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### What changes were proposed in this pull request? This PR aims to support a new configuration, `spark.kubernetes.driver.service.deleteOnTermination`, to clean up `Driver Service` resource during app termination. ### Why are the changes needed? The K8s service is one of the important resources and sometimes it's controlled by quota. ``` $ k describe quota Name: service Namespace: default Resource Used Hard -------- ---- ---- services 1 3 ``` Apache Spark creates a service for driver whose lifecycle is the same with driver pod. It means a new Spark job submission fails if the number of completed Spark jobs equals the number of service quota. **BEFORE** ``` $ k get pod NAME READY STATUS RESTARTS AGE org-apache-spark-examples-sparkpi-a32c9278e7061b4d-driver 0/1 Completed 0 31m org-apache-spark-examples-sparkpi-a9f1f578e721ef62-driver 0/1 Completed 0 78s $ k get svc NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE kubernetes ClusterIP 10.96.0.1 <none> 443/TCP 80m org-apache-spark-examples-sparkpi-a32c9278e7061b4d-driver-svc ClusterIP None <none> 7078/TCP,7079/TCP,4040/TCP 31m org-apache-spark-examples-sparkpi-a9f1f578e721ef62-driver-svc ClusterIP None <none> 7078/TCP,7079/TCP,4040/TCP 80s $ k describe quota Name: service Namespace: default Resource Used Hard -------- ---- ---- services 3 3 $ bin/spark-submit... Exception in thread "main" io.fabric8.kubernetes.client.KubernetesClientException: Failure executing: POST at: https://192.168.64.50:8443/api/v1/namespaces/default/services. Message: Forbidden! User minikube doesn't have permission. services "org-apache-spark-examples-sparkpi-843f6978e722819c-driver-svc" is forbidden: exceeded quota: service, requested: services=1, used: services=3, limited: services=3. ``` **AFTER** ``` $ k get pod NAME READY STATUS RESTARTS AGE org-apache-spark-examples-sparkpi-23d5f278e77731a7-driver 0/1 Completed 0 26s org-apache-spark-examples-sparkpi-d1292278e7768ed4-driver 0/1 Completed 0 67s org-apache-spark-examples-sparkpi-e5bedf78e776ea9d-driver 0/1 Completed 0 44s $ k get svc NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE kubernetes ClusterIP 10.96.0.1 <none> 443/TCP 172m $ k describe quota Name: service Namespace: default Resource Used Hard -------- ---- ---- services 1 3 ``` ### Does this PR introduce _any_ user-facing change? Yes, this PR adds a new configuration, `spark.kubernetes.driver.service.deleteOnTermination`, and enables it by default. The change is documented at the migration guide. ### How was this patch tested? Pass the CIs. This is tested with K8s IT manually. ``` KubernetesSuite: - Run SparkPi with no resources - Run SparkPi with a very long application name. - Use SparkLauncher.NO_RESOURCE - Run SparkPi with a master URL without a scheme. - Run SparkPi with an argument. - Run SparkPi with custom labels, annotations, and environment variables. - All pods have the same service account by default - Run extraJVMOptions check on driver - Run SparkRemoteFileTest using a remote data file - Verify logging configuration is picked from the provided SPARK_CONF_DIR/log4j.properties - Run SparkPi with env and mount secrets. - Run PySpark on simple pi.py example - Run PySpark to test a pyfiles example - Run PySpark with memory customization - Run in client mode. - Start pod creation from template - PVs with local storage - Launcher client dependencies - SPARK-33615: Launcher client archives - SPARK-33748: Launcher python client respecting PYSPARK_PYTHON - SPARK-33748: Launcher python client respecting spark.pyspark.python and spark.pyspark.driver.python - Launcher python client dependencies using a zip file - Test basic decommissioning - Test basic decommissioning with shuffle cleanup - Test decommissioning with dynamic allocation & shuffle cleanups - Test decommissioning timeouts - Run SparkR on simple dataframe.R example Run completed in 19 minutes, 9 seconds. Total number of tests run: 27 Suites: completed 2, aborted 0 Tests: succeeded 27, failed 0, canceled 0, ignored 0, pending 0 All tests passed. ``` Closes #32226 from dongjoon-hyun/SPARK-35131. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
65 lines
4.8 KiB
Markdown
65 lines
4.8 KiB
Markdown
---
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layout: global
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title: "Migration Guide: Spark Core"
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displayTitle: "Migration Guide: Spark Core"
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license: |
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Licensed to the Apache Software Foundation (ASF) under one or more
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contributor license agreements. See the NOTICE file distributed with
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this work for additional information regarding copyright ownership.
