HBASE-17707 New More Accurate Table Skew cost function/generator - revert due to missing JIRA number

hbase-server/src/main/java/org/apache/hadoop/hbase/master/balancer/BaseLoadBalancer.java

@@ -53,7 +53,6 @@ import org.apache.hadoop.hbase.master.RackManager;
 import org.apache.hadoop.hbase.master.RegionPlan;
 import org.apache.hadoop.hbase.master.balancer.BaseLoadBalancer.Cluster.Action.Type;
 import org.apache.hadoop.hbase.security.access.AccessControlLists;
-import org.apache.hadoop.hbase.util.Pair;
 import org.apache.hadoop.util.StringUtils;
 
 import com.google.common.annotations.VisibleForTesting;
@@ -141,7 +140,6 @@ public abstract class BaseLoadBalancer implements LoadBalancer {
     int[]   initialRegionIndexToServerIndex;    //regionIndex -> serverIndex (initial cluster state)
     int[]   regionIndexToTableIndex;     //regionIndex -> tableIndex
     int[][] numRegionsPerServerPerTable; //serverIndex -> tableIndex -> # regions
-    int[]   numRegionsPerTable;          // tableIndex -> number of regions that table has
     int[]   numMaxRegionsPerTable;       //tableIndex -> max number of regions in a single RS
     int[]   regionIndexToPrimaryIndex;   //regionIndex -> regionIndex of the primary
     boolean hasRegionReplicas = false;   //whether there is regions with replicas
@@ -332,7 +330,6 @@ public abstract class BaseLoadBalancer implements LoadBalancer {
 
       numTables = tables.size();
       numRegionsPerServerPerTable = new int[numServers][numTables];
-      numRegionsPerTable = new int[numTables];
 
       for (int i = 0; i < numServers; i++) {
         for (int j = 0; j < numTables; j++) {
@@ -342,7 +339,6 @@ public abstract class BaseLoadBalancer implements LoadBalancer {
 
       for (int i=0; i < regionIndexToServerIndex.length; i++) {
         if (regionIndexToServerIndex[i] >= 0) {
-          numRegionsPerTable[regionIndexToTableIndex[i]]++;
           numRegionsPerServerPerTable[regionIndexToServerIndex[i]][regionIndexToTableIndex[i]]++;
         }
       }
@@ -474,76 +470,6 @@ public abstract class BaseLoadBalancer implements LoadBalancer {
       }
     }
 
-    /**
-     * Returns the minimum number of regions of a table T each server would store if T were
-     * perfectly distributed (i.e. round-robin-ed) across the cluster
-     */
-    public int minRegionsIfEvenlyDistributed(int table) {
-      return numRegionsPerTable[table] / numServers;
-    }
-
-    /**
-     * Returns the maximum number of regions of a table T each server would store if T were
-     * perfectly distributed (i.e. round-robin-ed) across the cluster
-     */
-    public int maxRegionsIfEvenlyDistributed(int table) {
-      int min = minRegionsIfEvenlyDistributed(table);
-      return numRegionsPerTable[table] % numServers == 0 ? min : min + 1;
-    }
-
-    /**
-     * Returns the number of servers that should hold maxRegionsIfEvenlyDistributed for a given
-     * table. A special case here is if maxRegionsIfEvenlyDistributed == minRegionsIfEvenlyDistributed,
-     * in which case all servers should hold the max
-     */
-    public int numServersWithMaxRegionsIfEvenlyDistributed(int table) {
-      int numWithMax = numRegionsPerTable[table] % numServers;
-      if (numWithMax == 0) {
-        return numServers;
-      } else {
-        return numWithMax;
-      }
-    }
-
-    /**
-     * Returns true iff at least one server in the cluster stores either more than the min/max load
-     * per server when all regions are evenly distributed across the cluster
-     */
-    public boolean hasUnevenRegionDistribution() {
-      int minLoad = numRegions / numServers;
-      int maxLoad = numRegions % numServers == 0 ? minLoad : minLoad + 1;
-      for (int server = 0; server < numServers; server++) {
-        int numRegions = getNumRegions(server);
-        if (numRegions > maxLoad || numRegions < minLoad) {
-          return true;
-        }
-      }
-      return false;
-    }
-
-    /**
-     * Returns a pair where the first server is that with the least number of regions across the
-     * cluster and the second server is that with the most number of regions across the cluster
-     */
-    public Pair<Integer, Integer> findLeastAndMostLoadedServers() {
-      int minServer = 0;
-      int maxServer = 0;
-      int minLoad = getNumRegions(minServer);
-      int maxLoad = minLoad;
-      for (int server = 1; server < numServers; server++) {
-        int numRegions = getNumRegions(server);
-        if (numRegions < minLoad) {
-          minServer = server;
-          minLoad = numRegions;
-        }
-        if (numRegions > maxLoad) {
-          maxServer = server;
-          maxLoad = numRegions;
-        }
-      }
-      return Pair.newPair(minServer, maxServer);
-    }
-
     /** An action to move or swap a region */
     public static class Action {
       public static enum Type {

