpdateClusterOperator,
+ FlexibleInstanceGroupConfig,
+ flexible_scale_cluster_update,
+)
+
+try:
+ from airflow.sdk import TriggerRule
+except ImportError:
+ # Compatibility for Airflow < 3.1
+ from airflow.utils.trigger_rule import TriggerRule # type:
ignore[no-redef,attr-defined]
+
+from system.google import DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
+
+ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
+DAG_ID = "dataproc_cluster_high_obtainability"
+PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT") or
DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
+
+CLUSTER_NAME_BASE = f"cluster-{DAG_ID}".replace("_", "-")
+CLUSTER_NAME_FULL = CLUSTER_NAME_BASE + f"-{ENV_ID}".replace("_", "-")
+CLUSTER_NAME = CLUSTER_NAME_BASE if len(CLUSTER_NAME_FULL) >= 33 else
CLUSTER_NAME_FULL
+REGION = "us-central1"
+
+# [START how_to_cloud_dataproc_create_ho_cluster_config]
+# Note: no machine types, disk types or zone are specified. HO clusters are
+# cluster-wide: master and primary workers must both use a flexible config.
+HO_CLUSTER_CONFIG = {
+ "master_flexible_config": FlexibleInstanceGroupConfig(
+ num_instances=1, core_count=4, hardware_class="STANDARD"
+ ),
+ # A plain dict with the same keys is accepted as well.
+ "worker_flexible_config": {
+ "num_instances": 2,
+ "core_count": 8,
+ "hardware_class": "PERFORMANCE",
+ "boot_disk_size_gb": 500,
+ },
+ "secondary_worker_flexible_config": {
+ "num_instances": 1,
+ "core_count": 8,
+ "hardware_class": "PERFORMANCE",
+ },
+ "software_config": {
+ "image_version": "2.2",
+ },
+}
+# [END how_to_cloud_dataproc_create_ho_cluster_config]
+
+SPARK_PI_JOB = {
+ "reference": {"project_id": PROJECT_ID},
+ "placement": {"cluster_name": CLUSTER_NAME},
+ "spark_job": {
+ "main_class": "org.apache.spark.examples.SparkPi",
+ "jar_file_uris":
["file:///usr/lib/spark/examples/jars/spark-examples.jar"],
+ "args": ["10"],
+ },
+}
+
+with DAG(
Review Comment:
The example DAG `dataproc_cluster_high_obtainability` was executed and
verified end-to-end on GCP test project `dataproc-test-jnj` in `us-central1`.
All tasks completed with `SUCCESS`:
- `create_ho_cluster`: SUCCESS (Cluster created with 1 master, 4 primary
workers, and 1 secondary worker using 2 cores and 32GB pd-standard boot disk
per instance)
- `scale_ho_cluster`: SUCCESS (Updated cluster in a single call with
`config.worker_flexible_config.num_instances: 3` and
`config.secondary_worker_flexible_config.num_instances: 0`)
- `spark_pi`: SUCCESS (SparkPi job completed successfully on the scaled
cluster, outputting calculated Pi value)
- `delete_ho_cluster`: SUCCESS (Cluster cleanly torn down)
##########
providers/google/tests/system/google/cloud/dataproc/example_dataproc_cluster_high_obtainability.py:
##########
@@ -0,0 +1,172 @@
+#
+# 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.
+"""Example Airflow DAG for a Dataproc High Obtainability (HO) mode cluster.
+
+In High Obtainability mode node groups are described by abstract requirements
+(``FlexibleInstanceGroupConfig``: number of instances, core count and hardware
+class) instead of concrete machine types. Dataproc resolves them into a ranked
+list of widely available machine families to minimize stockouts. HO clusters
+are always auto-zoned.
+
+The DAG creates an HO cluster, scales its primary workers through the
+authoritative ``config.worker_flexible_config.num_instances`` update path,
+scales the secondary workers through the legacy
+``config.secondary_worker_config.num_instances`` compatibility path (which the
+API keeps accepting for HO clusters), submits a Spark job and deletes the
+cluster.
+"""
+
+from __future__ import annotations
+
+import os
+from datetime import datetime
+
+from airflow.models.dag import DAG
+from airflow.providers.google.cloud.operators.dataproc import (
+ DataprocCreateClusterOperator,
+ DataprocDeleteClusterOperator,
+ DataprocSubmitJobOperator,
+ DataprocUpdateClusterOperator,
+ FlexibleInstanceGroupConfig,
+ flexible_scale_cluster_update,
+)
+
+try:
+ from airflow.sdk import TriggerRule
+except ImportError:
+ # Compatibility for Airflow < 3.1
+ from airflow.utils.trigger_rule import TriggerRule # type:
ignore[no-redef,attr-defined]
+
+from system.google import DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
+
+ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
+DAG_ID = "dataproc_cluster_high_obtainability"
+PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT") or
DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
+
+CLUSTER_NAME_BASE = f"cluster-{DAG_ID}".replace("_", "-")
+CLUSTER_NAME_FULL = CLUSTER_NAME_BASE + f"-{ENV_ID}".replace("_", "-")
+CLUSTER_NAME = CLUSTER_NAME_BASE if len(CLUSTER_NAME_FULL) >= 33 else
CLUSTER_NAME_FULL
+REGION = "us-central1"
+
+# [START how_to_cloud_dataproc_create_ho_cluster_config]
+# Note: no machine types, disk types or zone are specified. HO clusters are
+# cluster-wide: master and primary workers must both use a flexible config.
+HO_CLUSTER_CONFIG = {
Review Comment:
Updated the example DAG to minimize resource consumption:
- Configured 2 cores and 32GB boot disk per node across master, primary
workers, and secondary workers.
- Converted to plain dictionaries instead of custom classes.
- Consolidated the scaling into a single `DataprocUpdateClusterOperator`
call scaling `worker_flexible_config` to 3 and
`secondary_worker_flexible_config` to 0 directly via `cluster` and
`update_mask` without the helper function.
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