/*
 * 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.job;

import static org.apache.spark.sql.types.DataTypes.IntegerType;

import java.util.ArrayList;
import java.util.List;
import java.util.Objects;
import java.util.Set;
import java.util.stream.Collectors;
import java.util.stream.IntStream;

import org.apache.hadoop.util.StringUtils;
import org.apache.kylin.common.util.RandomUtil;
import org.apache.kylin.metadata.model.TblColRef;
import org.apache.kylin.storage.StorageFactory;
import org.apache.kylin.engine.spark.NSparkCubingEngine;
import org.apache.kylin.engine.spark.application.SparkApplication;
import org.apache.kylin.engine.spark.builder.DFBuilderHelper$;
import org.apache.kylin.engine.spark.builder.DictionaryBuilderHelper;
import org.apache.kylin.metadata.cube.cuboid.NSpanningTree;
import org.apache.kylin.metadata.cube.cuboid.NSpanningTreeFactory;
import org.apache.kylin.metadata.cube.model.IndexPlan;
import org.apache.kylin.metadata.cube.model.LayoutEntity;
import org.apache.kylin.metadata.cube.model.NBatchConstants;
import org.apache.kylin.metadata.cube.model.NCubeJoinedFlatTableDesc;
import org.apache.kylin.metadata.cube.model.NDataLayout;
import org.apache.kylin.metadata.cube.model.NDataSegment;
import org.apache.kylin.metadata.cube.model.NDataflow;
import org.apache.kylin.metadata.cube.model.NDataflowManager;
import org.apache.kylin.metadata.cube.model.NDataflowUpdate;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.types.DataType;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import org.apache.spark.sql.util.SparderTypeUtil;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.google.common.collect.Lists;
import com.google.common.collect.Sets;

import lombok.val;
import lombok.var;

public class MockedDFBuildJob extends SparkApplication {
    protected static final Logger logger = LoggerFactory.getLogger(MockedDFBuildJob.class);
    protected volatile NSpanningTree nSpanningTree;

    public static void main(String[] args) {
        MockedDFBuildJob nDataflowBuildJob = new MockedDFBuildJob();
        nDataflowBuildJob.execute(args);
    }

    @Override
    protected void doExecute() throws Exception {
        long start = System.currentTimeMillis();
        logger.info("Start Build");
        String dfName = getParam(NBatchConstants.P_DATAFLOW_ID);

        Set<String> segmentIds = Sets.newHashSet(StringUtils.split(getParam(NBatchConstants.P_SEGMENT_IDS)));
        Set<Long> layoutIds = NSparkCubingUtil.str2Longs(getParam(NBatchConstants.P_LAYOUT_IDS));

        try {
            NDataflowManager dfMgr = NDataflowManager.getInstance(config, project);
            IndexPlan indexPlan = dfMgr.getDataflow(dfName).getIndexPlan();
            Set<LayoutEntity> cuboids = NSparkCubingUtil.toLayouts(indexPlan, layoutIds).stream()
                    .filter(Objects::nonNull).collect(Collectors.toSet());
            nSpanningTree = NSpanningTreeFactory.fromLayouts(cuboids, dfName);

            for (String segId : segmentIds) {
                NDataSegment seg = dfMgr.getDataflow(dfName).getSegment(segId);
                val dimensions = new ArrayList<Integer>(indexPlan.getModel().getEffectiveCols().keySet());
                List<DataType> sparkTypes = dimensions.stream().map(x -> indexPlan.getModel().getColRef(x).getType())
                        .map(tp -> SparderTypeUtil.toSparkType(tp, false)).collect(Collectors.toList());
                val collect = IntStream.range(0, dimensions.size())
                        .mapToObj(x -> new StructField(String.valueOf(dimensions.get(x)), sparkTypes.get(x), true,
                                Metadata.empty()))
                        .toArray(StructField[]::new);

                var structType = new StructType(collect);
                val needJoin = DFChooser.needJoinLookupTables(seg.getModel(), nSpanningTree);
                val flatTableDesc = new NCubeJoinedFlatTableDesc(indexPlan, seg.getSegRange(), needJoin);
                val nSpanningTree = NSpanningTreeFactory.fromLayouts(indexPlan.getAllLayouts(), dfName);
                for (TblColRef ref : DictionaryBuilderHelper.extractTreeRelatedGlobalDicts(seg,
                        nSpanningTree.getAllIndexEntities())) {
                    int columnIndex = flatTableDesc.getColumnIndex(ref);
                    structType = structType.add(
                            structType.apply(columnIndex).name() + DFBuilderHelper$.MODULE$.ENCODE_SUFFIX(),
                            IntegerType);
                }

                Dataset<Row> ds = ss.createDataFrame(Lists.newArrayList(), structType);

                cuboids.forEach(layout -> {
                    CuboidAggregator.agg(ds, layout.getOrderedDimensions().keySet(), indexPlan.getEffectiveMeasures(),
                            seg, nSpanningTree);

                    NDataLayout dataCuboid = NDataLayout.newDataLayout(seg.getDataflow(), seg.getId(), layout.getId());
                    dataCuboid.setRows(123);
                    dataCuboid.setSourceByteSize(123);
                    dataCuboid.setSourceRows(123);
                    dataCuboid.setBuildJobId(RandomUtil.randomUUIDStr());
                    dataCuboid.setFileCount(123);
                    dataCuboid.setByteSize(123);
                    StorageFactory.createEngineAdapter(layout, NSparkCubingEngine.NSparkCubingStorage.class)
                            .saveTo(NSparkCubingUtil.getStoragePath(seg, layout.getId()), ds, ss);

                    NDataflowUpdate update = new NDataflowUpdate(seg.getDataflow().getUuid());
                    update.setToAddOrUpdateLayouts(dataCuboid);
                    NDataflowManager.getInstance(config, project).updateDataflow(update);

                });

            }

            // mock flat table persist and set segment flatTableReady
            if (config.isPersistFlatTableEnabled()) {
                NDataflow dfCopy = NDataflowManager.getInstance(config, project).getDataflow(dfName).copy();
                NDataflowUpdate update = new NDataflowUpdate(dfName);
                List<NDataSegment> segsToUpdate = Lists.newArrayList();
                for (String segId : segmentIds) {
                    NDataSegment seg = dfCopy.getSegment(segId);
                    seg.setFlatTableReady(true);
                    segsToUpdate.add(seg);
                }
                update.setToUpdateSegs(segsToUpdate.toArray(new NDataSegment[0]));
                NDataflowManager.getInstance(config, project).updateDataflow(update);
            }

        } finally {
            logger.info("Finish build take" + (System.currentTimeMillis() - start) + " ms");
        }
    }

}