hbase-server/src/main/java/org/apache/hadoop/hbase/master/balancer/StochasticLoadBalancer.java

@@ -18,14 +18,10 @@
 package org.apache.hadoop.hbase.master.balancer;
 
 import java.util.ArrayDeque;
-import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.Collection;
-import java.util.Collections;
-import java.util.Comparator;
 import java.util.Deque;
 import java.util.HashMap;
-import java.util.Iterator;
 import java.util.LinkedList;
 import java.util.List;
 import java.util.Map;
@@ -34,6 +30,7 @@ import java.util.Random;
 
 import org.apache.commons.logging.Log;
 import org.apache.commons.logging.LogFactory;
+import org.apache.hadoop.hbase.classification.InterfaceAudience;
 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.hbase.ClusterStatus;
 import org.apache.hadoop.hbase.HBaseInterfaceAudience;
@@ -43,7 +40,6 @@ import org.apache.hadoop.hbase.RegionLoad;
 import org.apache.hadoop.hbase.ServerLoad;
 import org.apache.hadoop.hbase.ServerName;
 import org.apache.hadoop.hbase.TableName;
-import org.apache.hadoop.hbase.classification.InterfaceAudience;
 import org.apache.hadoop.hbase.master.MasterServices;
 import org.apache.hadoop.hbase.master.RegionPlan;
 import org.apache.hadoop.hbase.master.balancer.BaseLoadBalancer.Cluster.Action;
@@ -53,10 +49,6 @@ import org.apache.hadoop.hbase.master.balancer.BaseLoadBalancer.Cluster.MoveRegi
 import org.apache.hadoop.hbase.master.balancer.BaseLoadBalancer.Cluster.SwapRegionsAction;
 import org.apache.hadoop.hbase.util.Bytes;
 import org.apache.hadoop.hbase.util.EnvironmentEdgeManager;
-import org.apache.hadoop.hbase.util.Pair;
-
-import com.google.common.base.Optional;
-import com.google.common.base.Preconditions;
 
 /**
  * <p>This is a best effort load balancer. Given a Cost function F(C) =&gt; x It will
@@ -927,225 +919,6 @@ public class StochasticLoadBalancer extends BaseLoadBalancer {
     }
   }
 
