HBASE-17707 New More Accurate Table Skew cost function/generator
This reverts commit 3b914df949.
Signed-off-by: tedyu <yuzhihong@gmail.com>
This commit is contained in:
Kahlil Oppenheimer
tedyu
parent
dfc6cf3076
commit
93b0cdeaaf
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@ -53,6 +53,7 @@ import org.apache.hadoop.hbase.master.RackManager;
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import org.apache.hadoop.hbase.master.RegionPlan;
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import org.apache.hadoop.hbase.master.balancer.BaseLoadBalancer.Cluster.Action.Type;
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import org.apache.hadoop.hbase.security.access.AccessControlLists;
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import org.apache.hadoop.hbase.util.Pair;
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import org.apache.hadoop.util.StringUtils;
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import com.google.common.annotations.VisibleForTesting;
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@ -140,6 +141,7 @@ public abstract class BaseLoadBalancer implements LoadBalancer {
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int[] initialRegionIndexToServerIndex; //regionIndex -> serverIndex (initial cluster state)
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int[] regionIndexToTableIndex; //regionIndex -> tableIndex
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int[][] numRegionsPerServerPerTable; //serverIndex -> tableIndex -> # regions
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int[] numRegionsPerTable; // tableIndex -> number of regions that table has
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int[] numMaxRegionsPerTable; //tableIndex -> max number of regions in a single RS
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int[] regionIndexToPrimaryIndex; //regionIndex -> regionIndex of the primary
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boolean hasRegionReplicas = false; //whether there is regions with replicas
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@ -330,6 +332,7 @@ public abstract class BaseLoadBalancer implements LoadBalancer {
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numTables = tables.size();
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numRegionsPerServerPerTable = new int[numServers][numTables];
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numRegionsPerTable = new int[numTables];
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for (int i = 0; i < numServers; i++) {
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for (int j = 0; j < numTables; j++) {
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@ -339,6 +342,7 @@ public abstract class BaseLoadBalancer implements LoadBalancer {
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for (int i=0; i < regionIndexToServerIndex.length; i++) {
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if (regionIndexToServerIndex[i] >= 0) {
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numRegionsPerTable[regionIndexToTableIndex[i]]++;
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numRegionsPerServerPerTable[regionIndexToServerIndex[i]][regionIndexToTableIndex[i]]++;
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}
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}
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@ -470,6 +474,76 @@ public abstract class BaseLoadBalancer implements LoadBalancer {
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}
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}
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/**
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* Returns the minimum number of regions of a table T each server would store if T were
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* perfectly distributed (i.e. round-robin-ed) across the cluster
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*/
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public int minRegionsIfEvenlyDistributed(int table) {
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return numRegionsPerTable[table] / numServers;
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}
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/**
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* Returns the maximum number of regions of a table T each server would store if T were
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* perfectly distributed (i.e. round-robin-ed) across the cluster
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*/
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public int maxRegionsIfEvenlyDistributed(int table) {
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int min = minRegionsIfEvenlyDistributed(table);
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return numRegionsPerTable[table] % numServers == 0 ? min : min + 1;
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}
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/**
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* Returns the number of servers that should hold maxRegionsIfEvenlyDistributed for a given
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* table. A special case here is if maxRegionsIfEvenlyDistributed == minRegionsIfEvenlyDistributed,
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* in which case all servers should hold the max
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*/
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public int numServersWithMaxRegionsIfEvenlyDistributed(int table) {
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int numWithMax = numRegionsPerTable[table] % numServers;
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if (numWithMax == 0) {
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return numServers;
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} else {
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return numWithMax;
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}
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}
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/**
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* Returns true iff at least one server in the cluster stores either more than the min/max load
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* per server when all regions are evenly distributed across the cluster
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*/
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public boolean hasUnevenRegionDistribution() {
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int minLoad = numRegions / numServers;
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int maxLoad = numRegions % numServers == 0 ? minLoad : minLoad + 1;
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for (int server = 0; server < numServers; server++) {
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int numRegions = getNumRegions(server);
