jenniferdai commented on a change in pull request #4253: Add segment 
pre-processing Hadoop job
URL: https://github.com/apache/incubator-pinot/pull/4253#discussion_r291722598
 
 

 ##########
 File path: 
pinot-hadoop/src/main/java/org/apache/pinot/hadoop/job/reducers/SegmentPreprocessingReducer.java
 ##########
 @@ -0,0 +1,86 @@
+/**
+ * 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.pinot.hadoop.job.reducers;
+
+import java.io.IOException;
+import java.util.concurrent.atomic.AtomicInteger;
+import org.apache.avro.generic.GenericRecord;
+import org.apache.avro.mapred.AvroKey;
+import org.apache.avro.mapred.AvroValue;
+import org.apache.avro.mapreduce.AvroMultipleOutputs;
+import org.apache.commons.lang3.RandomStringUtils;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.io.NullWritable;
+import org.apache.hadoop.mapreduce.Reducer;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import static org.apache.pinot.hadoop.job.JobConfigConstants.*;
+
+
+public class SegmentPreprocessingReducer<T>
+    extends Reducer<T, AvroValue<GenericRecord>, AvroKey<GenericRecord>, 
NullWritable> {
+  private static final Logger LOGGER = 
LoggerFactory.getLogger(SegmentPreprocessingReducer.class);
+
+  private AvroMultipleOutputs _multipleOutputs;
+  private AtomicInteger _counter;
+  private int _maxNumberOfRecords;
+  private String _filePrefix;
+
+  @Override
+  public void setup(Context context) {
+    LOGGER.info("Using multiple outputs strategy.");
+    Configuration configuration = context.getConfiguration();
+    _multipleOutputs = new AvroMultipleOutputs(context);
+    _counter = new AtomicInteger();
+    // If it's 0, the output file won't be split into multiple files.
+    // If not, output file will be split when the number of records reaches 
this number.
+    _maxNumberOfRecords = configuration.getInt(MAXIMUM_NUMBER_OF_RECORDS, 0);
+    LOGGER.info("Maximum number of records per file: {}", _maxNumberOfRecords);
+    _filePrefix = RandomStringUtils.randomAlphanumeric(4);
+  }
+
+  @Override
+  public void reduce(final T inputRecord, final 
Iterable<AvroValue<GenericRecord>> values, final Context context)
+      throws IOException, InterruptedException {
+    for (final AvroValue<GenericRecord> value : values) {
+      String fileName = generateFileName();
+      _multipleOutputs.write(new AvroKey<>(value.datum()), NullWritable.get(), 
fileName);
+    }
+  }
+
+  @Override
+  public void cleanup(Context context) throws IOException, 
InterruptedException {
+    LOGGER.info("Clean up reducer.");
+    if (_multipleOutputs != null) {
+      _multipleOutputs.close();
+      _multipleOutputs = null;
+    }
+    LOGGER.info("Finished cleaning up reducer.");
+  }
+
+  private String generateFileName() {
+    if (_maxNumberOfRecords == 0) {
+      return _filePrefix;
+    } else {
+      return _filePrefix + (_counter.getAndIncrement() / _maxNumberOfRecords);
 
 Review comment:
   Can we hide the parameter and disallow setting it for now? I think it's a 
pretty big risk for clients because each reducer can have 1 tiny and the rest 
giant, and if users use this setting over a long period of time, they will have 
to completely rebootstrap all their data. They will expect that we will do this 
work intelligently, but we won't have that until the next phase. In prod, this 
can be very dangerous - users can have downtime and will to rebootstrap their 
entire dataset to fix this problem

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