-  /**
-   * Generates candidate actions to minimize the TableSkew cost function.
-   *
-   * For efficiency reasons, the cluster must be passed in when this generator is
-   * constructed. Every move generated is applied to the cost function
-   * (i.e. it is assumed that every action we generate is applied to the cluster).
-   * This means we can adjust our cost incrementally for the cluster, rather than
-   * recomputing at each iteration.
-   */
-  static class TableSkewCandidateGenerator extends CandidateGenerator {
-
-    // Mapping of table -> true iff too many servers in the cluster store at least
-    // cluster.maxRegionsIfEvenlydistributed(table)
-    boolean[] tablesWithEnoughServersWithMaxRegions = null;
-
-    @Override
-    Action generate(Cluster cluster) {
-      if (tablesWithEnoughServersWithMaxRegions == null || tablesWithEnoughServersWithMaxRegions.length != cluster.numTables) {
-        tablesWithEnoughServersWithMaxRegions = new boolean[cluster.numTables];
-      }
-      if (cluster.hasUnevenRegionDistribution()) {
-        Pair<Integer, Integer> leastAndMostLoadedServers = cluster.findLeastAndMostLoadedServers();
-        return moveFromTableWithEnoughRegions(cluster, leastAndMostLoadedServers.getSecond(), leastAndMostLoadedServers.getFirst());
-      } else {
-        Optional<TableAndServer> tableServer = findSkewedTableServer(cluster);
-        if (!tableServer.isPresent()) {
-          return Cluster.NullAction;
-        }
-        return findBestActionForTableServer(cluster, tableServer.get());
-      }
-    }
-
-    /**
-     * Returns a move fromServer -> toServer such that after the move fromServer will still have at least
-     * the min # regions in terms of table skew calculation
-     */
-    private Action moveFromTableWithEnoughRegions(Cluster cluster, int fromServer, int toServer) {
-      for (int table : getShuffledRangeOfInts(0, cluster.numTables)) {
-        int min = cluster.minRegionsIfEvenlyDistributed(table);
-        if (cluster.numRegionsPerServerPerTable[fromServer][table] > min) {
-          return getAction(fromServer, pickRandomRegionFromTableOnServer(cluster, fromServer, table), toServer, -1);
-        }
-      }
-      return Cluster.NullAction;
-    }
-
-    /**
-     * Picks a random subset of tables, then for each table T checks across cluster and returns first
-     * server (if any) which holds too many regions from T. Returns Optional.absent() if no servers
-     * are found that hold too many regions.
-     */
-    private Optional<TableAndServer> findSkewedTableServer(Cluster cluster) {
-      Optional<TableAndServer> tableServer = Optional.absent();
-      List<Integer> servers = getShuffledRangeOfInts(0, cluster.numServers);
-      Iterator<Integer> tableIter = getShuffledRangeOfInts(0, cluster.numTables).iterator();
-      while (tableIter.hasNext() && !tableServer.isPresent()) {
-        int table = tableIter.next();
-        int maxRegions = cluster.maxRegionsIfEvenlyDistributed(table);
-        int numShouldHaveMaxRegions = cluster.numServersWithMaxRegionsIfEvenlyDistributed(table);
-        int numWithMaxRegions = 0;
-        for (int server : servers) {
-          int numRegions = cluster.numRegionsPerServerPerTable[server][table];
-          // if more than max, server clearly has too many regions
-          if (numRegions > maxRegions) {
-            tableServer = Optional.of(new TableAndServer(table, server));
-            break;
-          }
-          // if equal to max, check to see if we are within acceptable limit
-          if (numRegions == maxRegions) {
-            numWithMaxRegions++;
-          }
-        }
-
-        tablesWithEnoughServersWithMaxRegions[table] = numWithMaxRegions >= numShouldHaveMaxRegions;
-        // If we have found a table with more than max, we are done
-        if (tableServer.isPresent()) {
-          break;
-        }
-
-        // Otherwise, check to see if there are too many servers with maxRegions
-        if (numWithMaxRegions > numShouldHaveMaxRegions) {
-          for (int server : servers) {
-            int numRegions = cluster.numRegionsPerServerPerTable[server][table];
-            if (numRegions == maxRegions) {
-              tableServer = Optional.of(new TableAndServer(table, server));
-              break;
-            }
-          }
-        }
-      }
-
-      return tableServer;
-    }
-
-    /**
-     * Returns an list of integers that stores [upper - lower] unique integers in random order
-     * s.t. for each integer i lower <= i < upper
-     */
-    private List<Integer> getShuffledRangeOfInts(int lower, int upper) {
-      Preconditions.checkArgument(lower < upper);
-      ArrayList<Integer> arr = new ArrayList<Integer>(upper - lower);
-      for (int i = lower; i < upper; i++) {
-        arr.add(i);
-      }
-      Collections.shuffle(arr);
-      return arr;
-    }
-
-    /**
-     * Pick a random region from the specified server and table. Returns -1 if no regions from
-     * the given table lie on the given server
-     */
-    protected int pickRandomRegionFromTableOnServer(Cluster cluster, int server, int table) {
-      if (server < 0 || table < 0) {
-        return -1;
-      }
-      List<Integer> regionsFromTable = new ArrayList<>();