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if (numRegions > maxLoad || numRegions < minLoad) {
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return true;
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}
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}
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return false;
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}
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/**
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* Returns a pair where the first server is that with the least number of regions across the
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* cluster and the second server is that with the most number of regions across the cluster
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*/
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public Pair<Integer, Integer> findLeastAndMostLoadedServers() {
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int minServer = 0;
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int maxServer = 0;
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int minLoad = getNumRegions(minServer);
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int maxLoad = minLoad;
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for (int server = 1; server < numServers; server++) {
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int numRegions = getNumRegions(server);
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if (numRegions < minLoad) {
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minServer = server;
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minLoad = numRegions;
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}
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if (numRegions > maxLoad) {
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maxServer = server;
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maxLoad = numRegions;
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}
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}
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return Pair.newPair(minServer, maxServer);
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}
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/** An action to move or swap a region */
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public static class Action {
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public static enum Type {
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@ -18,10 +18,14 @@
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package org.apache.hadoop.hbase.master.balancer;
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import java.util.ArrayDeque;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.Collection;
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import java.util.Collections;
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import java.util.Comparator;
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import java.util.Deque;
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import java.util.HashMap;
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import java.util.Iterator;
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import java.util.LinkedList;
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import java.util.List;
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import java.util.Map;
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@ -30,7 +34,6 @@ import java.util.Random;
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import org.apache.commons.logging.Log;
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import org.apache.commons.logging.LogFactory;
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import org.apache.hadoop.hbase.classification.InterfaceAudience;
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import org.apache.hadoop.conf.Configuration;
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import org.apache.hadoop.hbase.ClusterStatus;
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import org.apache.hadoop.hbase.HBaseInterfaceAudience;
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@ -40,6 +43,7 @@ import org.apache.hadoop.hbase.RegionLoad;
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import org.apache.hadoop.hbase.ServerLoad;
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import org.apache.hadoop.hbase.ServerName;
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import org.apache.hadoop.hbase.TableName;
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import org.apache.hadoop.hbase.classification.InterfaceAudience;
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import org.apache.hadoop.hbase.master.MasterServices;
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import org.apache.hadoop.hbase.master.RegionPlan;
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import org.apache.hadoop.hbase.master.balancer.BaseLoadBalancer.Cluster.Action;
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@ -49,6 +53,10 @@ import org.apache.hadoop.hbase.master.balancer.BaseLoadBalancer.Cluster.MoveRegi
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import org.apache.hadoop.hbase.master.balancer.BaseLoadBalancer.Cluster.SwapRegionsAction;
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import org.apache.hadoop.hbase.util.Bytes;
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import org.apache.hadoop.hbase.util.EnvironmentEdgeManager;
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import org.apache.hadoop.hbase.util.Pair;
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import com.google.common.base.Optional;
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import com.google.common.base.Preconditions;
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/**
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* <p>This is a best effort load balancer. Given a Cost function F(C) => x It will
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@ -919,6 +927,225 @@ public class StochasticLoadBalancer extends BaseLoadBalancer {
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}
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}
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/**
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* Generates candidate actions to minimize the TableSkew cost function.
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*
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* For efficiency reasons, the cluster must be passed in when this generator is
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* constructed. Every move generated is applied to the cost function
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* (i.e. it is assumed that every action we generate is applied to the cluster).
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* This means we can adjust our cost incrementally for the cluster, rather than
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* recomputing at each iteration.
