mirror of https://github.com/apache/druid.git
Extracted common methods
This commit is contained in:
Himadri Singh
parent
a8d8d1ea06
commit
c2604657ed
|
|
@ -0,0 +1,202 @@
|
|||
/*
|
||||
* Druid - a distributed column store.
|
||||
* Copyright (C) 2012, 2013 Metamarkets Group Inc.
|
||||
*
|
||||
* This program is free software; you can redistribute it and/or
|
||||
* modify it under the terms of the GNU General Public License
|
||||
* as published by the Free Software Foundation; either version 2
|
||||
* of the License, or (at your option) any later version.
|
||||
*
|
||||
* This program is distributed in the hope that it will be useful,
|
||||
* but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
* GNU General Public License for more details.
|
||||
*
|
||||
* You should have received a copy of the GNU General Public License
|
||||
* along with this program; if not, write to the Free Software
|
||||
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
|
||||
*/
|
||||
|
||||
package io.druid.server.coordinator;
|
||||
|
||||
import com.metamx.common.Pair;
|
||||
import com.metamx.emitter.EmittingLogger;
|
||||
import io.druid.timeline.DataSegment;
|
||||
import org.joda.time.DateTime;
|
||||
import org.joda.time.Interval;
|
||||
|
||||
import java.util.List;
|
||||
|
||||
public abstract class AbstractCostBalancerStrategy implements BalancerStrategy
|
||||
{
|
||||
private static final EmittingLogger log = new EmittingLogger(AbstractCostBalancerStrategy.class);
|
||||
private static final int DAY_IN_MILLIS = 1000 * 60 * 60 * 24;
|
||||
private static final int SEVEN_DAYS_IN_MILLIS = 7 * DAY_IN_MILLIS;
|
||||
private static final int THIRTY_DAYS_IN_MILLIS = 30 * DAY_IN_MILLIS;
|
||||
private final DateTime referenceTimestamp;
|
||||
|
||||
public AbstractCostBalancerStrategy(DateTime referenceTimestamp)
|
||||
{
|
||||
this.referenceTimestamp = referenceTimestamp;
|
||||
}
|
||||
|
||||
@Override
|
||||
public ServerHolder findNewSegmentHomeReplicator(
|
||||
DataSegment proposalSegment, List<ServerHolder> serverHolders
|
||||
)
|
||||
{
|
||||
ServerHolder holder = chooseBestServer(proposalSegment, serverHolders, false).rhs;
|
||||
if (holder != null && !holder.isServingSegment(proposalSegment)) {
|
||||
return holder;
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
|
||||
@Override
|
||||
public ServerHolder findNewSegmentHomeBalancer(
|
||||
DataSegment proposalSegment, List<ServerHolder> serverHolders
|
||||
)
|
||||
{
|
||||
return chooseBestServer(proposalSegment, serverHolders, true).rhs;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* For assignment, we want to move to the lowest cost server that isn't already serving the segment.
|
||||
*
|
||||
* @param proposalSegment A DataSegment that we are proposing to move.
|
||||
* @param serverHolders An iterable of ServerHolders for a particular tier.
|
||||
*
|
||||
* @return A ServerHolder with the new home for a segment.
|
||||
*/
|
||||
|
||||
protected abstract Pair<Double, ServerHolder> chooseBestServer(
|
||||
final DataSegment proposalSegment,
|
||||
final Iterable<ServerHolder> serverHolders,
|
||||
boolean includeCurrentServer
|
||||
);
|
||||
|
||||
/**
|
||||
* This defines the unnormalized cost function between two segments. There is a base cost given by
|
||||
* the minimum size of the two segments and additional penalties.
|
||||
* recencyPenalty: it is more likely that recent segments will be queried together
|
||||
* dataSourcePenalty: if two segments belong to the same data source, they are more likely to be involved
|
||||
* in the same queries
|
||||
* gapPenalty: it is more likely that segments close together in time will be queried together
|
||||
*
|
||||
* @param segment1 The first DataSegment.
|
||||
* @param segment2 The second DataSegment.
|
||||
*
|
||||
* @return The joint cost of placing the two DataSegments together on one node.
