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HBASE-14058 Stabilizing default heap memory tuner 

Signed-off-by: Elliott Clark <eclark@apache.org>
This commit is contained in:
abhilak 2015-07-22 11:30:18 -07:00 committed by Elliott Clark
parent 45d52129dd
commit 89ecb93c95
1 changed files with 63 additions and 30 deletions
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93
hbase-server/src/main/java/org/apache/hadoop/hbase/regionserver/DefaultHeapMemoryTuner.java
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@ -48,20 +48,24 @@ import org.apache.hadoop.hbase.util.RollingStatCalculator;
* same amount. If none of them is sufficient we do statistical analysis on number of cache misses
* and flushes to determine tuner direction. Based on these statistics we decide the tuner
* direction. If we are not confident about which step direction to take we do nothing and wait for
* next iteration. On expectation we will be tuning for at least 22% tuner calls. The number of
* next iteration. On expectation we will be tuning for at least 10% tuner calls. The number of
* past periods to consider for statistics calculation can be specified in config by
* <i>hbase.regionserver.heapmemory.autotuner.lookup.periods</i>. Also these many initial calls to
* tuner will be ignored (cache is warming up and we leave the system to reach steady state).
* After the tuner takes a step, in next call we insure that last call was indeed helpful and did
* not do us any harm. If not then we revert the previous step. The step size is dynamic and it
* changes based on current and previous tuning direction. When last tuner step was NEUTRAL
* and current tuning step is not NEUTRAL then we assume we are restarting the tuning process and
* step size is changed to maximum allowed size which can be specified in config by
* <i>hbase.regionserver.heapmemory.autotuner.step.max</i>. If we are reverting the previous step
* then we decrease step size to half. This decrease is similar to binary search where we try to
* reach the most desired value. The minimum step size can be specified in config by
* <i>hbase.regionserver.heapmemory.autotuner.step.min</i>. In other cases we leave step size
* unchanged.
* changes based on current and past few tuning directions and their step sizes. We maintain a
* parameter <i>decayingAvgTunerStepSize</i> which is sum of past tuner steps with
* sign(positive for increase in memstore and negative for increase in block cache). But rather
* than simple sum it is calculated by giving more priority to the recent tuning steps.
* When last few tuner steps were NETURAL then we assume we are restarting the tuning process and
* step size is updated to maximum allowed size which can be specified in config by
* <i>hbase.regionserver.heapmemory.autotuner.step.max</i>. If in a particular tuning operation
* the step direction is opposite to what indicated by <i>decayingTunerStepSizeSum</i>
* we decrease the step size by half. Step size does not change in other tuning operations.
* When step size gets below a certain threshold then the following tuner operations are
* considered to be neutral. The minimum step size can be specified in config by
* <i>hbase.regionserver.heapmemory.autotuner.step.min</i>.
*/
@InterfaceAudience.Private
class DefaultHeapMemoryTuner implements HeapMemoryTuner {
@ -74,9 +78,9 @@ class DefaultHeapMemoryTuner implements HeapMemoryTuner {
public static final String NUM_PERIODS_TO_IGNORE =
"hbase.regionserver.heapmemory.autotuner.ignored.periods";
// Maximum step size that the tuner can take
public static final float DEFAULT_MAX_STEP_VALUE = 0.08f; // 8%
public static final float DEFAULT_MAX_STEP_VALUE = 0.04f; // 4%
// Minimum step size that the tuner can take
public static final float DEFAULT_MIN_STEP_VALUE = 0.005f; // 0.5%
public static final float DEFAULT_MIN_STEP_VALUE = 0.00125f; // 0.125%
// If current block cache size or memstore size in use is below this level relative to memory
// provided to it then corresponding component will be considered to have sufficient memory
public static final float DEFAULT_SUFFICIENT_MEMORY_LEVEL_VALUE = 0.5f; // 50%
@ -85,6 +89,9 @@ class DefaultHeapMemoryTuner implements HeapMemoryTuner {
public static final int DEFAULT_LOOKUP_PERIODS = 60;
public static final int DEFAULT_NUM_PERIODS_IGNORED = 60;
private static final TunerResult NO_OP_TUNER_RESULT = new TunerResult(false);
// If deviation of tuner step size gets below this value then it means past few periods were
// NEUTRAL(given that last tuner period was also NEUTRAL).
