/*
 * 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.kylin.engine.spark;

import java.io.Serializable;
import java.nio.ByteBuffer;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.Locale;

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.mapreduce.Job;
import org.apache.kylin.common.KylinConfig;
import org.apache.kylin.common.util.AbstractApplication;
import org.apache.kylin.common.util.ByteArray;
import org.apache.kylin.common.util.HadoopUtil;
import org.apache.kylin.common.util.OptionsHelper;
import org.apache.kylin.cube.CubeDescManager;
import org.apache.kylin.cube.CubeInstance;
import org.apache.kylin.cube.CubeManager;
import org.apache.kylin.cube.CubeSegment;
import org.apache.kylin.cube.common.RowKeySplitter;
import org.apache.kylin.cube.cuboid.Cuboid;
import org.apache.kylin.cube.kv.AbstractRowKeyEncoder;
import org.apache.kylin.cube.kv.RowKeyEncoderProvider;
import org.apache.kylin.cube.model.CubeDesc;
import org.apache.kylin.cube.model.CubeJoinedFlatTableEnrich;
import org.apache.kylin.engine.EngineFactory;
import org.apache.kylin.engine.mr.BatchCubingJobBuilder2;
import org.apache.kylin.engine.mr.IMROutput2;
import org.apache.kylin.engine.mr.MRUtil;
import org.apache.kylin.engine.mr.common.AbstractHadoopJob;
import org.apache.kylin.engine.mr.common.BaseCuboidBuilder;
import org.apache.kylin.engine.mr.common.BatchConstants;
import org.apache.kylin.engine.mr.common.CubeStatsReader;
import org.apache.kylin.engine.mr.common.NDCuboidBuilder;
import org.apache.kylin.engine.mr.common.SerializableConfiguration;
import org.apache.kylin.job.JoinedFlatTable;
import org.apache.kylin.measure.BufferedMeasureCodec;
import org.apache.kylin.measure.MeasureAggregators;
import org.apache.kylin.measure.MeasureIngester;
import org.apache.kylin.metadata.model.MeasureDesc;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFlatMapFunction;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.storage.StorageLevel;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import scala.Tuple2;

/**
 * Spark application to build cube with the "by-layer" algorithm. Only support source data from Hive; Metadata in HBase.
 */
public class SparkCubingByLayer extends AbstractApplication implements Serializable {

    protected static final Logger logger = LoggerFactory.getLogger(SparkCubingByLayer.class);

    public static final Option OPTION_CUBE_NAME = OptionBuilder.withArgName(BatchConstants.ARG_CUBE_NAME).hasArg()
            .isRequired(true).withDescription("Cube Name").create(BatchConstants.ARG_CUBE_NAME);
    public static final Option OPTION_SEGMENT_ID = OptionBuilder.withArgName("segment").hasArg().isRequired(true)
            .withDescription("Cube Segment Id").create("segmentId");
    public static final Option OPTION_META_URL = OptionBuilder.withArgName("metaUrl").hasArg().isRequired(true)
            .withDescription("HDFS metadata url").create("metaUrl");
    public static final Option OPTION_OUTPUT_PATH = OptionBuilder.withArgName(BatchConstants.ARG_OUTPUT).hasArg()
            .isRequired(true).withDescription("Cube output path").create(BatchConstants.ARG_OUTPUT);
    public static final Option OPTION_INPUT_TABLE = OptionBuilder.withArgName("hiveTable").hasArg().isRequired(true)
            .withDescription("Hive Intermediate Table").create("hiveTable");
    public static final Option OPTION_INPUT_PATH = OptionBuilder.withArgName(BatchConstants.ARG_INPUT).hasArg()
            .isRequired(true).withDescription("Hive Intermediate Table PATH").create(BatchConstants.ARG_INPUT);

    private Options options;

    public SparkCubingByLayer() {
        options = new Options();
        options.addOption(OPTION_INPUT_TABLE);
        options.addOption(OPTION_INPUT_PATH);
        options.addOption(OPTION_CUBE_NAME);
        options.addOption(OPTION_SEGMENT_ID);
        options.addOption(OPTION_META_URL);
        options.addOption(OPTION_OUTPUT_PATH);
    }

