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/*
 * Druid - a distributed column store.
 * Copyright (C) 2012, 2013  Metamarkets Group Inc.
 *
 * This program is free software; you can redistribute it and/or
 * modify it under the terms of the GNU General Public License
 * as published by the Free Software Foundation; either version 2
 * of the License, or (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, write to the Free Software
 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 */

package io.druid.server.coordinator;

import com.metamx.common.Pair;
import com.metamx.emitter.EmittingLogger;
import io.druid.timeline.DataSegment;
import org.joda.time.DateTime;
import org.joda.time.Interval;

import java.util.List;

public class CostBalancerStrategy implements BalancerStrategy
{
  private static final EmittingLogger log = new EmittingLogger(CostBalancerStrategy.class);
  private static final int DAY_IN_MILLIS = 1000 * 60 * 60 * 24;
  private static final int SEVEN_DAYS_IN_MILLIS = 7 * DAY_IN_MILLIS;
  private static final int THIRTY_DAYS_IN_MILLIS = 30 * DAY_IN_MILLIS;
  private final DateTime referenceTimestamp;

  public CostBalancerStrategy(DateTime referenceTimestamp)
  {
    this.referenceTimestamp = referenceTimestamp;
  }

  @Override
  public ServerHolder findNewSegmentHomeReplicator(
      DataSegment proposalSegment, List serverHolders
  )
  {
    ServerHolder holder = chooseBestServer(proposalSegment, serverHolders, false).rhs;
    if (holder != null && !holder.isServingSegment(proposalSegment)) {
      return holder;
    }
    return null;
  }


  @Override
  public ServerHolder findNewSegmentHomeBalancer(
      DataSegment proposalSegment, List serverHolders
  )
  {
    return chooseBestServer(proposalSegment, serverHolders, true).rhs;
  }


  /**
   * For assignment, we want to move to the lowest cost server that isn't already serving the segment.
   *
   * @param proposalSegment A DataSegment that we are proposing to move.
   * @param serverHolders   An iterable of ServerHolders for a particular tier.
   *
   * @return A ServerHolder with the new home for a segment.
   */

  private Pair chooseBestServer(
      final DataSegment proposalSegment,
      final Iterable serverHolders,
      boolean includeCurrentServer
  )
  {

    Pair bestServer = Pair.of(Double.POSITIVE_INFINITY, null);
    final long proposalSegmentSize = proposalSegment.getSize();

    for (ServerHolder server : serverHolders) {
      if (includeCurrentServer || !server.isServingSegment(proposalSegment)) {
        /** Don't calculate cost if the server doesn't have enough space or is loading the segment */
        if (proposalSegmentSize > server.getAvailableSize() || server.isLoadingSegment(proposalSegment)) {
          continue;
        }

        /** The contribution to the total cost of a given server by proposing to move the segment to that server is... */
        double cost = 0f;
        /**  the sum of the costs of other (exclusive of the proposalSegment) segments on the server */
        for (DataSegment segment : server.getServer().getSegments().values()) {
          if (!proposalSegment.equals(segment)) {
            cost += computeJointSegmentCosts(proposalSegment, segment);
          }
        }
        /**  plus the costs of segments that will be loaded */
        for (DataSegment segment : server.getPeon().getSegmentsToLoad()) {
          cost += computeJointSegmentCosts(proposalSegment, segment);
        }

        if (cost < bestServer.lhs) {
          bestServer = Pair.of(cost, server);
        }
      }
    }

    return bestServer;
  }

  /**
   * This defines the unnormalized cost function between two segments.  There is a base cost given by
   * the minimum size of the two segments and additional penalties.
   * recencyPenalty: it is more likely that recent segments will be queried together
   * dataSourcePenalty: if two segments belong to the same data source, they are more likely to be involved
   * in the same queries
   * gapPenalty: it is more likely that segments close together in time will be queried together
   *
   * @param segment1 The first DataSegment.
   * @param segment2 The second DataSegment.
   *
   * @return The joint cost of placing the two DataSegments together on one node.
   */
  public double computeJointSegmentCosts(final DataSegment segment1, final DataSegment segment2)
  {
    final Interval gap = segment1.getInterval().gap(segment2.getInterval());