|
||||
*/
|
||||
public double computeJointSegmentCosts(final DataSegment segment1, final DataSegment segment2)
|
||||
{
|
||||
final Interval gap = segment1.getInterval().gap(segment2.getInterval());
|
||||
|
||||
final double baseCost = Math.min(segment1.getSize(), segment2.getSize());
|
||||
double recencyPenalty = 1;
|
||||
double dataSourcePenalty = 1;
|
||||
double gapPenalty = 1;
|
||||
|
||||
if (segment1.getDataSource().equals(segment2.getDataSource())) {
|
||||
dataSourcePenalty = 2;
|
||||
}
|
||||
|
||||
double maxDiff = Math.max(
|
||||
referenceTimestamp.getMillis() - segment1.getInterval().getEndMillis(),
|
||||
referenceTimestamp.getMillis() - segment2.getInterval().getEndMillis()
|
||||
);
|
||||
double segment1diff = referenceTimestamp.getMillis() - segment1.getInterval().getEndMillis();
|
||||
double segment2diff = referenceTimestamp.getMillis() - segment2.getInterval().getEndMillis();
|
||||
if (segment1diff < SEVEN_DAYS_IN_MILLIS && segment2diff < SEVEN_DAYS_IN_MILLIS) {
|
||||
recencyPenalty = (2 - segment1diff / SEVEN_DAYS_IN_MILLIS) * (2 - segment2diff / SEVEN_DAYS_IN_MILLIS);
|
||||
}
|
||||
|
||||
/** gap is null if the two segment intervals overlap or if they're adjacent */
|
||||
if (gap == null) {
|
||||
gapPenalty = 2;
|
||||
} else {
|
||||
long gapMillis = gap.toDurationMillis();
|
||||
if (gapMillis < THIRTY_DAYS_IN_MILLIS) {
|
||||
gapPenalty = 2 - gapMillis / THIRTY_DAYS_IN_MILLIS;
|
||||
}
|
||||
}
|
||||
|
||||
final double cost = baseCost * recencyPenalty * dataSourcePenalty * gapPenalty;
|
||||
|
||||
return cost;
|
||||
}
|
||||
|
||||
public BalancerSegmentHolder pickSegmentToMove(final List<ServerHolder> serverHolders)
|
||||
{
|
||||
ReservoirSegmentSampler sampler = new ReservoirSegmentSampler();
|
||||
return sampler.getRandomBalancerSegmentHolder(serverHolders);
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculates the initial cost of the Druid segment configuration.
|
||||
*
|
||||
* @param serverHolders A list of ServerHolders for a particular tier.
|
||||
*
|
||||
* @return The initial cost of the Druid tier.
|
||||
*/
|
||||
public double calculateInitialTotalCost(final List<ServerHolder> serverHolders)
|
||||
{
|
||||
double cost = 0;
|
||||
for (ServerHolder server : serverHolders) {
|
||||
DataSegment[] segments = server.getServer().getSegments().values().toArray(new DataSegment[]{});
|
||||
for (int i = 0; i < segments.length; ++i) {
|
||||
for (int j = i; j < segments.length; ++j) {
|
||||
cost += computeJointSegmentCosts(segments[i], segments[j]);
|
||||
}
|
||||
}
|
||||
}
|
||||
return cost;
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculates the cost normalization. This is such that the normalized cost is lower bounded
|
||||
* by 1 (e.g. when each segment gets its own historical node).
|
||||
*
|
||||
* @param serverHolders A list of ServerHolders for a particular tier.
|
||||
*
|
||||
* @return The normalization value (the sum of the diagonal entries in the
|
||||
* pairwise cost matrix). This is the cost of a cluster if each
|
||||
* segment were to get its own historical node.
