[GitHub] [hudi] lw309637554 commented on a change in pull request #2379: [HUDI-1399] support a independent clustering spark job to asynchronously clustering

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lw309637554 commented on a change in pull request #2379:
URL: https://github.com/apache/hudi/pull/2379#discussion_r551984380



##########
File path: hudi-utilities/src/main/java/org/apache/hudi/utilities/HoodieClusteringJob.java
##########
@@ -0,0 +1,159 @@
+/*
+ * 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.utilities;
+
+import com.beust.jcommander.JCommander;
+import com.beust.jcommander.Parameter;
+import org.apache.avro.Schema;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hudi.client.SparkRDDWriteClient;
+import org.apache.hudi.client.WriteStatus;
+import org.apache.hudi.common.config.TypedProperties;
+import org.apache.hudi.common.fs.FSUtils;
+import org.apache.hudi.common.table.HoodieTableMetaClient;
+import org.apache.hudi.common.table.TableSchemaResolver;
+import org.apache.hudi.common.util.Option;
+import org.apache.log4j.LogManager;
+import org.apache.log4j.Logger;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.List;
+
+public class HoodieClusteringJob {
+
+  private static final Logger LOG = LogManager.getLogger(HoodieClusteringJob.class);
+  private final Config cfg;
+  private transient FileSystem fs;
+  private TypedProperties props;
+  private final JavaSparkContext jsc;
+
+  public HoodieClusteringJob(JavaSparkContext jsc, Config cfg) {
+    this.cfg = cfg;
+    this.jsc = jsc;
+    this.props = cfg.propsFilePath == null
+        ? UtilHelpers.buildProperties(cfg.configs)
+        : readConfigFromFileSystem(jsc, cfg);
+  }
+
+  private TypedProperties readConfigFromFileSystem(JavaSparkContext jsc, Config cfg) {
+    final FileSystem fs = FSUtils.getFs(cfg.basePath, jsc.hadoopConfiguration());
+
+    return UtilHelpers
+        .readConfig(fs, new Path(cfg.propsFilePath), cfg.configs)
+        .getConfig();
+  }
+
+  public static class Config implements Serializable {
+    @Parameter(names = {"--base-path", "-sp"}, description = "Base path for the table", required = true)
+    public String basePath = null;
+    @Parameter(names = {"--table-name", "-tn"}, description = "Table name", required = true)
+    public String tableName = null;
+    @Parameter(names = {"--instant-time", "-it"}, description = "Clustering Instant time", required = true)
+    public String clusteringInstantTime = null;
+    @Parameter(names = {"--parallelism", "-pl"}, description = "Parallelism for hoodie insert", required = false)
+    public int parallelism = 1;
+    @Parameter(names = {"--spark-master", "-ms"}, description = "Spark master", required = false)
+    public String sparkMaster = null;
+    @Parameter(names = {"--spark-memory", "-sm"}, description = "spark memory to use", required = true)
+    public String sparkMemory = null;
+    @Parameter(names = {"--retry", "-rt"}, description = "number of retries", required = false)
+    public int retry = 0;
+
+    @Parameter(names = {"--schedule", "-sc"}, description = "Schedule clustering")
+    public Boolean runSchedule = false;
+    @Parameter(names = {"--help", "-h"}, help = true)
+    public Boolean help = false;
+
+    @Parameter(names = {"--props"}, description = "path to properties file on localfs or dfs, with configurations for "
+        + "hoodie client for clustering")
+    public String propsFilePath = null;
+
+    @Parameter(names = {"--hoodie-conf"}, description = "Any configuration that can be set in the properties file "
+        + "(using the CLI parameter \"--props\") can also be passed command line using this parameter. This can be repeated",
+            splitter = IdentitySplitter.class)
+    public List<String> configs = new ArrayList<>();
+  }
+
+  public static void main(String[] args) {
+    final Config cfg = new Config();
+    JCommander cmd = new JCommander(cfg, null, args);
+    if (cfg.help || args.length == 0) {
+      cmd.usage();
+      System.exit(1);
+    }
+    final JavaSparkContext jsc = UtilHelpers.buildSparkContext("clustering-" + cfg.tableName, cfg.sparkMaster, cfg.sparkMemory);
+    HoodieClusteringJob clusteringJob = new HoodieClusteringJob(jsc, cfg);
+    int result = clusteringJob.cluster(cfg.retry);
+    String resultMsg = String.format("Clustering with basePath: %s, tableName: %s, runSchedule: %s, clusteringInstantTime: %s",
+        cfg.basePath, cfg.tableName, cfg.runSchedule, cfg.clusteringInstantTime);
+    if (result == -1) {
+      LOG.error(resultMsg + " failed");
+    } else {
+      LOG.info(resultMsg + " success");
+    }
+    jsc.stop();
+  }
+
+  public int cluster(int retry) {
+    this.fs = FSUtils.getFs(cfg.basePath, jsc.hadoopConfiguration());
+    int ret = -1;
+    try {
+      do {
+        if (cfg.runSchedule) {
+          LOG.info("Do schedule");
+          ret = doSchedule(jsc);
+        } else {
+          LOG.info("Do cluster");
+          ret = doCluster(jsc);
+        }
+      } while (ret != 0 && retry-- > 0);
+    } catch (Throwable t) {
+      LOG.error("Cluster failed", t);
+    }
+    return ret;
+  }
+
+  private String getSchemaFromLatestInstant() throws Exception {
+    HoodieTableMetaClient metaClient =  new HoodieTableMetaClient(jsc.hadoopConfiguration(), cfg.basePath, true);
+    TableSchemaResolver schemaUtil = new TableSchemaResolver(metaClient);
+    Schema schema = schemaUtil.getTableAvroSchema(false);
+    return schema.toString();
+  }
+
+  private int doCluster(JavaSparkContext jsc) throws Exception {
+    String schemaStr = getSchemaFromLatestInstant();
+    SparkRDDWriteClient client =
+        UtilHelpers.createHoodieClient(jsc, cfg.basePath, schemaStr, cfg.parallelism, Option.empty(), props);
+    JavaRDD<WriteStatus> writeResponse =
+        (JavaRDD<WriteStatus>) client.cluster(cfg.clusteringInstantTime, true).getWriteStatuses();
+    return UtilHelpers.handleErrors(jsc, cfg.clusteringInstantTime, writeResponse);
+  }
+
+  private int doSchedule(JavaSparkContext jsc) throws Exception {
+    String schemaStr = getSchemaFromLatestInstant();
+    SparkRDDWriteClient client =
+        UtilHelpers.createHoodieClient(jsc, cfg.basePath, schemaStr, cfg.parallelism, Option.empty(), props);
+    return client.scheduleClusteringAtInstant(cfg.clusteringInstantTime, Option.empty()) ? 0 : -1;

Review comment:
       @satishkotha  make sense , have use client.scheduleClustering() to auto generate instantTime. Also have some questions for you. Why clustering instant time should greater than latest commit?  I see BaseScheduleCompactionActionExecutor.execute() have check  that should not have earliest write inflight instant time than compaction instant time. But BaseClusteringPlanActionExecutor.execute()  do not have similarity check. Thanks.




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