    final double baseCost = Math.min(segment1.getSize(), segment2.getSize());
    double recencyPenalty = 1;
    double dataSourcePenalty = 1;
    double gapPenalty = 1;

    if (segment1.getDataSource().equals(segment2.getDataSource())) {
      dataSourcePenalty = 2;
    }

    double maxDiff = Math.max(
        referenceTimestamp.getMillis() - segment1.getInterval().getEndMillis(),
        referenceTimestamp.getMillis() - segment2.getInterval().getEndMillis()
    );
    double segment1diff = referenceTimestamp.getMillis() - segment1.getInterval().getEndMillis();
    double segment2diff = referenceTimestamp.getMillis() - segment2.getInterval().getEndMillis();
    if (segment1diff < SEVEN_DAYS_IN_MILLIS && segment2diff < SEVEN_DAYS_IN_MILLIS) {
      recencyPenalty = (2 - segment1diff / SEVEN_DAYS_IN_MILLIS) * (2 - segment2diff / SEVEN_DAYS_IN_MILLIS);
    }

    /** gap is null if the two segment intervals overlap or if they're adjacent */
    if (gap == null) {
      gapPenalty = 2;
    } else {
      long gapMillis = gap.toDurationMillis();
      if (gapMillis < THIRTY_DAYS_IN_MILLIS) {
        gapPenalty = 2 - gapMillis / THIRTY_DAYS_IN_MILLIS;
      }
    }

    final double cost = baseCost * recencyPenalty * dataSourcePenalty * gapPenalty;

    return cost;
  }

  public BalancerSegmentHolder pickSegmentToMove(final List serverHolders)
  {
    ReservoirSegmentSampler sampler = new ReservoirSegmentSampler();
    return sampler.getRandomBalancerSegmentHolder(serverHolders);
  }

  /**
   * Calculates the initial cost of the Druid segment configuration.
   *
   * @param serverHolders A list of ServerHolders for a particular tier.
   *
   * @return The initial cost of the Druid tier.
   */
  public double calculateInitialTotalCost(final List serverHolders)
  {
    double cost = 0;
    for (ServerHolder server : serverHolders) {
      DataSegment[] segments = server.getServer().getSegments().values().toArray(new DataSegment[]{});
      for (int i = 0; i < segments.length; ++i) {
        for (int j = i; j < segments.length; ++j) {
          cost += computeJointSegmentCosts(segments[i], segments[j]);
        }
      }
    }
    return cost;
  }

  /**
   * Calculates the cost normalization.  This is such that the normalized cost is lower bounded
   * by 1 (e.g. when each segment gets its own historical node).
   *
   * @param serverHolders A list of ServerHolders for a particular tier.
   *
   * @return The normalization value (the sum of the diagonal entries in the
   *         pairwise cost matrix).  This is the cost of a cluster if each
   *         segment were to get its own historical node.
   */
  public double calculateNormalization(final List serverHolders)
  {
    double cost = 0;
    for (ServerHolder server : serverHolders) {
      for (DataSegment segment : server.getServer().getSegments().values()) {
        cost += computeJointSegmentCosts(segment, segment);
      }
    }
    return cost;
  }

  @Override
  public void emitStats(
      String tier,
      CoordinatorStats stats, List serverHolderList
  )
  {
    final double initialTotalCost = calculateInitialTotalCost(serverHolderList);
    final double normalization = calculateNormalization(serverHolderList);
    final double normalizedInitialCost = initialTotalCost / normalization;

    stats.addToTieredStat("initialCost", tier, (long) initialTotalCost);
    stats.addToTieredStat("normalization", tier, (long) normalization);
    stats.addToTieredStat("normalizedInitialCostTimesOneThousand", tier, (long) (normalizedInitialCost * 1000));

    log.info(
        "[%s]: Initial Total Cost: [%f], Normalization: [%f], Initial Normalized Cost: [%f]",
        tier,
        initialTotalCost,
        normalization,
        normalizedInitialCost
    );

  }
}




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