|
||||
*/
|
||||
public double calculateNormalization(final List<ServerHolder> serverHolders)
|
||||
{
|
||||
double cost = 0;
|
||||
for (ServerHolder server : serverHolders) {
|
||||
for (DataSegment segment : server.getServer().getSegments().values()) {
|
||||
cost += computeJointSegmentCosts(segment, segment);
|
||||
}
|
||||
}
|
||||
return cost;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void emitStats(
|
||||
String tier,
|
||||
CoordinatorStats stats, List<ServerHolder> serverHolderList
|
||||
)
|
||||
{
|
||||
final double initialTotalCost = calculateInitialTotalCost(serverHolderList);
|
||||
final double normalization = calculateNormalization(serverHolderList);
|
||||
final double normalizedInitialCost = initialTotalCost / normalization;
|
||||
|
||||
stats.addToTieredStat("initialCost", tier, (long) initialTotalCost);
|
||||
stats.addToTieredStat("normalization", tier, (long) normalization);
|
||||
stats.addToTieredStat("normalizedInitialCostTimesOneThousand", tier, (long) (normalizedInitialCost * 1000));
|
||||
|
||||
log.info(
|
||||
"[%s]: Initial Total Cost: [%f], Normalization: [%f], Initial Normalized Cost: [%f]",
|
||||
tier,
|
||||
initialTotalCost,
|
||||
normalization,
|
||||
normalizedInitialCost
|
||||
);
|
||||
|
||||
}
|
||||
}
|
||||
|
|
@ -33,51 +33,16 @@ import java.util.concurrent.ExecutorService;
|
|||
import java.util.concurrent.Executors;
|
||||
import java.util.concurrent.Future;
|
||||
|
||||
public class CostBalancerMultithreadStrategy implements BalancerStrategy
|
||||
public class CostBalancerMultithreadStrategy extends AbstractCostBalancerStrategy
|
||||
{
|
||||
private static final EmittingLogger log = new EmittingLogger(CostBalancerMultithreadStrategy.class);
|
||||
private static final int DAY_IN_MILLIS = 1000 * 60 * 60 * 24;
|
||||
private static final int SEVEN_DAYS_IN_MILLIS = 7 * DAY_IN_MILLIS;
|
||||
private static final int THIRTY_DAYS_IN_MILLIS = 30 * DAY_IN_MILLIS;
|
||||
private final long referenceTimestampInMillis;
|
||||
|
||||
public CostBalancerMultithreadStrategy(DateTime referenceTimestamp)
|
||||
{
|
||||
this.referenceTimestampInMillis = referenceTimestamp.getMillis();
|
||||
super(referenceTimestamp);
|
||||
}
|
||||
|
||||
@Override
|
||||
public ServerHolder findNewSegmentHomeReplicator(
|
||||
DataSegment proposalSegment, List<ServerHolder> serverHolders
|
||||
)
|
||||
{
|
||||
ServerHolder holder = chooseBestServer(proposalSegment, serverHolders, false).rhs;
|
||||
if (holder != null && !holder.isServingSegment(proposalSegment)) {
|
||||
return holder;
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
|
||||
@Override
|
||||
public ServerHolder findNewSegmentHomeBalancer(
|
||||
DataSegment proposalSegment, List<ServerHolder> serverHolders
|
||||
)
|
||||
{
|
||||
return chooseBestServer(proposalSegment, serverHolders, true).rhs;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* For assignment, we want to move to the lowest cost server that isn't already serving the segment.
|
||||
*
|
||||
* @param proposalSegment A DataSegment that we are proposing to move.
|
||||
* @param serverHolders An iterable of ServerHolders for a particular tier.
|
||||
*
|
||||
* @return A ServerHolder with the new home for a segment.
|
||||
*/
|
||||
|
||||
private Pair<Double, ServerHolder> chooseBestServer(
|
||||
protected Pair<Double, ServerHolder> chooseBestServer(
|
||||
final DataSegment proposalSegment,
|
||||
final Iterable<ServerHolder> serverHolders,
|
||||
final boolean includeCurrentServer
|
||||
|
|
@ -151,123 +116,4 @@ public class CostBalancerMultithreadStrategy implements BalancerStrategy
|
|||
return Pair.of(Double.POSITIVE_INFINITY, null);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* This defines the unnormalized cost function between two segments. There is a base cost given by
|
||||
* the minimum size of the two segments and additional penalties.
|
||||
* recencyPenalty: it is more likely that recent segments will be queried together
|
||||
* dataSourcePenalty: if two segments belong to the same data source, they are more likely to be involved
|
||||
* in the same queries
|
||||
* gapPenalty: it is more likely that segments close together in time will be queried together
|
||||
*
|
||||
* @param segment1 The first DataSegment.
|
||||
* @param segment2 The second DataSegment.
|
||||
*
|
||||
* @return The joint cost of placing the two DataSegments together on one node.
