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[HUDI-1764] Add Hudi-CLI support for clustering (#2773)

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* tmp base

* update

* update unit test

* update

* update

* update CLI parameters

* linting

* update doSchedule in HoodieClusteringJob

* update

* update diff according to comments
This commit is contained in:
Jintao Guan
2021-04-20 09:46:42 -07:00
committed by GitHub
parent d6d52c6063
commit 3253079507
3 changed files with 153 additions and 2 deletions
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107
hudi-cli/src/main/java/org/apache/hudi/cli/commands/ClusteringCommand.java Normal file
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@@ -0,0 +1,107 @@
/*
* 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.
*/
package org.apache.hudi.cli.commands;
import org.apache.hudi.cli.HoodieCLI;
import org.apache.hudi.cli.commands.SparkMain.SparkCommand;
import org.apache.hudi.cli.utils.InputStreamConsumer;
import org.apache.hudi.cli.utils.SparkUtil;
import org.apache.hudi.common.table.HoodieTableMetaClient;
import org.apache.hudi.common.table.timeline.HoodieActiveTimeline;
import org.apache.hudi.utilities.UtilHelpers;
import org.apache.log4j.LogManager;
import org.apache.log4j.Logger;
import org.apache.spark.launcher.SparkLauncher;
import org.apache.spark.util.Utils;
import org.springframework.shell.core.CommandMarker;
import org.springframework.shell.core.annotation.CliCommand;
import org.springframework.shell.core.annotation.CliOption;
import org.springframework.stereotype.Component;
import scala.collection.JavaConverters;
@Component
public class ClusteringCommand implements CommandMarker {
private static final Logger LOG = LogManager.getLogger(ClusteringCommand.class);
@CliCommand(value = "clustering schedule", help = "Schedule Clustering")
public String scheduleClustering(
@CliOption(key = "sparkMemory", help = "Spark executor memory",
unspecifiedDefaultValue = "1G") final String sparkMemory,
@CliOption(key = "propsFilePath", help = "path to properties file on localfs or dfs with configurations for hoodie client for clustering",
unspecifiedDefaultValue = "") final String propsFilePath,
@CliOption(key = "hoodieConfigs", help = "Any configuration that can be set in the properties file can be passed here in the form of an array",
unspecifiedDefaultValue = "") final String[] configs) throws Exception {
HoodieTableMetaClient client = HoodieCLI.getTableMetaClient();
boolean initialized = HoodieCLI.initConf();
HoodieCLI.initFS(initialized);
String sparkPropertiesPath =
Utils.getDefaultPropertiesFile(JavaConverters.mapAsScalaMapConverter(System.getenv()).asScala());
SparkLauncher sparkLauncher = SparkUtil.initLauncher(sparkPropertiesPath);
// First get a clustering instant time and pass it to spark launcher for scheduling clustering
String clusteringInstantTime = HoodieActiveTimeline.createNewInstantTime();
sparkLauncher.addAppArgs(SparkCommand.CLUSTERING_SCHEDULE.toString(), client.getBasePath(),
client.getTableConfig().getTableName(), clusteringInstantTime, sparkMemory, propsFilePath);
UtilHelpers.validateAndAddProperties(configs, sparkLauncher);
Process process = sparkLauncher.launch();
InputStreamConsumer.captureOutput(process);
int exitCode = process.waitFor();
if (exitCode != 0) {
return "Failed to schedule clustering for " + clusteringInstantTime;
}
return "Succeeded to schedule clustering for " + clusteringInstantTime;
}
@CliCommand(value = "clustering run", help = "Run Clustering")
public String runClustering(
@CliOption(key = "parallelism", help = "Parallelism for hoodie clustering",
unspecifiedDefaultValue = "1") final String parallelism,
@CliOption(key = "sparkMemory", help = "Spark executor memory",
unspecifiedDefaultValue = "4G") final String sparkMemory,
@CliOption(key = "retry", help = "Number of retries",
unspecifiedDefaultValue = "1") final String retry,
@CliOption(key = "clusteringInstant", help = "Clustering instant time",
mandatory = true) final String clusteringInstantTime,
@CliOption(key = "propsFilePath", help = "path to properties file on localfs or dfs with configurations for hoodie client for compacting",
unspecifiedDefaultValue = "") final String propsFilePath,
@CliOption(key = "hoodieConfigs", help = "Any configuration that can be set in the properties file can be passed here in the form of an array",
unspecifiedDefaultValue = "") final String[] configs
) throws Exception {
HoodieTableMetaClient client = HoodieCLI.getTableMetaClient();
boolean initialized = HoodieCLI.initConf();
HoodieCLI.initFS(initialized);
String sparkPropertiesPath =
Utils.getDefaultPropertiesFile(JavaConverters.mapAsScalaMapConverter(System.getenv()).asScala());
SparkLauncher sparkLauncher = SparkUtil.initLauncher(sparkPropertiesPath);
sparkLauncher.addAppArgs(SparkCommand.CLUSTERING_RUN.toString(), client.getBasePath(),
client.getTableConfig().getTableName(), clusteringInstantTime, parallelism, sparkMemory, retry, propsFilePath);
UtilHelpers.validateAndAddProperties(configs, sparkLauncher);
Process process = sparkLauncher.launch();
InputStreamConsumer.captureOutput(process);
int exitCode = process.waitFor();
if (exitCode != 0) {
return "Failed to run clustering for " + clusteringInstantTime;
}
return "Succeeded to run clustering for " + clusteringInstantTime;
}
}

43
hudi-cli/src/main/java/org/apache/hudi/cli/commands/SparkMain.java
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@@ -41,6 +41,7 @@ import org.apache.hudi.table.upgrade.SparkUpgradeDowngrade;
import org.apache.hudi.utilities.HDFSParquetImporter;
import org.apache.hudi.utilities.HDFSParquetImporter.Config;
import org.apache.hudi.utilities.HoodieCleaner;
import org.apache.hudi.utilities.HoodieClusteringJob;
import org.apache.hudi.utilities.HoodieCompactionAdminTool;
import org.apache.hudi.utilities.HoodieCompactionAdminTool.Operation;
import org.apache.hudi.utilities.HoodieCompactor;
@@ -70,7 +71,8 @@ public class SparkMain {
*/
enum SparkCommand {
BOOTSTRAP, ROLLBACK, DEDUPLICATE, ROLLBACK_TO_SAVEPOINT, SAVEPOINT, IMPORT, UPSERT, COMPACT_SCHEDULE, COMPACT_RUN,
COMPACT_UNSCHEDULE_PLAN, COMPACT_UNSCHEDULE_FILE, COMPACT_VALIDATE, COMPACT_REPAIR, CLEAN, DELETE_SAVEPOINT, UPGRADE, DOWNGRADE
COMPACT_UNSCHEDULE_PLAN, COMPACT_UNSCHEDULE_FILE, COMPACT_VALIDATE, COMPACT_REPAIR, CLUSTERING_SCHEDULE,
CLUSTERING_RUN, CLEAN, DELETE_SAVEPOINT, UPGRADE, DOWNGRADE
}
public static void main(String[] args) throws Exception {
@@ -157,6 +159,31 @@ public class SparkMain {
Boolean.parseBoolean(args[7]), Boolean.parseBoolean(args[8]));
returnCode = 0;
break;
case CLUSTERING_RUN:
assert (args.length >= 8);
propsFilePath = null;
if (!StringUtils.isNullOrEmpty(args[7])) {
propsFilePath = args[7];
}
configs = new ArrayList<>();
if (args.length > 8) {
configs.addAll(Arrays.asList(args).subList(8, args.length));
}
returnCode = cluster(jsc, args[1], args[2], args[3], Integer.parseInt(args[4]), args[5],
Integer.parseInt(args[6]), false, propsFilePath, configs);
break;
case CLUSTERING_SCHEDULE:
assert (args.length >= 6);
propsFilePath = null;
if (!StringUtils.isNullOrEmpty(args[5])) {
propsFilePath = args[5];
}
configs = new ArrayList<>();
if (args.length > 6) {
configs.addAll(Arrays.asList(args).subList(6, args.length));
}
returnCode = cluster(jsc, args[1], args[2], args[3], 1, args[4], 0, true, propsFilePath, configs);
break;
case CLEAN:
assert (args.length >= 5);
propsFilePath = null;
@@ -302,6 +329,20 @@ public class SparkMain {
return new HoodieCompactor(jsc, cfg).compact(retry);
}
private static int cluster(JavaSparkContext jsc, String basePath, String tableName, String clusteringInstant,
int parallelism, String sparkMemory, int retry, boolean schedule, String propsFilePath, List<String> configs) {
HoodieClusteringJob.Config cfg = new HoodieClusteringJob.Config();
cfg.basePath = basePath;
cfg.tableName = tableName;
cfg.clusteringInstantTime = clusteringInstant;
cfg.parallelism = parallelism;
cfg.runSchedule = schedule;
cfg.propsFilePath = propsFilePath;
cfg.configs = configs;
jsc.getConf().set("spark.executor.memory", sparkMemory);
return new HoodieClusteringJob(jsc, cfg).cluster(retry);
}
private static int deduplicatePartitionPath(JavaSparkContext jsc, String duplicatedPartitionPath,
String repairedOutputPath, String basePath, boolean dryRun, String dedupeType) {
DedupeSparkJob job = new DedupeSparkJob(basePath, duplicatedPartitionPath, repairedOutputPath, new SQLContext(jsc),

5
hudi-utilities/src/main/java/org/apache/hudi/utilities/HoodieClusteringJob.java
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@@ -164,7 +164,10 @@ public class HoodieClusteringJob {
String schemaStr = getSchemaFromLatestInstant();
SparkRDDWriteClient client =
UtilHelpers.createHoodieClient(jsc, cfg.basePath, schemaStr, cfg.parallelism, Option.empty(), props);
if (cfg.clusteringInstantTime != null) {
client.scheduleClusteringAtInstant(cfg.clusteringInstantTime, Option.empty());
return Option.of(cfg.clusteringInstantTime);
}
return client.scheduleClustering(Option.empty());
}
}