HBASE-17707 New More Accurate Table Skew cost function/generator (Kahlil Oppenheimer)

This commit is contained in:
tedyu 2017-03-02 09:46:38 -08:00
parent 0b3ecc5ee7
commit 06e984b086
4 changed files with 536 additions and 3 deletions

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@ -53,6 +53,7 @@ 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;
@ -140,6 +141,7 @@ 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
@ -330,6 +332,7 @@ 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++) {
@ -339,6 +342,7 @@ 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]]++;
}
}
@ -470,6 +474,76 @@ 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 {

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@ -18,15 +18,20 @@
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.HashSet;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
import java.util.Random;
import java.util.Set;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
@ -49,6 +54,10 @@ 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
@ -919,6 +928,214 @@ 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) {
List<Integer> servers = getShuffledRangeOfInts(0, cluster.numServers);
for (int table : getShuffledRangeOfInts(0, cluster.numTables)) {
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) {
return Optional.of(new TableAndServer(table, server));
}
// if equal to max, check to see if we are within acceptable limit
if (numRegions == maxRegions) {
numWithMaxRegions++;
}
}
// Check to see if there are too many with maxRegions
tablesWithEnoughServersWithMaxRegions[table] = numWithMaxRegions >= numShouldHaveMaxRegions;
if (numWithMaxRegions > numShouldHaveMaxRegions) {
for (int server : servers) {
int numRegions = cluster.numRegionsPerServerPerTable[server][table];
if (numRegions == maxRegions) {
return Optional.of(new TableAndServer(table, server));
}
}
}
}
return Optional.absent();
}
/**
* 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.
*/
@ -966,8 +1183,7 @@ public class StochasticLoadBalancer extends BaseLoadBalancer {
break;
case SWAP_REGIONS:
SwapRegionsAction a = (SwapRegionsAction) action;
regionMoved(a.fromRegion, a.fromServer, a.toServer);
regionMoved(a.toRegion, a.toServer, a.fromServer);
regionSwapped(a.fromRegion, a.fromServer, a.toRegion, a.toServer);
break;
default:
throw new RuntimeException("Uknown action:" + action.type);
@ -977,6 +1193,11 @@ 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();
/**
@ -1170,9 +1391,188 @@ 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
@ -1588,10 +1988,32 @@ 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;
}
}

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@ -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
@ -253,6 +260,32 @@ public class TestStochasticLoadBalancer extends BalancerTestBase {
double result = storeFileCostFunction.getRegionLoadCost(regionLoads);
// storefile size cost is simply an average of it's value over time
assertEquals(2.5, result, 0.01);
}
@Test (timeout=45000)
public void testTableSkewCandidateGeneratorConvergesToZero() {
int replication = 1;
StochasticLoadBalancer.CostFunction
costFunction = new StochasticLoadBalancer.TableSkewCostFunction(conf);
CandidateGenerator generator = new TableSkewCandidateGenerator();
for (int i = 0; i < 5; i++) {
int numNodes = rand.nextInt(100) + 1; // num nodes between 1 - 100
int numTables = rand.nextInt(100) + 1; // num tables between 1 and 100
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

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@ -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)