-      for (int region : cluster.regionsPerServer[server]) {
-        if (cluster.regionIndexToTableIndex[region] == table) {
-          regionsFromTable.add(region);
-        }
-      }
-      return regionsFromTable.get(RANDOM.nextInt(regionsFromTable.size()));
-    }
-
-    /**
-     * Returns servers in the cluster that store fewer than k regions for the given table (sorted by
-     * servers with the fewest regions from givenTable first)
-     */
-    public List<Integer> getServersWithFewerThanKRegionsFromTable(final Cluster cluster, final int givenTable, int k) {
-      List<Integer> serversWithFewerThanK = new ArrayList<>();
-      for (int server = 0; server < cluster.numServers; server++) {
-        if (cluster.numRegionsPerServerPerTable[server][givenTable] < k) {
-          serversWithFewerThanK.add(server);
-        }
-      }
-      Collections.sort(serversWithFewerThanK, new Comparator<Integer>() {
-        @Override
-        public int compare(Integer o1, Integer o2) {
-          return cluster.numRegionsPerServerPerTable[o1.intValue()][givenTable] - cluster.numRegionsPerServerPerTable[o2.intValue()][givenTable];
-        }
-      });
-      return serversWithFewerThanK;
-    }
-
-    /**
-     * Given a table T for which server S stores too many regions, attempts to find a
-     * SWAP operation that will better balance the cluster
-     */
-    public Action findBestActionForTableServer(Cluster cluster, TableAndServer tableServer) {
-      int fromTable = tableServer.getTable();
-      int fromServer = tableServer.getServer();
-
-      int minNumRegions = cluster.minRegionsIfEvenlyDistributed(fromTable);
-      int maxNumRegions = cluster.maxRegionsIfEvenlyDistributed(fromTable);
-      List<Integer> servers;
-      if (tablesWithEnoughServersWithMaxRegions[fromTable]) {
-        servers = getServersWithFewerThanKRegionsFromTable(cluster, fromTable, minNumRegions);
-      } else {
-        servers = getServersWithFewerThanKRegionsFromTable(cluster, fromTable, maxNumRegions);
-      }
-
-      if (servers.isEmpty()) {
-        return Cluster.NullAction;
-      }
-
-      Optional<Action> swap = trySwap(cluster, fromServer, fromTable, servers);
-      if (swap.isPresent()) {
-        return swap.get();
-      }
-
-      // If we cannot perform a swap, we should do nothing
-      return Cluster.NullAction;
-    }
-
-    /**
-     * Given server1, table1, we try to find server2 and table2 such that
-     * at least 3 of the following 4 criteria are met
-     *
-     * 1) server1 has too many regions of table1
-     * 2) server1 has too few regions of table2
-     * 3) server2 has too many regions of table2
-     * 4) server2 has too few regions of table1
-     *
-     * We consider N regions from table T
-     *    too few if: N < cluster.minRegionsIfEvenlyDistributed(T)
-     *    too many if: N > cluster.maxRegionsIfEvenlyDistributed(T)
-     *
-     * Because (1) and (4) are true apriori, we only need to check for (2) and (3).
-     *
-     * If 3 of the 4 criteria are met, we return a swap operation between
-     * randomly selected regions from table1 on server1 and from table2 on server2.
-     *
-     * Optional.absent() is returned if we could not find such a SWAP.
-     */
-    private Optional<Action> trySwap(Cluster cluster, int server1, int table1, List<Integer> candidateServers) {
-      // Because conditions (1) and (4) are true apriori, we only need to meet one of conditions (2) or (3)
-      List<Integer> tables = getShuffledRangeOfInts(0, cluster.numTables);
-      for (int table2 : tables) {
-        int minRegions = cluster.minRegionsIfEvenlyDistributed(table2);
-        int maxRegions = cluster.maxRegionsIfEvenlyDistributed(table2);
-        for (int server2 : candidateServers) {
-          int numRegions1 = cluster.numRegionsPerServerPerTable[server1][table2];
-          int numRegions2 = cluster.numRegionsPerServerPerTable[server2][table2];
-          if (numRegions2 == 0) {
-            continue;
-          }
-          if ((numRegions1 < minRegions || numRegions2 > maxRegions) ||
-              (minRegions != maxRegions && numRegions1 == minRegions && numRegions2 == maxRegions)) {
-            int region1 = pickRandomRegionFromTableOnServer(cluster, server1, table1);
-            int region2 = pickRandomRegionFromTableOnServer(cluster, server2, table2);
-            return Optional.of(getAction(server1, region1, server2, region2));
-          }
-        }
-      }
-      return Optional.absent();
-    }
-  }
-
   /**
    * Base class of StochasticLoadBalancer's Cost Functions.
    */
@@ -1193,7 +966,8 @@ public class StochasticLoadBalancer extends BaseLoadBalancer {
         break;
       case SWAP_REGIONS:
         SwapRegionsAction a = (SwapRegionsAction) action;
-        regionSwapped(a.fromRegion, a.fromServer, a.toRegion, a.toServer);
+        regionMoved(a.fromRegion, a.fromServer, a.toServer);
+        regionMoved(a.toRegion, a.toServer, a.fromServer);
         break;
       default:
         throw new RuntimeException("Uknown action:" + action.type);
@@ -1203,11 +977,6 @@ public class StochasticLoadBalancer extends BaseLoadBalancer {
     protected void regionMoved(int region, int oldServer, int newServer) {
     }
 
