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KYLIN-4035 Calculate column cardinality by using spark engine
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engine-spark/src/main/java/org/apache/kylin/engine/spark/SparkColumnCardinality.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. | ||
| */ | ||
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| package org.apache.kylin.engine.spark; | ||
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| import org.apache.commons.cli.Option; | ||
| import org.apache.commons.cli.OptionBuilder; | ||
| import org.apache.commons.cli.Options; | ||
| import org.apache.hadoop.fs.Path; | ||
| import org.apache.hadoop.io.IntWritable; | ||
| import org.apache.hadoop.io.LongWritable; | ||
| import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; | ||
| import org.apache.kylin.common.util.AbstractApplication; | ||
| import org.apache.kylin.common.util.Bytes; | ||
| import org.apache.kylin.common.util.HadoopUtil; | ||
| import org.apache.kylin.common.util.OptionsHelper; | ||
| import org.apache.kylin.engine.mr.common.BatchConstants; | ||
| import org.apache.kylin.measure.hllc.HLLCounter; | ||
| import org.apache.spark.SparkConf; | ||
| import org.apache.spark.api.java.JavaPairRDD; | ||
| import org.apache.spark.api.java.JavaRDD; | ||
| import org.apache.spark.api.java.JavaSparkContext; | ||
| import org.apache.spark.api.java.function.PairFlatMapFunction; | ||
| import org.slf4j.Logger; | ||
| import org.slf4j.LoggerFactory; | ||
| import scala.Tuple2; | ||
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| import java.io.Serializable; | ||
| import java.util.ArrayList; | ||
| import java.util.HashMap; | ||
| import java.util.Iterator; | ||
| import java.util.List; | ||
| import java.util.Map; | ||
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| public class SparkColumnCardinality extends AbstractApplication implements Serializable { | ||
| protected static final Logger logger = LoggerFactory.getLogger(SparkColumnCardinality.class); | ||
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| public static final Option OPTION_TABLE_NAME = OptionBuilder.withArgName(BatchConstants.ARG_TABLE_NAME).hasArg() | ||
| .isRequired(true).withDescription("Table Name").create(BatchConstants.ARG_TABLE_NAME); | ||
| public static final Option OPTION_OUTPUT = OptionBuilder.withArgName(BatchConstants.ARG_OUTPUT).hasArg() | ||
| .isRequired(true).withDescription("Output").create(BatchConstants.ARG_OUTPUT); | ||
| public static final Option OPTION_PRJ = OptionBuilder.withArgName(BatchConstants.ARG_PROJECT).hasArg() | ||
| .isRequired(true).withDescription("Project name").create(BatchConstants.ARG_PROJECT); | ||
| public static final Option OPTION_COLUMN_COUNT = OptionBuilder.withArgName(BatchConstants.CFG_OUTPUT_COLUMN).hasArg() | ||
| .isRequired(true).withDescription("column count").create(BatchConstants.CFG_OUTPUT_COLUMN); | ||
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| private Options options; | ||
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| public SparkColumnCardinality() { | ||
| options = new Options(); | ||
| options.addOption(OPTION_TABLE_NAME); | ||
| options.addOption(OPTION_OUTPUT); | ||
| options.addOption(OPTION_PRJ); | ||
| options.addOption(OPTION_COLUMN_COUNT); | ||
| } | ||
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| @Override | ||
| protected Options getOptions() { | ||
| return options; | ||
| } | ||
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| @Override | ||
| protected void execute(OptionsHelper optionsHelper) throws Exception { | ||
| String tableName = optionsHelper.getOptionValue(OPTION_TABLE_NAME); | ||
| String output = optionsHelper.getOptionValue(OPTION_OUTPUT); | ||
| int columnCnt = Integer.valueOf(optionsHelper.getOptionValue(OPTION_COLUMN_COUNT)); | ||
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| Class[] kryoClassArray = new Class[]{Class.forName("scala.reflect.ClassTag$$anon$1"), | ||
| Class.forName("org.apache.kylin.engine.mr.steps.SelfDefineSortableKey")}; | ||
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| SparkConf conf = new SparkConf().setAppName("Calculate table:" + tableName); | ||
| //set spark.sql.catalogImplementation=hive, If it is not set, SparkSession can't read hive metadata, and throw "org.apache.spark.sql.AnalysisException" | ||
| conf.set("spark.sql.catalogImplementation", "hive"); | ||
| //serialization conf | ||
| conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer"); | ||
| conf.set("spark.kryo.registrator", "org.apache.kylin.engine.spark.KylinKryoRegistrator"); | ||
| conf.set("spark.kryo.registrationRequired", "true").registerKryoClasses(kryoClassArray); | ||
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| KylinSparkJobListener jobListener = new KylinSparkJobListener(); | ||
| try (JavaSparkContext sc = new JavaSparkContext(conf)) { | ||
| sc.sc().addSparkListener(jobListener); | ||
| HadoopUtil.deletePath(sc.hadoopConfiguration(), new Path(output)); | ||
| // table will be loaded by spark sql, so isSequenceFile set false | ||
| final JavaRDD<String[]> recordRDD = SparkUtil.hiveRecordInputRDD(false, sc, null, tableName); | ||
| JavaPairRDD<Integer, Long> resultRdd = recordRDD.mapPartitionsToPair(new BuildHllCounter()) | ||
| .reduceByKey((x, y) -> { | ||
| x.merge(y); | ||
| return x; | ||
| }) | ||
| .mapToPair(record -> { | ||
| return new Tuple2<>(record._1, record._2.getCountEstimate()); | ||
| }) | ||
| .sortByKey(true, 1) | ||
| .cache(); | ||
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| if (resultRdd.count() == 0) { | ||
| ArrayList<Tuple2<Integer, Long>> list = new ArrayList<>(); | ||
| for (int i = 0; i < columnCnt; ++i) { | ||
| list.add(new Tuple2<>(i, 0L)); | ||
| } | ||
| JavaPairRDD<Integer, Long> nullRdd = sc.parallelizePairs(list).repartition(1); | ||
| nullRdd.saveAsNewAPIHadoopFile(output, IntWritable.class, LongWritable.class, TextOutputFormat.class); | ||
| } else { | ||
| resultRdd.saveAsNewAPIHadoopFile(output, IntWritable.class, LongWritable.class, TextOutputFormat.class); | ||
| } | ||
| } | ||
| } | ||
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| static class BuildHllCounter implements | ||
| PairFlatMapFunction<Iterator<String[]>, Integer, HLLCounter> { | ||
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| public BuildHllCounter() { | ||
| logger.info("BuildHllCounter init here."); | ||
| } | ||
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| @Override | ||
| public Iterator<Tuple2<Integer, HLLCounter>> call(Iterator<String[]> iterator) throws Exception { | ||
| HashMap<Integer, HLLCounter> hllmap = new HashMap<>(); | ||
| while (iterator.hasNext()) { | ||
| String[] values = iterator.next(); | ||
| for (int m = 0; m < values.length; ++m) { | ||
| String fieldValue = values[m]; | ||
| if (fieldValue == null) { | ||
| fieldValue = "NULL"; | ||
| } | ||
| getHllc(hllmap, m).add(Bytes.toBytes(fieldValue)); | ||
| } | ||
| } | ||
| // convert from hashmap to tuple2(scala). | ||
| List<Tuple2<Integer, HLLCounter>> result = new ArrayList<>(); | ||
| for (Map.Entry<Integer, HLLCounter> entry : hllmap.entrySet()) { | ||
| result.add(new Tuple2<>(entry.getKey(), entry.getValue())); | ||
| } | ||
| return result.iterator(); | ||
| } | ||
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| private HLLCounter getHllc(HashMap<Integer, HLLCounter> hllcMap, Integer key) { | ||
| if (!hllcMap.containsKey(key)) { | ||
| hllcMap.put(key, new HLLCounter()); | ||
| } | ||
| return hllcMap.get(key); | ||
| } | ||
| } | ||
| } |
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