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The ASF licenses this file to You under the Apache License, Version 2.0
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(the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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---
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* Table of contents
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{:toc}
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## Upgrading from Core 3.1 to 3.2
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- Since Spark 3.2, `spark.hadoopRDD.ignoreEmptySplits` is set to `true` by default which means Spark will not create empty partitions for empty input splits. To restore the behavior before Spark 3.2, you can set `spark.hadoopRDD.ignoreEmptySplits` to `false`.
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- Since Spark 3.2, `spark.eventLog.compression.codec` is set to `zstd` by default which means Spark will not fallback to use `spark.io.compression.codec` anymore.
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- Since Spark 3.2, `spark.storage.replication.proactive` is enabled by default which means Spark tries to replenish in case of the loss of cached RDD block replicas due to executor failures. To restore the behavior before Spark 3.2, you can set `spark.storage.replication.proactive` to `false`.
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- In Spark 3.2, `spark.launcher.childConectionTimeout` is deprecated (typo) though still works. Use `spark.launcher.childConnectionTimeout` instead.
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- In Spark 3.2, support for Apache Mesos as a resource manager is deprecated and will be removed in a future version.
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- In Spark 3.2, Spark will delete K8s driver service resource when the application terminates by itself. To restore the behavior before Spark 3.2, you can set `spark.kubernetes.driver.service.deleteOnTermination` to `false`.
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## Upgrading from Core 3.0 to 3.1
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- In Spark 3.0 and below, `SparkContext` can be created in executors. Since Spark 3.1, an exception will be thrown when creating `SparkContext` in executors. You can allow it by setting the configuration `spark.executor.allowSparkContext` when creating `SparkContext` in executors.
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- In Spark 3.0 and below, Spark propagated the Hadoop classpath from `yarn.application.classpath` and `mapreduce.application.classpath` into the Spark application submitted to YARN when Spark distribution is with the built-in Hadoop. Since Spark 3.1, it does not propagate anymore when the Spark distribution is with the built-in Hadoop in order to prevent the failure from the different transitive dependencies picked up from the Hadoop cluster such as Guava and Jackson. To restore the behavior before Spark 3.1, you can set `spark.yarn.populateHadoopClasspath` to `true`.
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## Upgrading from Core 2.4 to 3.0
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- The `org.apache.spark.ExecutorPlugin` interface and related configuration has been replaced with
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`org.apache.spark.api.plugin.SparkPlugin`, which adds new functionality. Plugins using the old
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interface must be modified to extend the new interfaces. Check the
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[Monitoring](monitoring.html) guide for more details.
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- Deprecated method `TaskContext.isRunningLocally` has been removed. Local execution was removed and it always has returned `false`.
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- Deprecated method `shuffleBytesWritten`, `shuffleWriteTime` and `shuffleRecordsWritten` in `ShuffleWriteMetrics` have been removed. Instead, use `bytesWritten`, `writeTime ` and `recordsWritten` respectively.
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- Deprecated method `AccumulableInfo.apply` have been removed because creating `AccumulableInfo` is disallowed.
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- Deprecated accumulator v1 APIs have been removed and please use v2 APIs instead.
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- Event log file will be written as UTF-8 encoding, and Spark History Server will replay event log files as UTF-8 encoding. Previously Spark wrote the event log file as default charset of driver JVM process, so Spark History Server of Spark 2.x is needed to read the old event log files in case of incompatible encoding.
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- A new protocol for fetching shuffle blocks is used. It's recommended that external shuffle services be upgraded when running Spark 3.0 apps. You can still use old external shuffle services by setting the configuration `spark.shuffle.useOldFetchProtocol` to `true`. Otherwise, Spark may run into errors with messages like `IllegalArgumentException: Unexpected message type: <number>`.
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- `SPARK_WORKER_INSTANCES` is deprecated in Standalone mode. It's recommended to launch multiple executors in one worker and launch one worker per node instead of launching multiple workers per node and launching one executor per worker.
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