-    protected void regionSwapped(int region1, int server1, int region2, int server2) {
-      regionMoved(region1, server1, server2);
-      regionMoved(region2, server2, server1);
-    }
-
     abstract double cost();
 
     /**
@@ -1401,188 +1170,9 @@ public class StochasticLoadBalancer extends BaseLoadBalancer {
         "hbase.master.balancer.stochastic.tableSkewCost";
     private static final float DEFAULT_TABLE_SKEW_COST = 35;
 
-    /**
-     * Ranges from 0.0 to 1.0 and is the proportion of how much the most skewed table
-     * (as opposed to the average skew across all tables) should affect TableSkew cost
-     */
-    private static final String MAX_TABLE_SKEW_WEIGHT_KEY =
-        "hbase.master.balancer.stochastic.maxTableSkewWeight";
-    private float DEFAULT_MAX_TABLE_SKEW_WEIGHT = 0.0f;
-
-    private final float maxTableSkewWeight;
-    private final float avgTableSkewWeight;
-
-    // Number of moves for each table required to bring the cluster to a perfectly balanced
-    // state (i.e. as if you had round-robin-ed regions across cluster)
-    private int[] numMovesPerTable;
-
     TableSkewCostFunction(Configuration conf) {
       super(conf);
       this.setMultiplier(conf.getFloat(TABLE_SKEW_COST_KEY, DEFAULT_TABLE_SKEW_COST));
-      maxTableSkewWeight = conf.getFloat(MAX_TABLE_SKEW_WEIGHT_KEY, DEFAULT_MAX_TABLE_SKEW_WEIGHT);
-      Preconditions.checkArgument(0.0 <= maxTableSkewWeight && maxTableSkewWeight <= 1.0);
-      avgTableSkewWeight = 1 - maxTableSkewWeight;
-    }
-
-    /**
-     * Computes cost by:
-     *
-     * 1) Computing a skew score for each table (based on the number of regions
-     * from that table that would have to be moved to reach an evenly balanced state)
-     *
-     * 2) Taking a weighted average of the highest skew score with the average skew score
-     *
-     * 3) Square rooting that value to more evenly distribute the values between 0-1
-     * (since we have observed they are generally very small).
-     *
-     * @return the table skew cost for the cluster
-     */
-    @Override
-    double cost() {
-      double[] skewPerTable = computeSkewPerTable();
-      if (skewPerTable.length == 0) {
-        return 0;
-      }
-      double maxTableSkew = max(skewPerTable);
-      double avgTableSkew = average(skewPerTable);
-
-      return Math.sqrt(maxTableSkewWeight * maxTableSkew + avgTableSkewWeight * avgTableSkew);
-    }
-
-    @Override
-    void init(Cluster cluster) {
-      super.init(cluster);
-      numMovesPerTable = computeNumMovesPerTable();
-    }
-
-    /**
-     * Adjusts computed number of moves after two regions have been swapped
-     */
-    @Override
-    protected void regionSwapped(int region1, int server1, int region2, int server2) {
-      // If different tables, simply perform two moves
-      if (cluster.regionIndexToTableIndex[region1] != cluster.regionIndexToTableIndex[region2]) {
-        super.regionSwapped(region1, server1, region2, server2);
-        return;
-      }
-      // If same table, do nothing
-    }
-
-    /**
-     * Adjusts computed number of moves per table after a region has been moved
-     */
-    @Override
-    protected void regionMoved(int region, int oldServer, int newServer) {
-      int table = cluster.regionIndexToTableIndex[region];
-      numMovesPerTable[table] = computeNumMovesForTable(table);
-    }
-
-    /**
-     * Returns a mapping of table -> numMoves, where numMoves is the number of regions required to bring
-     * each table to a fully balanced state (i.e. as if its regions had been round-robin-ed across the cluster).
-     */
-    private int[] computeNumMovesPerTable() {
-      // Determine # region moves required for each table to have regions perfectly distributed across cluster
-      int[] numMovesPerTable = new int[cluster.numTables];
-      for (int table = 0; table < cluster.numTables; table++) {
-        numMovesPerTable[table] = computeNumMovesForTable(table);
-      }
-      return numMovesPerTable;
