xushiyan commented on a change in pull request #1360: [HUDI-344][RFC-09] Hudi 
Dataset Snapshot Exporter
URL: https://github.com/apache/incubator-hudi/pull/1360#discussion_r386788061
 
 

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 File path: 
hudi-utilities/src/main/java/org/apache/hudi/utilities/HoodieSnapshotExporter.java
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+/*
+ * 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.hadoop.fs.FileStatus;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.FileUtil;
+import org.apache.hadoop.fs.Path;
+import org.apache.hudi.common.util.StringUtils;
+import org.apache.hudi.common.SerializableConfiguration;
+import org.apache.hudi.common.model.HoodiePartitionMetadata;
+import org.apache.hudi.common.table.HoodieTableConfig;
+import org.apache.hudi.common.table.HoodieTableMetaClient;
+import org.apache.hudi.common.table.HoodieTimeline;
+import org.apache.hudi.common.table.TableFileSystemView;
+import org.apache.hudi.common.table.timeline.HoodieInstant;
+import org.apache.hudi.common.table.view.HoodieTableFileSystemView;
+import org.apache.hudi.common.util.FSUtils;
+import org.apache.hudi.common.util.Option;
+import org.apache.log4j.LogManager;
+import org.apache.log4j.Logger;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.sql.Column;
+import org.apache.spark.sql.DataFrameWriter;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SaveMode;
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.execution.datasources.DataSource;
+
+import scala.Tuple2;
+import scala.collection.JavaConversions;
+
+import java.io.IOException;
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.stream.Collectors;
+
+/**
+ * Export the latest records of Hudi dataset to a set of external files (e.g., 
plain parquet files).
+ */
+
+public class HoodieSnapshotExporter {
+  private static final Logger LOG = 
LogManager.getLogger(HoodieSnapshotExporter.class);
+
+  public static class Config implements Serializable {
+    @Parameter(names = {"--source-base-path"}, description = "Base path for 
the source Hudi dataset to be snapshotted", required = true)
+    String sourceBasePath = null;
+
+    @Parameter(names = {"--target-base-path"}, description = "Base path for 
the target output files (snapshots)", required = true)
+    String targetOutputPath = null;
+
+    @Parameter(names = {"--snapshot-prefix"}, description = "Snapshot prefix 
or directory under the target base path in order to segregate different 
snapshots")
+    String snapshotPrefix;
+
+    @Parameter(names = {"--output-format"}, description = "e.g. Hudi or 
Parquet", required = true)
+    String outputFormat;
+
+    @Parameter(names = {"--output-partition-field"}, description = "A field to 
be used by Spark repartitioning")
+    String outputPartitionField;
+  }
+
+  public int export(SparkSession spark, Config cfg) throws IOException {
+    JavaSparkContext jsc = new JavaSparkContext(spark.sparkContext());
+    FileSystem fs = FSUtils.getFs(cfg.sourceBasePath, 
jsc.hadoopConfiguration());
+
+    final SerializableConfiguration serConf = new 
SerializableConfiguration(jsc.hadoopConfiguration());
+    final HoodieTableMetaClient tableMetadata = new 
HoodieTableMetaClient(fs.getConf(), cfg.sourceBasePath);
+    final TableFileSystemView.BaseFileOnlyView fsView = new 
HoodieTableFileSystemView(tableMetadata,
+        
tableMetadata.getActiveTimeline().getCommitsTimeline().filterCompletedInstants());
+    // Get the latest commit
+    Option<HoodieInstant> latestCommit =
+        
tableMetadata.getActiveTimeline().getCommitsTimeline().filterCompletedInstants().lastInstant();
+    if (!latestCommit.isPresent()) {
+      LOG.error("No commits present. Nothing to snapshot");
+      return -1;
+    }
+    final String latestCommitTimestamp = latestCommit.get().getTimestamp();
+    LOG.info(String.format("Starting to snapshot latest version files which 
are also no-late-than %s.",
+        latestCommitTimestamp));
+
+    List<String> partitions = FSUtils.getAllPartitionPaths(fs, 
cfg.sourceBasePath, false);
+    if (partitions.size() > 0) {
+      List<String> dataFiles = new ArrayList<>();
+
+      if (!StringUtils.isNullOrEmpty(cfg.snapshotPrefix)) {
+        for (String partition : partitions) {
+          if (partition.contains(cfg.snapshotPrefix)) {
+            dataFiles.addAll(fsView.getLatestBaseFilesBeforeOrOn(partition, 
latestCommitTimestamp).map(f -> f.getPath()).collect(Collectors.toList()));
+          }
+        }
+      } else {
+        for (String partition : partitions) {
+          dataFiles.addAll(fsView.getLatestBaseFilesBeforeOrOn(partition, 
latestCommitTimestamp).map(f -> f.getPath()).collect(Collectors.toList()));
+        }
+      }
+      try {
+        DataSource.lookupDataSource(cfg.outputFormat, 
spark.sessionState().conf());
+      } catch (Exception e) {
+        LOG.error(String.format("The %s output format is not supported! ", 
cfg.outputFormat));
+        return -1;
+      }
+      if (!cfg.outputFormat.equalsIgnoreCase("hudi")) {
+        // Do transformation
+        // A field to do simple Spark repartitioning
+        DataFrameWriter<Row> write = null;
+        Dataset<Row> original = 
spark.read().parquet(JavaConversions.asScalaIterator(dataFiles.iterator()).toSeq());
+        List<Column> needColumns = 
Arrays.asList(original.columns()).stream().filter(col -> 
!col.contains("_hoodie_")).map(col -> new 
Column(col)).collect(Collectors.toList());
 
 Review comment:
   This is optional but actually i'd even prefer having an immutable list of 
`_hoodie_*` reserved field names so we can be explicit on what we are removing 
while exporting.

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