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*/
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static class TableSkewCandidateGenerator extends CandidateGenerator {
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// Mapping of table -> true iff too many servers in the cluster store at least
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// cluster.maxRegionsIfEvenlydistributed(table)
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boolean[] tablesWithEnoughServersWithMaxRegions = null;
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@Override
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Action generate(Cluster cluster) {
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if (tablesWithEnoughServersWithMaxRegions == null || tablesWithEnoughServersWithMaxRegions.length != cluster.numTables) {
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tablesWithEnoughServersWithMaxRegions = new boolean[cluster.numTables];
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}
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if (cluster.hasUnevenRegionDistribution()) {
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Pair<Integer, Integer> leastAndMostLoadedServers = cluster.findLeastAndMostLoadedServers();
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return moveFromTableWithEnoughRegions(cluster, leastAndMostLoadedServers.getSecond(), leastAndMostLoadedServers.getFirst());
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} else {
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Optional<TableAndServer> tableServer = findSkewedTableServer(cluster);
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if (!tableServer.isPresent()) {
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return Cluster.NullAction;
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}
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return findBestActionForTableServer(cluster, tableServer.get());
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}
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}
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/**
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* Returns a move fromServer -> toServer such that after the move fromServer will still have at least
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* the min # regions in terms of table skew calculation
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*/
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private Action moveFromTableWithEnoughRegions(Cluster cluster, int fromServer, int toServer) {
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for (int table : getShuffledRangeOfInts(0, cluster.numTables)) {
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int min = cluster.minRegionsIfEvenlyDistributed(table);
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if (cluster.numRegionsPerServerPerTable[fromServer][table] > min) {
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return getAction(fromServer, pickRandomRegionFromTableOnServer(cluster, fromServer, table), toServer, -1);
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}
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}
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return Cluster.NullAction;
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}
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/**
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* Picks a random subset of tables, then for each table T checks across cluster and returns first
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* server (if any) which holds too many regions from T. Returns Optional.absent() if no servers
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* are found that hold too many regions.
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*/
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private Optional<TableAndServer> findSkewedTableServer(Cluster cluster) {
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Optional<TableAndServer> tableServer = Optional.absent();
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List<Integer> servers = getShuffledRangeOfInts(0, cluster.numServers);
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Iterator<Integer> tableIter = getShuffledRangeOfInts(0, cluster.numTables).iterator();
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while (tableIter.hasNext() && !tableServer.isPresent()) {
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int table = tableIter.next();
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int maxRegions = cluster.maxRegionsIfEvenlyDistributed(table);
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int numShouldHaveMaxRegions = cluster.numServersWithMaxRegionsIfEvenlyDistributed(table);
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int numWithMaxRegions = 0;
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for (int server : servers) {
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int numRegions = cluster.numRegionsPerServerPerTable[server][table];
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// if more than max, server clearly has too many regions
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if (numRegions > maxRegions) {
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tableServer = Optional.of(new TableAndServer(table, server));
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break;
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}
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// if equal to max, check to see if we are within acceptable limit
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if (numRegions == maxRegions) {
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numWithMaxRegions++;
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}
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}
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tablesWithEnoughServersWithMaxRegions[table] = numWithMaxRegions >= numShouldHaveMaxRegions;
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// If we have found a table with more than max, we are done
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if (tableServer.isPresent()) {
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break;
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}
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// Otherwise, check to see if there are too many servers with maxRegions
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if (numWithMaxRegions > numShouldHaveMaxRegions) {
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for (int server : servers) {
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int numRegions = cluster.numRegionsPerServerPerTable[server][table];
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if (numRegions == maxRegions) {
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tableServer = Optional.of(new TableAndServer(table, server));
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break;
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}
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}
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}
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}
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return tableServer;
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}
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/**
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* Returns an list of integers that stores [upper - lower] unique integers in random order
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* s.t. for each integer i lower <= i < upper
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*/
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private List<Integer> getShuffledRangeOfInts(int lower, int upper) {