private static final double TUNER_STEP_EPS = 1e-6;
private Log LOG = LogFactory.getLog(DefaultHeapMemoryTuner.class);
private TunerResult TUNER_RESULT = new TunerResult(true);
@ -106,9 +113,14 @@ class DefaultHeapMemoryTuner implements HeapMemoryTuner {
private RollingStatCalculator rollingStatsForCacheMisses;
private RollingStatCalculator rollingStatsForFlushes;
private RollingStatCalculator rollingStatsForEvictions;
private RollingStatCalculator rollingStatsForTunerSteps;
// Set step size to max value for tuning, this step size will adjust dynamically while tuning
private float step = DEFAULT_MAX_STEP_VALUE;
private StepDirection prevTuneDirection = StepDirection.NEUTRAL;
//positive means memstore's size was increased
//It is not just arithmetic sum of past tuner periods. More priority is given to recent
//tuning steps.
private double decayingTunerStepSizeSum = 0;
@Override
public TunerResult tune(TunerContext context) {
@ -124,6 +136,7 @@ class DefaultHeapMemoryTuner implements HeapMemoryTuner {
if (ignoreInitialPeriods < numPeriodsToIgnore) {
// Ignoring the first few tuner periods
ignoreInitialPeriods++;
rollingStatsForTunerSteps.insertDataValue(0);
return NO_OP_TUNER_RESULT;
}
String tunerLog = "";
@ -190,30 +203,33 @@ class DefaultHeapMemoryTuner implements HeapMemoryTuner {
}
// If we are not reverting. We try to tune memory sizes by looking at cache misses / flushes.
if (!isReverting){
// mean +- deviation/2 is considered to be normal
// mean +- deviation*0.8 is considered to be normal
// below it its consider low and above it is considered high.
// We can safely assume that the number cache misses, flushes are normally distributed over
// past periods and hence on all the above mentioned classes (normal, high and low)
// are equally likely with 33% probability each. Hence there is very good probability that
// we will not always fall in default step.
// are likely to occur with probability 56%, 22%, 22% respectively. Hence there is at
// least ~10% probability that we will not fall in NEUTRAL step.
// This optimization solution is feedback based and we revert when we
// dont find our steps helpful. Hence we want to do tuning only when we have clear
// indications because too many unnecessary tuning may affect the performance of cluster.
if ((double)cacheMissCount < rollingStatsForCacheMisses.getMean() -
rollingStatsForCacheMisses.getDeviation()/2.00 &&
rollingStatsForCacheMisses.getDeviation()*0.80 &&
(double)totalFlushCount < rollingStatsForFlushes.getMean() -
rollingStatsForFlushes.getDeviation()/2.00) {
rollingStatsForFlushes.getDeviation()*0.80) {
// Everything is fine no tuning required
newTuneDirection = StepDirection.NEUTRAL;
} else if ((double)cacheMissCount > rollingStatsForCacheMisses.getMean() +
rollingStatsForCacheMisses.getDeviation()/2.00 &&
rollingStatsForCacheMisses.getDeviation()*0.80 &&
(double)totalFlushCount < rollingStatsForFlushes.getMean() -
rollingStatsForFlushes.getDeviation()/2.00) {
rollingStatsForFlushes.getDeviation()*0.80) {
// more misses , increasing cache size
newTuneDirection = StepDirection.INCREASE_BLOCK_CACHE_SIZE;
tunerLog +=
"Increasing block cache size as observed increase in number of cache misses.";
} else if ((double)cacheMissCount < rollingStatsForCacheMisses.getMean() -
rollingStatsForCacheMisses.getDeviation()/2.00 &&
rollingStatsForCacheMisses.getDeviation()*0.80 &&
(double)totalFlushCount > rollingStatsForFlushes.getMean() +
rollingStatsForFlushes.getDeviation()/2.00) {
rollingStatsForFlushes.getDeviation()*0.80) {
// more flushes , increasing memstore size
newTuneDirection = StepDirection.INCREASE_MEMSTORE_SIZE;
tunerLog += "Increasing memstore size as observed increase in number of flushes.";
@ -228,32 +244,48 @@ class DefaultHeapMemoryTuner implements HeapMemoryTuner {
}
}
}
// Adjusting step size for tuning to get to steady state.