|
||||
*/
|
||||
public double computeJointSegmentCosts(final DataSegment segment1, final DataSegment segment2)
|
||||
{
|
||||
final Interval gap = segment1.getInterval().gap(segment2.getInterval());
|
||||
|
||||
final double baseCost = Math.min(segment1.getSize(), segment2.getSize());
|
||||
double recencyPenalty = 1;
|
||||
double dataSourcePenalty = 1;
|
||||
double gapPenalty = 1;
|
||||
|
||||
if (segment1.getDataSource().equals(segment2.getDataSource())) {
|
||||
dataSourcePenalty = 2;
|
||||
}
|
||||
|
||||
double segment1diff = referenceTimestampInMillis - segment1.getInterval().getEndMillis();
|
||||
double segment2diff = referenceTimestampInMillis - segment2.getInterval().getEndMillis();
|
||||
if (segment1diff < SEVEN_DAYS_IN_MILLIS && segment2diff < SEVEN_DAYS_IN_MILLIS) {
|
||||
recencyPenalty = (2 - segment1diff / SEVEN_DAYS_IN_MILLIS) * (2 - segment2diff / SEVEN_DAYS_IN_MILLIS);
|
||||
}
|
||||
|
||||
/** gap is null if the two segment intervals overlap or if they're adjacent */
|
||||
if (gap == null) {
|
||||
gapPenalty = 2;
|
||||
} else {
|
||||
long gapMillis = gap.toDurationMillis();
|
||||
if (gapMillis < THIRTY_DAYS_IN_MILLIS) {
|
||||
gapPenalty = 2 - gapMillis / THIRTY_DAYS_IN_MILLIS;
|
||||
}
|
||||
}
|
||||
|
||||
final double cost = baseCost * recencyPenalty * dataSourcePenalty * gapPenalty;
|
||||
|
||||
return cost;
|
||||
}
|
||||
|
||||
public BalancerSegmentHolder pickSegmentToMove(final List<ServerHolder> serverHolders)
|
||||
{
|
||||
ReservoirSegmentSampler sampler = new ReservoirSegmentSampler();
|
||||
return sampler.getRandomBalancerSegmentHolder(serverHolders);
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculates the initial cost of the Druid segment configuration.
|
||||
*
|
||||
* @param serverHolders A list of ServerHolders for a particular tier.
|
||||
*
|
||||
* @return The initial cost of the Druid tier.
|
||||
*/
|
||||
public double calculateInitialTotalCost(final List<ServerHolder> serverHolders)
|
||||
{
|
||||
double cost = 0;
|
||||
for (ServerHolder server : serverHolders) {
|
||||
DataSegment[] segments = server.getServer().getSegments().values().toArray(new DataSegment[]{});
|
||||
for (int i = 0; i < segments.length; ++i) {
|
||||
for (int j = i; j < segments.length; ++j) {
|
||||
cost += computeJointSegmentCosts(segments[i], segments[j]);
|
||||
}
|
||||
}
|
||||
}
|
||||
return cost;
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculates the cost normalization. This is such that the normalized cost is lower bounded
|
||||
* by 1 (e.g. when each segment gets its own historical node).
|
||||
*
|
||||
* @param serverHolders A list of ServerHolders for a particular tier.
|
||||
*
|
||||
* @return The normalization value (the sum of the diagonal entries in the
|
||||
* pairwise cost matrix). This is the cost of a cluster if each
|
||||
* segment were to get its own historical node.
|
||||
*/
|
||||
public double calculateNormalization(final List<ServerHolder> serverHolders)
|
||||
{
|
||||
double cost = 0;
|
||||
for (ServerHolder server : serverHolders) {
|
||||
for (DataSegment segment : server.getServer().getSegments().values()) {
|
||||
cost += computeJointSegmentCosts(segment, segment);
|
||||
}
|
||||
}
|
||||
return cost;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void emitStats(
|
||||
String tier,
|
||||
CoordinatorStats stats, List<ServerHolder> serverHolderList
|
||||
)
|
||||
{
|
||||
final double initialTotalCost = calculateInitialTotalCost(serverHolderList);
|
||||
final double normalization = calculateNormalization(serverHolderList);
|
||||
final double normalizedInitialCost = initialTotalCost / normalization;
|
||||
|
||||
stats.addToTieredStat("initialCost", tier, (long) initialTotalCost);
|
||||