    @Override
    protected Options getOptions() {
        return options;
    }

    @Override
    protected void execute(OptionsHelper optionsHelper) throws Exception {
        String metaUrl = optionsHelper.getOptionValue(OPTION_META_URL);
        String hiveTable = optionsHelper.getOptionValue(OPTION_INPUT_TABLE);
        String inputPath = optionsHelper.getOptionValue(OPTION_INPUT_PATH);
        String cubeName = optionsHelper.getOptionValue(OPTION_CUBE_NAME);
        String segmentId = optionsHelper.getOptionValue(OPTION_SEGMENT_ID);
        String outputPath = optionsHelper.getOptionValue(OPTION_OUTPUT_PATH);

        Class[] kryoClassArray = new Class[] { Class.forName("scala.reflect.ClassTag$$anon$1") };

        SparkConf conf = new SparkConf().setAppName("Cubing for:" + cubeName + " segment " + segmentId);
        //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);

        KylinSparkJobListener jobListener = new KylinSparkJobListener();
        JavaSparkContext sc = new JavaSparkContext(conf);
        sc.sc().addSparkListener(jobListener);
        HadoopUtil.deletePath(sc.hadoopConfiguration(), new Path(outputPath));
        SparkUtil.modifySparkHadoopConfiguration(sc.sc()); // set dfs.replication=2 and enable compress
        final SerializableConfiguration sConf = new SerializableConfiguration(sc.hadoopConfiguration());
        KylinConfig envConfig = AbstractHadoopJob.loadKylinConfigFromHdfs(sConf, metaUrl);

        final CubeInstance cubeInstance = CubeManager.getInstance(envConfig).getCube(cubeName);
        final CubeDesc cubeDesc = cubeInstance.getDescriptor();
        final CubeSegment cubeSegment = cubeInstance.getSegmentById(segmentId);

        logger.info("RDD input path: {}", inputPath);
        logger.info("RDD Output path: {}", outputPath);

        final Job job = Job.getInstance(sConf.get());
        SparkUtil.setHadoopConfForCuboid(job, cubeSegment, metaUrl);

        int countMeasureIndex = 0;
        for (MeasureDesc measureDesc : cubeDesc.getMeasures()) {
            if (measureDesc.getFunction().isCount() == true) {
                break;
            } else {
                countMeasureIndex++;
            }
        }
        final CubeStatsReader cubeStatsReader = new CubeStatsReader(cubeSegment, envConfig);
        boolean[] needAggr = new boolean[cubeDesc.getMeasures().size()];
        boolean allNormalMeasure = true;
        for (int i = 0; i < cubeDesc.getMeasures().size(); i++) {
            needAggr[i] = !cubeDesc.getMeasures().get(i).getFunction().getMeasureType().onlyAggrInBaseCuboid();
            allNormalMeasure = allNormalMeasure && needAggr[i];
        }
        logger.info("All measure are normal (agg on all cuboids) ? : " + allNormalMeasure);
        StorageLevel storageLevel = StorageLevel.fromString(envConfig.getSparkStorageLevel());

        boolean isSequenceFile = JoinedFlatTable.SEQUENCEFILE.equalsIgnoreCase(envConfig.getFlatTableStorageFormat());

        final JavaPairRDD<ByteArray, Object[]> encodedBaseRDD = SparkUtil
                .hiveRecordInputRDD(isSequenceFile, sc, inputPath, hiveTable)
                .mapToPair(new EncodeBaseCuboid(cubeName, segmentId, metaUrl, sConf));

        Long totalCount = 0L;
        if (envConfig.isSparkSanityCheckEnabled()) {
            totalCount = encodedBaseRDD.count();
        }

        final BaseCuboidReducerFunction2 baseCuboidReducerFunction = new BaseCuboidReducerFunction2(cubeName, metaUrl,
                sConf);
        BaseCuboidReducerFunction2 reducerFunction2 = baseCuboidReducerFunction;
        if (allNormalMeasure == false) {
            reducerFunction2 = new CuboidReducerFunction2(cubeName, metaUrl, sConf, needAggr);
        }

        final int totalLevels = cubeSegment.getCuboidScheduler().getBuildLevel();
        JavaPairRDD<ByteArray, Object[]>[] allRDDs = new JavaPairRDD[totalLevels + 1];
        int level = 0;
        int partition = SparkUtil.estimateLayerPartitionNum(level, cubeStatsReader, envConfig);

        // aggregate to calculate base cuboid
        allRDDs[0] = encodedBaseRDD.reduceByKey(baseCuboidReducerFunction, partition).persist(storageLevel);

        saveToHDFS(allRDDs[0], metaUrl, cubeName, cubeSegment, outputPath, 0, job, envConfig);

        PairFlatMapFunction flatMapFunction = new CuboidFlatMap(cubeName, segmentId, metaUrl, sConf);
        // aggregate to ND cuboids
        for (level = 1; level <= totalLevels; level++) {
            partition = SparkUtil.estimateLayerPartitionNum(level, cubeStatsReader, envConfig);

            allRDDs[level] = allRDDs[level - 1].flatMapToPair(flatMapFunction).reduceByKey(reducerFunction2, partition)
                    .persist(storageLevel);
            allRDDs[level - 1].unpersist(false);
            if (envConfig.isSparkSanityCheckEnabled() == true) {
                sanityCheck(allRDDs[level], totalCount, level, cubeStatsReader, countMeasureIndex);
            }
            saveToHDFS(allRDDs[level], metaUrl, cubeName, cubeSegment, outputPath, level, job, envConfig);
        }
        allRDDs[totalLevels].unpersist(false);
        logger.info("Finished on calculating all level cuboids.");
        logger.info("HDFS: Number of bytes written=" + jobListener.metrics.getBytesWritten());
        //HadoopUtil.deleteHDFSMeta(metaUrl);
    }

    protected JavaPairRDD<ByteArray, Object[]> prepareOutput(JavaPairRDD<ByteArray, Object[]> rdd, KylinConfig config,
            CubeSegment segment, int level) {
        return rdd;
    }

    protected void saveToHDFS(final JavaPairRDD<ByteArray, Object[]> rdd, final String metaUrl, final String cubeName,
            final CubeSegment cubeSeg, final String hdfsBaseLocation, final int level, final Job job,
            final KylinConfig kylinConfig) throws Exception {
        final String cuboidOutputPath = BatchCubingJobBuilder2.getCuboidOutputPathsByLevel(hdfsBaseLocation, level);
        final SerializableConfiguration sConf = new SerializableConfiguration(job.getConfiguration());

        IMROutput2.IMROutputFormat outputFormat = MRUtil.getBatchCubingOutputSide2(cubeSeg).getOuputFormat();
        outputFormat.configureJobOutput(job, cuboidOutputPath, cubeSeg, cubeSeg.getCuboidScheduler(), level);

        prepareOutput(rdd, kylinConfig, cubeSeg, level).mapToPair(
                new PairFunction<Tuple2<ByteArray, Object[]>, org.apache.hadoop.io.Text, org.apache.hadoop.io.Text>() {
                    private volatile transient boolean initialized = false;
                    BufferedMeasureCodec codec;

                    @Override
                    public Tuple2<org.apache.hadoop.io.Text, org.apache.hadoop.io.Text> call(
                            Tuple2<ByteArray, Object[]> tuple2) throws Exception {

                        if (initialized == false) {
                            synchronized (SparkCubingByLayer.class) {
                                if (initialized == false) {
                                    KylinConfig kylinConfig = AbstractHadoopJob.loadKylinConfigFromHdfs(sConf, metaUrl);
                                    try (KylinConfig.SetAndUnsetThreadLocalConfig autoUnset = KylinConfig
                                            .setAndUnsetThreadLocalConfig(kylinConfig)) {
                                        CubeDesc desc = CubeDescManager.getInstance(kylinConfig).getCubeDesc(cubeName);
                                        codec = new BufferedMeasureCodec(desc.getMeasures());
                                        initialized = true;
                                    }
                                }
                            }
                        }
                        ByteBuffer valueBuf = codec.encode(tuple2._2());
                        org.apache.hadoop.io.Text textResult =  new org.apache.hadoop.io.Text();
                        textResult.set(valueBuf.array(), 0, valueBuf.position());
                        return new Tuple2<>(new org.apache.hadoop.io.Text(tuple2._1().array()), textResult);
                    }
                }).saveAsNewAPIHadoopDataset(job.getConfiguration());
        logger.info("Persisting RDD for level " + level + " into " + cuboidOutputPath);
    }

    static public class EncodeBaseCuboid implements PairFunction<String[], ByteArray, Object[]> {
        private volatile transient boolean initialized = false;
        private BaseCuboidBuilder baseCuboidBuilder = null;
        private String cubeName;
        private String segmentId;
        private String metaUrl;
        private SerializableConfiguration conf;

        public EncodeBaseCuboid(String cubeName, String segmentId, String metaurl, SerializableConfiguration conf) {
            this.cubeName = cubeName;
            this.segmentId = segmentId;
            this.metaUrl = metaurl;
            this.conf = conf;
        }

        @Override
        public Tuple2<ByteArray, Object[]> call(String[] rowArray) throws Exception {
            if (initialized == false) {
                synchronized (SparkCubingByLayer.class) {
                    if (initialized == false) {
                        KylinConfig kConfig = AbstractHadoopJob.loadKylinConfigFromHdfs(conf, metaUrl);
                        try (KylinConfig.SetAndUnsetThreadLocalConfig autoUnset = KylinConfig
                                .setAndUnsetThreadLocalConfig(kConfig)) {
                            CubeInstance cubeInstance = CubeManager.getInstance(kConfig).getCube(cubeName);
                            CubeDesc cubeDesc = cubeInstance.getDescriptor();
                            CubeSegment cubeSegment = cubeInstance.getSegmentById(segmentId);
                            CubeJoinedFlatTableEnrich interDesc = new CubeJoinedFlatTableEnrich(
                                    EngineFactory.getJoinedFlatTableDesc(cubeSegment), cubeDesc);
                            long baseCuboidId = Cuboid.getBaseCuboidId(cubeDesc);
                            Cuboid baseCuboid = Cuboid.findForMandatory(cubeDesc, baseCuboidId);
                            baseCuboidBuilder = new BaseCuboidBuilder(kConfig, cubeDesc, cubeSegment, interDesc,
                                    AbstractRowKeyEncoder.createInstance(cubeSegment, baseCuboid),
                                    MeasureIngester.create(cubeDesc.getMeasures()), cubeSegment.buildDictionaryMap());
                            initialized = true;
                        }
                    }
                }
            }
            baseCuboidBuilder.resetAggrs();
            byte[] rowKey = baseCuboidBuilder.buildKey(rowArray);
            Object[] result = baseCuboidBuilder.buildValueObjects(rowArray);
            return new Tuple2<>(new ByteArray(rowKey), result);
        }
    }

    static public class BaseCuboidReducerFunction2 implements Function2<Object[], Object[], Object[]> {
        protected String cubeName;
        protected String metaUrl;
        protected CubeDesc cubeDesc;
        protected int measureNum;
        protected MeasureAggregators aggregators;
        protected volatile transient boolean initialized = false;
        protected SerializableConfiguration conf;

        public BaseCuboidReducerFunction2(String cubeName, String metaUrl, SerializableConfiguration conf) {
            this.cubeName = cubeName;
            this.metaUrl = metaUrl;
            this.conf = conf;
        }

        public void init() {
            KylinConfig kConfig = AbstractHadoopJob.loadKylinConfigFromHdfs(conf, metaUrl);
            try (KylinConfig.SetAndUnsetThreadLocalConfig autoUnset = KylinConfig
                    .setAndUnsetThreadLocalConfig(kConfig)) {
                CubeInstance cubeInstance = CubeManager.getInstance(kConfig).getCube(cubeName);
                cubeDesc = cubeInstance.getDescriptor();
                aggregators = new MeasureAggregators(cubeDesc.getMeasures());
                measureNum = cubeDesc.getMeasures().size();
            }
        }

        @Override
        public Object[] call(Object[] input1, Object[] input2) throws Exception {
            if (initialized == false) {
                synchronized (SparkCubingByLayer.class) {
                    if (initialized == false) {
                        init();
                        initialized = true;
                    }
                }
            }
            Object[] result = new Object[measureNum];
            aggregators.aggregate(input1, input2, result);
            return result;
        }
    }

    static public class CuboidReducerFunction2 extends BaseCuboidReducerFunction2 {
        private boolean[] needAggr;

        public CuboidReducerFunction2(String cubeName, String metaUrl, SerializableConfiguration conf,
                boolean[] needAggr) {
            super(cubeName, metaUrl, conf);
            this.needAggr = needAggr;
        }