-    }
-
-    /**
-     * Computes the number of moves required across all servers to bring the given table to a balanced state
-     * (i.e. as if its regions had been round-robin-ed across the cluster). We only consider moves as # of regions
-     * that need to be sent, not received, so that we do not double count region moves.
-     */
-    private int computeNumMovesForTable(int table) {
-      int numMinRegions = cluster.minRegionsIfEvenlyDistributed(table);
-      int numMaxRegions = cluster.maxRegionsIfEvenlyDistributed(table);
-      int numMaxServersRemaining = cluster.numServersWithMaxRegionsIfEvenlyDistributed(table);
-      int numMoves = 0;
-
-      for (int server = 0; server < cluster.numServers; server++) {
-        int numRegions = cluster.numRegionsPerServerPerTable[server][table];
-        if (numRegions >= numMaxRegions && numMaxServersRemaining > 0) {
-          numMoves += numRegions - numMaxRegions;
-          numMaxServersRemaining--;
-        } else if (numRegions > numMinRegions) {
-          numMoves += numRegions - numMinRegions;
-        }
-      }
-      return numMoves;
-    }
-
-    /**
-     * Returns mapping of tableIndex -> tableSkewScore, where tableSkewScore is a double between 0 to 1 with
-     * 0 indicating no table skew (i.e. perfect distribution of regions among servers), and 1 representing
-     * pathological table skew (i.e. all of a servers regions belonging to one table).
-     */
-    private double[] computeSkewPerTable() {
-      if (numMovesPerTable == null) {
-        numMovesPerTable = computeNumMovesPerTable();
-      }
-      double[] scaledSkewPerTable = new double[numMovesPerTable.length];
-      for (int table = 0; table < numMovesPerTable.length; table++) {
-        int numTotalRegions = cluster.numRegionsPerTable[table];
-        int maxRegions = cluster.maxRegionsIfEvenlyDistributed(table);
-        int pathologicalNumMoves = numTotalRegions - maxRegions;
-        scaledSkewPerTable[table] = pathologicalNumMoves == 0 ? 0 : (double) numMovesPerTable[table] / pathologicalNumMoves;
-      }
-      return scaledSkewPerTable;
-    }
-
-    /**
-     * Returns the max of the values in the passed array
-     */
-    private double max(double[] arr) {
-      double max = arr[0];
-      for (double d : arr) {
-        if (d > max) {
-          max = d;
-        }
-      }
-      return max;
-    }
-
-    /**
-     * Returns the average of the values in the passed array
-     */
-    private double average(double[] arr) {
-      double sum = 0;
-      for (double d : arr) {
-        sum += d;
-      }
-      return sum / arr.length;
-    }
-  }
-
-  /**
-   * Compute the cost of a potential cluster configuration based upon how evenly
-   * distributed tables are.
-   *
-   * @deprecated replaced by TableSkewCostFunction
-   * This function only considers the maximum # of regions of each table stored
-   * on any one server. This, however, neglects a number of cases. Consider the case
-   * where N servers store 1 more region than as if the regions had been round robin-ed
-   * across the cluster, but then K servers stored 0 regions of the table. The maximum
-   * # regions stored would not properly reflect the table-skew of the cluster.
-   *
-   * Furthermore, this relies upon the cluster.numMaxRegionsPerTable field, which is not
-   * properly updated. The values per table only increase as the cluster shifts (i.e.
-   * as new maxima are found), but they do not go down when the maximum skew decreases
-   * for a particular table.
-   */
-  @Deprecated
-  static class OldTableSkewCostFunction extends CostFunction {
-
-    private static final String TABLE_SKEW_COST_KEY =
-        "hbase.master.balancer.stochastic.tableSkewCost";
-    private static final float DEFAULT_TABLE_SKEW_COST = 35;
-
-    OldTableSkewCostFunction(Configuration conf) {
-      super(conf);
-      this.setMultiplier(conf.getFloat(TABLE_SKEW_COST_KEY, DEFAULT_TABLE_SKEW_COST));
     }
 