stats.addToTieredStat("normalization", tier, (long) normalization);
|
||||
stats.addToTieredStat("normalizedInitialCostTimesOneThousand", tier, (long) (normalizedInitialCost * 1000));
|
||||
|
||||
log.info(
|
||||
"[%s]: Initial Total Cost: [%f], Normalization: [%f], Initial Normalized Cost: [%f]",
|
||||
tier,
|
||||
initialTotalCost,
|
||||
normalization,
|
||||
normalizedInitialCost
|
||||
);
|
||||
|
||||
}
|
||||
}
|
||||
|
|
@ -26,6 +26,6 @@ public class CostBalancerMultithreadStrategyFactory implements BalancerStrategyF
|
|||
@Override
|
||||
public BalancerStrategy createBalancerStrategy(DateTime referenceTimestamp)
|
||||
{
|
||||
return new CostBalancerStrategy(referenceTimestamp);
|
||||
return new CostBalancerMultithreadStrategy(referenceTimestamp);
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -27,51 +27,16 @@ import org.joda.time.Interval;
|
|||
|
||||
import java.util.List;
|
||||
|
||||
public class CostBalancerStrategy implements BalancerStrategy
|
||||
public class CostBalancerStrategy extends AbstractCostBalancerStrategy
|
||||
{
|
||||
private static final EmittingLogger log = new EmittingLogger(CostBalancerStrategy.class);
|
||||
private static final int DAY_IN_MILLIS = 1000 * 60 * 60 * 24;
|
||||
private static final int SEVEN_DAYS_IN_MILLIS = 7 * DAY_IN_MILLIS;
|
||||
private static final int THIRTY_DAYS_IN_MILLIS = 30 * DAY_IN_MILLIS;
|
||||
private final DateTime referenceTimestamp;
|
||||
|
||||
public CostBalancerStrategy(DateTime referenceTimestamp)
|
||||
{
|
||||
this.referenceTimestamp = referenceTimestamp;
|
||||
super(referenceTimestamp);
|
||||
}
|
||||
|
||||
@Override
|
||||
public ServerHolder findNewSegmentHomeReplicator(
|
||||
DataSegment proposalSegment, List<ServerHolder> serverHolders
|
||||
)
|
||||
{
|
||||
ServerHolder holder = chooseBestServer(proposalSegment, serverHolders, false).rhs;
|
||||
if (holder != null && !holder.isServingSegment(proposalSegment)) {
|
||||
return holder;
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
|
||||
@Override
|
||||
public ServerHolder findNewSegmentHomeBalancer(
|
||||
DataSegment proposalSegment, List<ServerHolder> serverHolders
|
||||
)
|
||||
{
|
||||
return chooseBestServer(proposalSegment, serverHolders, true).rhs;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* For assignment, we want to move to the lowest cost server that isn't already serving the segment.
|
||||
*
|
||||
* @param proposalSegment A DataSegment that we are proposing to move.
|
||||
* @param serverHolders An iterable of ServerHolders for a particular tier.
|
||||
*
|
||||
* @return A ServerHolder with the new home for a segment.
|
||||
*/
|
||||
|
||||
private Pair<Double, ServerHolder> chooseBestServer(
|
||||
protected Pair<Double, ServerHolder> chooseBestServer(
|
||||
final DataSegment proposalSegment,
|
||||
final Iterable<ServerHolder> serverHolders,
|
||||
boolean includeCurrentServer
|
||||
|
|
@ -109,127 +74,4 @@ public class CostBalancerStrategy implements BalancerStrategy
|
|||
|
||||
return bestServer;
|
||||
}
|
||||
|
||||
/**
|
||||
* This defines the unnormalized cost function between two segments. There is a base cost given by
|
||||
* the minimum size of the two segments and additional penalties.
|
||||
* recencyPenalty: it is more likely that recent segments will be queried together
|
||||
* dataSourcePenalty: if two segments belong to the same data source, they are more likely to be involved
|
||||
* in the same queries
|
||||
* gapPenalty: it is more likely that segments close together in time will be queried together
|
||||
*
|
||||
* @param segment1 The first DataSegment.
|
||||
* @param segment2 The second DataSegment.
|
||||
*
|
||||
* @return The joint cost of placing the two DataSegments together on one node.