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Preconditions.checkArgument(lower < upper);
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ArrayList<Integer> arr = new ArrayList<Integer>(upper - lower);
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for (int i = lower; i < upper; i++) {
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arr.add(i);
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}
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Collections.shuffle(arr);
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return arr;
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}
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/**
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* Pick a random region from the specified server and table. Returns -1 if no regions from
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* the given table lie on the given server
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*/
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protected int pickRandomRegionFromTableOnServer(Cluster cluster, int server, int table) {
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if (server < 0 || table < 0) {
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return -1;
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}
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List<Integer> regionsFromTable = new ArrayList<>();
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for (int region : cluster.regionsPerServer[server]) {
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if (cluster.regionIndexToTableIndex[region] == table) {
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regionsFromTable.add(region);
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}
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}
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return regionsFromTable.get(RANDOM.nextInt(regionsFromTable.size()));
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}
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/**
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* Returns servers in the cluster that store fewer than k regions for the given table (sorted by
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* servers with the fewest regions from givenTable first)
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*/
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public List<Integer> getServersWithFewerThanKRegionsFromTable(final Cluster cluster, final int givenTable, int k) {
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List<Integer> serversWithFewerThanK = new ArrayList<>();
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for (int server = 0; server < cluster.numServers; server++) {
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if (cluster.numRegionsPerServerPerTable[server][givenTable] < k) {
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serversWithFewerThanK.add(server);
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}
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}
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Collections.sort(serversWithFewerThanK, new Comparator<Integer>() {
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@Override
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public int compare(Integer o1, Integer o2) {
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return cluster.numRegionsPerServerPerTable[o1.intValue()][givenTable] - cluster.numRegionsPerServerPerTable[o2.intValue()][givenTable];
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}
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});
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return serversWithFewerThanK;
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}
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/**
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* Given a table T for which server S stores too many regions, attempts to find a
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* SWAP operation that will better balance the cluster
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*/
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public Action findBestActionForTableServer(Cluster cluster, TableAndServer tableServer) {
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int fromTable = tableServer.getTable();
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int fromServer = tableServer.getServer();
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int minNumRegions = cluster.minRegionsIfEvenlyDistributed(fromTable);
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int maxNumRegions = cluster.maxRegionsIfEvenlyDistributed(fromTable);
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List<Integer> servers;
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if (tablesWithEnoughServersWithMaxRegions[fromTable]) {
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servers = getServersWithFewerThanKRegionsFromTable(cluster, fromTable, minNumRegions);
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} else {
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servers = getServersWithFewerThanKRegionsFromTable(cluster, fromTable, maxNumRegions);
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}
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if (servers.isEmpty()) {
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return Cluster.NullAction;
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}
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Optional<Action> swap = trySwap(cluster, fromServer, fromTable, servers);
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if (swap.isPresent()) {
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return swap.get();
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}
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// If we cannot perform a swap, we should do nothing
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return Cluster.NullAction;
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}
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/**
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* Given server1, table1, we try to find server2 and table2 such that
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* at least 3 of the following 4 criteria are met
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*
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* 1) server1 has too many regions of table1
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* 2) server1 has too few regions of table2
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* 3) server2 has too many regions of table2
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* 4) server2 has too few regions of table1
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*
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* We consider N regions from table T
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* too few if: N < cluster.minRegionsIfEvenlyDistributed(T)
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* too many if: N > cluster.maxRegionsIfEvenlyDistributed(T)
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*
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* Because (1) and (4) are true apriori, we only need to check for (2) and (3).
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*
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* If 3 of the 4 criteria are met, we return a swap operation between
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* randomly selected regions from table1 on server1 and from table2 on server2.
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*
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* Optional.absent() is returned if we could not find such a SWAP.