     @Override
@@ -1998,32 +1588,10 @@ public class StochasticLoadBalancer extends BaseLoadBalancer {
     }
   }
 
-  /**
-   * Data structure that holds table and server indexes
-   */
-  static class TableAndServer {
-    private final int table;
-    private final int server;
-
-    public TableAndServer(int table, int server) {
-      this.table = table;
-      this.server = server;
-    }
-
-    public int getTable() {
-      return table;
-    }
-
-    public int getServer() {
-      return server;
-    }
-  }
-
   /**
    * A helper function to compose the attribute name from tablename and costfunction name
    */
   public static String composeAttributeName(String tableName, String costFunctionName) {
     return tableName + TABLE_FUNCTION_SEP + costFunctionName;
   }
-
 }

hbase-server/src/test/java/org/apache/hadoop/hbase/master/balancer/TestStochasticLoadBalancer.java

@@ -48,8 +48,6 @@ import org.apache.hadoop.hbase.master.MockNoopMasterServices;
 import org.apache.hadoop.hbase.master.RackManager;
 import org.apache.hadoop.hbase.master.RegionPlan;
 import org.apache.hadoop.hbase.master.balancer.BaseLoadBalancer.Cluster;
-import org.apache.hadoop.hbase.master.balancer.StochasticLoadBalancer.CandidateGenerator;
-import org.apache.hadoop.hbase.master.balancer.StochasticLoadBalancer.TableSkewCandidateGenerator;
 import org.apache.hadoop.hbase.testclassification.FlakeyTests;
 import org.apache.hadoop.hbase.testclassification.MediumTests;
 import org.apache.hadoop.hbase.util.Bytes;
@@ -121,9 +119,7 @@ public class TestStochasticLoadBalancer extends BalancerTestBase {
    */
   @Test
   public void testBalanceCluster() throws Exception {
-    float oldMinCostNeedBalance = conf.getFloat(StochasticLoadBalancer.MIN_COST_NEED_BALANCE_KEY, 0.05f);
+
-    conf.setFloat(StochasticLoadBalancer.MIN_COST_NEED_BALANCE_KEY, 0.02f);
-    loadBalancer.setConf(conf);
     for (int[] mockCluster : clusterStateMocks) {
       Map<ServerName, List<HRegionInfo>> servers = mockClusterServers(mockCluster);
       List<ServerAndLoad> list = convertToList(servers);
@@ -139,9 +135,6 @@ public class TestStochasticLoadBalancer extends BalancerTestBase {
         returnServer(entry.getKey());
       }
     }
-    // reset config
-    conf.setFloat(StochasticLoadBalancer.MIN_COST_NEED_BALANCE_KEY, oldMinCostNeedBalance);
-    loadBalancer.setConf(conf);
   }
 
