jintaoguan commented on a change in pull request #2773: URL: https://github.com/apache/hudi/pull/2773#discussion_r609351211
########## File path: hudi-cli/src/main/java/org/apache/hudi/cli/commands/ClusteringCommand.java ########## @@ -0,0 +1,102 @@ +/* + * 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(); Review comment: Good catch! Thanks. ########## File path: hudi-cli/src/main/java/org/apache/hudi/cli/commands/ClusteringCommand.java ########## @@ -0,0 +1,102 @@ +/* + * 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(); + 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 "Attempted to schedule clustering for " + clusteringInstantTime; Review comment: Updated. ########## File path: hudi-cli/src/main/java/org/apache/hudi/cli/commands/ClusteringCommand.java ########## @@ -0,0 +1,102 @@ +/* + * 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(); + 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 "Attempted 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(); + 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 "Clustering successfully completed for " + clusteringInstantTime; Review comment: Updated. -- This is an automated message from the Apache Git Service. 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