// Adjusting step size for tuning to get to steady state or restart from steady state.
// Even if the step size was 4% and 32 GB memory size, we will be shifting 1 GB back and forth
// per tuner operation and it can affect the performance of cluster
if (prevTuneDirection == StepDirection.NEUTRAL && newTuneDirection != StepDirection.NEUTRAL) {
// Restarting the tuning from steady state.
// per tuner operation and it can affect the performance of cluster so we keep on decreasing
// step size until everything settles.
if (prevTuneDirection == StepDirection.NEUTRAL
&& newTuneDirection != StepDirection.NEUTRAL
&& rollingStatsForTunerSteps.getDeviation() < TUNER_STEP_EPS) {
// Restarting the tuning from steady state and setting step size to maximum.
// The deviation cannot be that low if last period was neutral and some recent periods were
// not neutral.
step = maximumStepSize;
} else if (prevTuneDirection != newTuneDirection) {
// Decrease the step size to reach the steady state. Similar procedure as binary search.
} else if ((newTuneDirection == StepDirection.INCREASE_MEMSTORE_SIZE
&& decayingTunerStepSizeSum < 0) ||
(newTuneDirection == StepDirection.INCREASE_BLOCK_CACHE_SIZE
&& decayingTunerStepSizeSum > 0)) {
// Current step is opposite of past tuner actions so decrease the step size to reach steady
// state.
step = step/2.00f;
if (step < minimumStepSize) {
// Ensure step size does not gets too small.
step = minimumStepSize;
}
}
if (step < minimumStepSize) {
// If step size is too small then we do nothing.
step = 0.0f;
newTuneDirection = StepDirection.NEUTRAL;
}
// Increase / decrease the memstore / block cahce sizes depending on new tuner step.
switch (newTuneDirection) {
case INCREASE_BLOCK_CACHE_SIZE:
newBlockCacheSize = context.getCurBlockCacheSize() + step;
newMemstoreSize = context.getCurMemStoreSize() - step;
rollingStatsForTunerSteps.insertDataValue(-(int)(step*100000));
decayingTunerStepSizeSum = (decayingTunerStepSizeSum - step)/2.00f;
break;
case INCREASE_MEMSTORE_SIZE:
newBlockCacheSize = context.getCurBlockCacheSize() - step;
newMemstoreSize = context.getCurMemStoreSize() + step;
rollingStatsForTunerSteps.insertDataValue((int)(step*100000));
decayingTunerStepSizeSum = (decayingTunerStepSizeSum + step)/2.00f;
break;
default:
prevTuneDirection = StepDirection.NEUTRAL;
rollingStatsForTunerSteps.insertDataValue(0);
decayingTunerStepSizeSum = (decayingTunerStepSizeSum)/2.00f;
return NO_OP_TUNER_RESULT;
}
// Check we are within max/min bounds.
@ -303,6 +335,7 @@ class DefaultHeapMemoryTuner implements HeapMemoryTuner {
this.rollingStatsForCacheMisses = new RollingStatCalculator(this.tunerLookupPeriods);
this.rollingStatsForFlushes = new RollingStatCalculator(this.tunerLookupPeriods);
this.rollingStatsForEvictions = new RollingStatCalculator(this.tunerLookupPeriods);
this.rollingStatsForTunerSteps = new RollingStatCalculator(this.tunerLookupPeriods);
}
private enum StepDirection{