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*/
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private Optional<Action> trySwap(Cluster cluster, int server1, int table1, List<Integer> candidateServers) {
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// Because conditions (1) and (4) are true apriori, we only need to meet one of conditions (2) or (3)
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List<Integer> tables = getShuffledRangeOfInts(0, cluster.numTables);
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for (int table2 : tables) {
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int minRegions = cluster.minRegionsIfEvenlyDistributed(table2);
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int maxRegions = cluster.maxRegionsIfEvenlyDistributed(table2);
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for (int server2 : candidateServers) {
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int numRegions1 = cluster.numRegionsPerServerPerTable[server1][table2];
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int numRegions2 = cluster.numRegionsPerServerPerTable[server2][table2];
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if (numRegions2 == 0) {
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continue;
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}
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if ((numRegions1 < minRegions || numRegions2 > maxRegions) ||
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(minRegions != maxRegions && numRegions1 == minRegions && numRegions2 == maxRegions)) {
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int region1 = pickRandomRegionFromTableOnServer(cluster, server1, table1);
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int region2 = pickRandomRegionFromTableOnServer(cluster, server2, table2);
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return Optional.of(getAction(server1, region1, server2, region2));
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}
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}
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}
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return Optional.absent();
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}
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}
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/**
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* Base class of StochasticLoadBalancer's Cost Functions.
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*/
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@ -966,8 +1193,7 @@ public class StochasticLoadBalancer extends BaseLoadBalancer {
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break;
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case SWAP_REGIONS:
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SwapRegionsAction a = (SwapRegionsAction) action;
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regionMoved(a.fromRegion, a.fromServer, a.toServer);
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regionMoved(a.toRegion, a.toServer, a.fromServer);
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regionSwapped(a.fromRegion, a.fromServer, a.toRegion, a.toServer);
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break;
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default:
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throw new RuntimeException("Uknown action:" + action.type);
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@ -977,6 +1203,11 @@ public class StochasticLoadBalancer extends BaseLoadBalancer {
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protected void regionMoved(int region, int oldServer, int newServer) {
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}
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protected void regionSwapped(int region1, int server1, int region2, int server2) {
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regionMoved(region1, server1, server2);
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regionMoved(region2, server2, server1);
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}
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abstract double cost();
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/**
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@ -1170,9 +1401,188 @@ public class StochasticLoadBalancer extends BaseLoadBalancer {
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"hbase.master.balancer.stochastic.tableSkewCost";
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private static final float DEFAULT_TABLE_SKEW_COST = 35;
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/**
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* Ranges from 0.0 to 1.0 and is the proportion of how much the most skewed table
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* (as opposed to the average skew across all tables) should affect TableSkew cost
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*/
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private static final String MAX_TABLE_SKEW_WEIGHT_KEY =
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"hbase.master.balancer.stochastic.maxTableSkewWeight";
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private float DEFAULT_MAX_TABLE_SKEW_WEIGHT = 0.0f;
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private final float maxTableSkewWeight;
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private final float avgTableSkewWeight;
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// Number of moves for each table required to bring the cluster to a perfectly balanced
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// state (i.e. as if you had round-robin-ed regions across cluster)
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private int[] numMovesPerTable;
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TableSkewCostFunction(Configuration conf) {
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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
|
||||
|
|
@ -1588,10 +1998,32 @@ 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;
|
||||
}
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -48,6 +48,8 @@ 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;
|
||||
|
|
@ -119,7 +121,9 @@ 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);
|
||||
|
|
@ -135,6 +139,9 @@ public class TestStochasticLoadBalancer extends BalancerTestBase {
|
|||
returnServer(entry.getKey());
|
||||
}
|
||||
}
|
||||
// reset config
|
||||
conf.setFloat(StochasticLoadBalancer.MIN_COST_NEED_BALANCE_KEY, oldMinCostNeedBalance);
|
||||
loadBalancer.setConf(conf);
|
||||
}
|
||||
|
||||
@Test
|
||||
|
|
@ -255,6 +262,32 @@ 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();
|
||||
|
|
|
|||
|
|
@ -35,6 +35,7 @@ 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);
|
||||
|
|
@ -70,6 +71,7 @@ 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;
|
||||
|
|
@ -77,6 +79,8 @@ 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)
|
||||
|
|
|
|||
Loading…
Reference in New Issue