        @Override
        public Object[] call(Object[] input1, Object[] input2) throws Exception {
            if (initialized == false) {
                synchronized (SparkCubingByLayer.class) {
                    if (initialized == false) {
                        init();
                        initialized = true;
                    }
                }
            }
            Object[] result = new Object[measureNum];
            aggregators.aggregate(input1, input2, result, needAggr);
            return result;
        }
    }

    private static final java.lang.Iterable<Tuple2<ByteArray, Object[]>> EMTPY_ITERATOR = new ArrayList(0);

    static public class CuboidFlatMap implements PairFlatMapFunction<Tuple2<ByteArray, Object[]>, ByteArray, Object[]> {

        private String cubeName;
        private String segmentId;
        private String metaUrl;
        private CubeSegment cubeSegment;
        private CubeDesc cubeDesc;
        private NDCuboidBuilder ndCuboidBuilder;
        private RowKeySplitter rowKeySplitter;
        private volatile transient boolean initialized = false;
        private SerializableConfiguration conf;

        public CuboidFlatMap(String cubeName, String segmentId, String metaUrl, SerializableConfiguration conf) {
            this.cubeName = cubeName;
            this.segmentId = segmentId;
            this.metaUrl = metaUrl;
            this.conf = conf;
        }

        public void init() {
            KylinConfig kConfig = AbstractHadoopJob.loadKylinConfigFromHdfs(conf, metaUrl);
            try (KylinConfig.SetAndUnsetThreadLocalConfig autoUnset = KylinConfig
                    .setAndUnsetThreadLocalConfig(kConfig)) {
                CubeInstance cubeInstance = CubeManager.getInstance(kConfig).getCube(cubeName);
                this.cubeSegment = cubeInstance.getSegmentById(segmentId);
                this.cubeDesc = cubeInstance.getDescriptor();
                this.ndCuboidBuilder = new NDCuboidBuilder(cubeSegment, new RowKeyEncoderProvider(cubeSegment));
                this.rowKeySplitter = new RowKeySplitter(cubeSegment);
            }
        }

        @Override
        public Iterator<Tuple2<ByteArray, Object[]>> call(final Tuple2<ByteArray, Object[]> tuple2) throws Exception {
            if (initialized == false) {
                synchronized (SparkCubingByLayer.class) {
                    if (initialized == false) {
                        init();
                        initialized = true;
                    }
                }
            }

            byte[] key = tuple2._1().array();
            long cuboidId = rowKeySplitter.parseCuboid(key);
            final List<Long> myChildren = cubeSegment.getCuboidScheduler().getSpanningCuboid(cuboidId);

            // if still empty or null
            if (myChildren == null || myChildren.size() == 0) {
                return EMTPY_ITERATOR.iterator();
            }
            rowKeySplitter.split(key);
            final Cuboid parentCuboid = Cuboid.findForMandatory(cubeDesc, cuboidId);

            List<Tuple2<ByteArray, Object[]>> tuples = new ArrayList(myChildren.size());
            for (Long child : myChildren) {
                Cuboid childCuboid = Cuboid.findForMandatory(cubeDesc, child);
                ByteArray result = ndCuboidBuilder.buildKey2(parentCuboid, childCuboid,
                        rowKeySplitter.getSplitBuffers());

                tuples.add(new Tuple2<>(result, tuple2._2()));
            }

            return tuples.iterator();
        }
    }

    protected void sanityCheck(JavaPairRDD<ByteArray, Object[]> rdd, Long totalCount, int thisLevel,
            CubeStatsReader cubeStatsReader, final int countMeasureIndex) {
        int thisCuboidNum = cubeStatsReader.getCuboidsByLayer(thisLevel).size();
        Long count2 = getRDDCountSum(rdd, countMeasureIndex);
        if (count2 != totalCount * thisCuboidNum) {
            throw new IllegalStateException(
                    String.format(Locale.ROOT, "Sanity check failed, level %s, total count(*) is %s; cuboid number %s",
                            thisLevel, count2, thisCuboidNum));
        } else {
            logger.info("sanity check success for level " + thisLevel + ", count(*) is " + (count2 / thisCuboidNum));
        }
    }

    private Long getRDDCountSum(JavaPairRDD<ByteArray, Object[]> rdd, final int countMeasureIndex) {
        final ByteArray ONE = new ByteArray();
        Long count = rdd.mapValues(new Function<Object[], Long>() {
            @Override
            public Long call(Object[] objects) throws Exception {
                return (Long) objects[countMeasureIndex];
            }
        }).reduce(new Function2<Tuple2<ByteArray, Long>, Tuple2<ByteArray, Long>, Tuple2<ByteArray, Long>>() {
            @Override
            public Tuple2<ByteArray, Long> call(Tuple2<ByteArray, Long> longTuple2, Tuple2<ByteArray, Long> longTuple22)
                    throws Exception {
                return new Tuple2<>(ONE, longTuple2._2() + longTuple22._2());
            }
        })._2();
        return count;
    }

}