   @Test
@@ -262,32 +255,6 @@ public class TestStochasticLoadBalancer extends BalancerTestBase {
     assertEquals(2.5, result, 0.01);
   }
 
-  @Test (timeout=60000)
-  public void testTableSkewCandidateGeneratorConvergesToZero() {
-    int replication = 1;
-    StochasticLoadBalancer.CostFunction
-        costFunction = new StochasticLoadBalancer.TableSkewCostFunction(conf);
-    CandidateGenerator generator = new TableSkewCandidateGenerator();
-    for (int i = 0; i < 100; i++) {
-      int numNodes = rand.nextInt(500) + 1; // num nodes between 1 - 500
-      int numTables = rand.nextInt(500) + 1; // num tables between 1 and 1000
-      int numRegions = rand.nextInt(numTables * 99) + Math.max(numTables, numNodes); // num regions between max(numTables, numNodes) - numTables*100
-      int numRegionsPerServer = rand.nextInt(numRegions / numNodes) + 1; // num regions per server (except one) between 1 and numRegions / numNodes
-
-      Map<ServerName, List<HRegionInfo>> serverMap = createServerMap(numNodes, numRegions, numRegionsPerServer, replication, numTables);
-      BaseLoadBalancer.Cluster cluster = new Cluster(serverMap, null, null, null);
-      costFunction.init(cluster);
-      double cost = costFunction.cost();
-      while (cost > 0) {
-        Cluster.Action action = generator.generate(cluster);
-        cluster.doAction(action);
-        costFunction.postAction(action);
-        cost = costFunction.cost();
-      }
-      assertEquals(0, cost, .000000000001);
-    }
-  }
-
   @Test
   public void testCostFromArray() {
     Configuration conf = HBaseConfiguration.create();

hbase-server/src/test/java/org/apache/hadoop/hbase/master/balancer/TestStochasticLoadBalancer2.java

@@ -35,7 +35,6 @@ public class TestStochasticLoadBalancer2 extends BalancerTestBase {
     conf.setFloat("hbase.master.balancer.stochastic.maxMovePercent", 1.0f);
     conf.setLong(StochasticLoadBalancer.MAX_STEPS_KEY, 2000000L);
     conf.setFloat("hbase.master.balancer.stochastic.localityCost", 0);
-
     conf.setLong("hbase.master.balancer.stochastic.maxRunningTime", 90 * 1000); // 90 sec
     conf.setFloat("hbase.master.balancer.stochastic.minCostNeedBalance", 0.05f);
     loadBalancer.setConf(conf);
@@ -71,7 +70,6 @@ public class TestStochasticLoadBalancer2 extends BalancerTestBase {
   public void testRegionReplicasOnMidClusterHighReplication() {
     conf.setLong(StochasticLoadBalancer.MAX_STEPS_KEY, 4000000L);
     conf.setLong("hbase.master.balancer.stochastic.maxRunningTime", 120 * 1000); // 120 sec
-    conf.setFloat("hbase.master.balancer.stochastic.tableSkewCost", 4);
     loadBalancer.setConf(conf);
     int numNodes = 80;
     int numRegions = 6 * numNodes;
@@ -79,8 +77,6 @@ public class TestStochasticLoadBalancer2 extends BalancerTestBase {
     int numRegionsPerServer = 5;
     int numTables = 10;
     testWithCluster(numNodes, numRegions, numRegionsPerServer, replication, numTables, false, true);
-    // reset config
-    conf.setFloat("hbase.master.balancer.stochastic.tableSkewCost", 35);
   }
 
   @Test (timeout = 800000)