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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.cassandra.test.microbench;
  
  import java.nio.ByteBuffer;
  import java.util.Arrays;
  import java.util.concurrent.ThreadLocalRandom;
  import java.util.concurrent.TimeUnit;
  
  import org.apache.cassandra.db.Clustering;
  import org.apache.cassandra.db.ClusteringBound;
  import org.apache.cassandra.db.ClusteringBoundary;
  import org.apache.cassandra.db.ClusteringPrefix.Kind;
  import org.apache.cassandra.db.marshal.LongType;
  import org.apache.cassandra.db.rows.BufferCell;
  import org.apache.cassandra.db.rows.Cell;
  import org.apache.cassandra.io.sstable.metadata.MetadataCollector;
  import org.apache.cassandra.schema.ColumnMetadata;
  import org.apache.cassandra.schema.TableMetadata;
  import org.jctools.util.Pow2;
  import org.openjdk.jmh.annotations.*;
  
  @BenchmarkMode(Mode.AverageTime)
  @OutputTimeUnit(TimeUnit.NANOSECONDS)
  @Warmup(iterations = 10, time = 1)
  @Measurement(iterations = 10, time = 1)
  @Fork(1)
  @State(Scope.Benchmark)
  public class MetadataCollectorBench
  {
      @Param({ "10" })
      int clusteringKeyNum;
  
      @Param({ "10000" })
      int datasetSize;
      int datumIndex;
      Cell<?>[] cells;
      Clustering<?>[] clusterings;
      ClusteringBound<?>[] clusteringBounds;
      ClusteringBoundary<?>[] clusteringBoundaries;
      MetadataCollector collector;
  
      @Setup
      public void setup()
      {
          TableMetadata.Builder tableMetadataBuilder = TableMetadata.builder("k", "t")
                                                                    .addPartitionKeyColumn("pk", LongType.instance)
                                                                    .addRegularColumn("rc", LongType.instance);
          for (int i = 0; i < clusteringKeyNum; i++)
              tableMetadataBuilder.addClusteringColumn("ck" + i, LongType.instance);
          TableMetadata tableMetadata = tableMetadataBuilder.build();
          collector = new MetadataCollector(tableMetadata.comparator);
  
          ColumnMetadata columnMetadata = tableMetadata.regularColumns().iterator().next();
          ThreadLocalRandom current = ThreadLocalRandom.current();
          datasetSize = Pow2.roundToPowerOfTwo(datasetSize);
          cells = new Cell[datasetSize];
          for (int i = 0; i < datasetSize; i++)
          {
              cells[i] = new BufferCell(columnMetadata, current.nextLong(0, Long.MAX_VALUE), current.nextInt(1, Integer.MAX_VALUE), Cell.NO_DELETION_TIME, null, null);
          }
          clusterings = new Clustering[datasetSize];
          clusteringBounds = new ClusteringBound[datasetSize];
          clusteringBoundaries = new ClusteringBoundary[datasetSize];
          ByteBuffer[] cks = new ByteBuffer[clusteringKeyNum];
          Kind[] clusteringBoundKinds = new Kind[]{ Kind.INCL_START_BOUND, Kind.INCL_END_BOUND, Kind.EXCL_START_BOUND, Kind.EXCL_END_BOUND };
          Kind[] clusteringBoundaryKinds = new Kind[]{ Kind.INCL_END_EXCL_START_BOUNDARY, Kind.EXCL_END_INCL_START_BOUNDARY };
          for (int i = 0; i < datasetSize; i++)
          {
              for (int j = 0; j < clusteringKeyNum; j++)
                  cks[j] = LongType.instance.decompose(current.nextLong());
              clusterings[i] = Clustering.make(Arrays.copyOf(cks, cks.length));
              clusteringBounds[i] = ClusteringBound.create(clusteringBoundKinds[i % clusteringBoundKinds.length], clusterings[i]);
              clusteringBoundaries[i] = ClusteringBoundary.create(clusteringBoundaryKinds[i % clusteringBoundaryKinds.length], clusterings[i]);
          }
  
          System.gc();
          // shuffle array contents to ensure a more 'natural' layout
          for (int i = 0; i < datasetSize; i++)
          {
              int to = current.nextInt(0, datasetSize);
              Cell<?> temp = cells[i];
              cells[i] = cells[to];
              cells[to] = temp;
          }
  
          for (int i = 0; i < datasetSize; i++)
          {
              int to = current.nextInt(0, datasetSize);
              Clustering<?> temp = clusterings[i];
              clusterings[i] = clusterings[to];
              clusterings[to] = temp;
          }
      }
  
      @Benchmark
      public void updateCell()
      {
          collector.update(nextCell());
      }
  
      @Benchmark
      public void updateClustering()
      {
          collector.updateClusteringValues(nextClustering());
      }
  
      @Benchmark
      public void updateClusteringBound()
      {
          collector.updateClusteringValuesByBoundOrBoundary(nextClusteringBound());
      }
  
      @Benchmark
      public void updateClusteringBoundary()
      {
          collector.updateClusteringValuesByBoundOrBoundary(nextClusteringBoundary());
      }
  
      public Cell<?> nextCell()
      {
          return cells[datumIndex++ & (cells.length - 1)];
      }
  
      public Clustering<?> nextClustering()
      {
          return clusterings[datumIndex++ & (clusterings.length - 1)];
      }
  
      public ClusteringBound<?> nextClusteringBound()
      {
          return clusteringBounds[datumIndex++ & (clusteringBounds.length - 1)];
      }
  
      public ClusteringBoundary<?> nextClusteringBoundary()
      {
          return clusteringBoundaries[datumIndex++ & (clusteringBoundaries.length - 1)];
      }
  }