|
||||
*/
|
||||
public double computeJointSegmentCosts(final DataSegment segment1, final DataSegment segment2)
|
||||
{
|
||||
final Interval gap = segment1.getInterval().gap(segment2.getInterval());
|
||||
|
||||
final double baseCost = Math.min(segment1.getSize(), segment2.getSize());
|
||||
double recencyPenalty = 1;
|
||||
double dataSourcePenalty = 1;
|
||||
double gapPenalty = 1;
|
||||
|
||||
if (segment1.getDataSource().equals(segment2.getDataSource())) {
|
||||
dataSourcePenalty = 2;
|
||||
}
|
||||
|
||||
double maxDiff = Math.max(
|
||||
referenceTimestamp.getMillis() - segment1.getInterval().getEndMillis(),
|
||||
referenceTimestamp.getMillis() - segment2.getInterval().getEndMillis()
|
||||
);
|
||||
double segment1diff = referenceTimestamp.getMillis() - segment1.getInterval().getEndMillis();
|
||||
double segment2diff = referenceTimestamp.getMillis() - segment2.getInterval().getEndMillis();
|
||||
if (segment1diff < SEVEN_DAYS_IN_MILLIS && segment2diff < SEVEN_DAYS_IN_MILLIS) {
|
||||
recencyPenalty = (2 - segment1diff / SEVEN_DAYS_IN_MILLIS) * (2 - segment2diff / SEVEN_DAYS_IN_MILLIS);
|
||||
}
|
||||
|
||||
/** gap is null if the two segment intervals overlap or if they're adjacent */
|
||||
if (gap == null) {
|
||||
gapPenalty = 2;
|
||||
} else {
|
||||
long gapMillis = gap.toDurationMillis();
|
||||
if (gapMillis < THIRTY_DAYS_IN_MILLIS) {
|
||||
gapPenalty = 2 - gapMillis / THIRTY_DAYS_IN_MILLIS;
|
||||
}
|
||||
}
|
||||
|
||||
final double cost = baseCost * recencyPenalty * dataSourcePenalty * gapPenalty;
|
||||
|
||||
return cost;
|
||||
}
|
||||
|
||||
public BalancerSegmentHolder pickSegmentToMove(final List<ServerHolder> serverHolders)
|
||||
{
|
||||
ReservoirSegmentSampler sampler = new ReservoirSegmentSampler();
|
||||
return sampler.getRandomBalancerSegmentHolder(serverHolders);
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculates the initial cost of the Druid segment configuration.
|
||||
*
|
||||
* @param serverHolders A list of ServerHolders for a particular tier.
|
||||
*
|
||||
* @return The initial cost of the Druid tier.
|
||||
*/
|
||||
public double calculateInitialTotalCost(final List<ServerHolder> serverHolders)
|
||||
{
|
||||
double cost = 0;
|
||||
for (ServerHolder server : serverHolders) {
|
||||
DataSegment[] segments = server.getServer().getSegments().values().toArray(new DataSegment[]{});
|
||||
for (int i = 0; i < segments.length; ++i) {
|
||||
for (int j = i; j < segments.length; ++j) {
|
||||
cost += computeJointSegmentCosts(segments[i], segments[j]);
|
||||
}
|
||||
}
|
||||
}
|
||||
return cost;
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculates the cost normalization. This is such that the normalized cost is lower bounded
|
||||
* by 1 (e.g. when each segment gets its own historical node).
|
||||
*
|
||||
* @param serverHolders A list of ServerHolders for a particular tier.
|
||||
*
|
||||
* @return The normalization value (the sum of the diagonal entries in the
|
||||
* pairwise cost matrix). This is the cost of a cluster if each
|
||||
* segment were to get its own historical node.
|
||||
*/
|
||||
public double calculateNormalization(final List<ServerHolder> serverHolders)
|
||||
{
|
||||
double cost = 0;
|
||||
for (ServerHolder server : serverHolders) {
|
||||
for (DataSegment segment : server.getServer().getSegments().values()) {
|
||||
cost += computeJointSegmentCosts(segment, segment);
|
||||
}
|
||||
}
|
||||
return cost;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void emitStats(
|
||||
String tier,
|
||||
CoordinatorStats stats, List<ServerHolder> serverHolderList
|
||||
)
|
||||
{
|
||||
final double initialTotalCost = calculateInitialTotalCost(serverHolderList);
|
||||
final double normalization = calculateNormalization(serverHolderList);
|
||||
final double normalizedInitialCost = initialTotalCost / normalization;
|
||||
|
||||
stats.addToTieredStat("initialCost", tier, (long) initialTotalCost);
|
||||
stats.addToTieredStat("normalization", tier, (long) normalization);
|
||||
stats.addToTieredStat("normalizedInitialCostTimesOneThousand", tier, (long) (normalizedInitialCost * 1000));
|
||||
|
||||
log.info(
|
||||
"[%s]: Initial Total Cost: [%f], Normalization: [%f], Initial Normalized Cost: [%f]",
|
||||
tier,
|
||||
initialTotalCost,
|
||||
normalization,
|
||||
normalizedInitialCost
|
||||
);
|
||||
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue