HBASE-6131 Add attribution for code added by HBASE-5533 metrics
git-svn-id: https://svn.apache.org/repos/asf/hbase/trunk@1344299 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Michael Stack
parent
31d4659cce
commit
20bd3f02d0
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@ -297,6 +297,10 @@
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<dependencies>
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<dependencies>
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<!-- General dependencies -->
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<!-- General dependencies -->
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<dependency>
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<groupId>com.yammer.metrics</groupId>
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<artifactId>metrics-core</artifactId>
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</dependency>
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<dependency>
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<dependency>
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<groupId>com.google.guava</groupId>
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<groupId>com.google.guava</groupId>
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<artifactId>guava</artifactId>
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<artifactId>guava</artifactId>
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@ -1,226 +0,0 @@
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/**
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.hadoop.hbase.metrics.histogram;
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import java.util.ArrayList;
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import java.util.Random;
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import java.util.concurrent.ConcurrentSkipListMap;
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import java.util.concurrent.Executors;
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import java.util.concurrent.ScheduledExecutorService;
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import java.util.concurrent.ThreadFactory;
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import java.util.concurrent.TimeUnit;
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import java.util.concurrent.atomic.AtomicInteger;
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import java.util.concurrent.atomic.AtomicLong;
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import java.util.concurrent.locks.ReentrantReadWriteLock;
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/**
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* An exponentially-decaying random sample of {@code long}s.
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* Uses Cormode et al's forward-decaying priority reservoir sampling method
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* to produce a statistically representative sample, exponentially biased
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* towards newer entries.
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*
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* see Cormode et al.
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* Forward Decay: A Practical Time Decay Model for Streaming Systems. ICDE '09
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*/
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public class ExponentiallyDecayingSample implements Sample {
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private static final Random RANDOM = new Random();
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private static final long RESCALE_THRESHOLD = TimeUnit.HOURS.toNanos(1);
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private static final ScheduledExecutorService TICK_SERVICE =
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Executors.newScheduledThreadPool(1,
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getNamedDaemonThreadFactory(Thread.currentThread().getName() + ".decayingSampleTick."));
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private static volatile long CURRENT_TICK =
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TimeUnit.MILLISECONDS.toSeconds(System.currentTimeMillis());
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static {
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// sample at twice our signal's frequency (1Hz) per the Nyquist theorem
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TICK_SERVICE.scheduleAtFixedRate(new Runnable() {
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@Override
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public void run() {
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CURRENT_TICK =
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TimeUnit.MILLISECONDS.toSeconds(System.currentTimeMillis());
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}
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}, 0, 500, TimeUnit.MILLISECONDS);
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}
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private final ConcurrentSkipListMap<Double, Long> values =
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new ConcurrentSkipListMap<Double, Long>();
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private final ReentrantReadWriteLock lock = new ReentrantReadWriteLock();
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private final AtomicLong count = new AtomicLong(0);
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private final AtomicLong nextScaleTime = new AtomicLong(0);
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private final double alpha;
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private final int reservoirSize;
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private volatile long startTime;
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/**
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* Constructor for an ExponentiallyDecayingSample.
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*
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* @param reservoirSize the number of samples to keep in the reservoir
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* @param alpha the exponential decay factor; the higher this is,
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* the more biased the sample will be towards newer
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* values
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*/
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public ExponentiallyDecayingSample(int reservoirSize, double alpha) {
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this.alpha = alpha;
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this.reservoirSize = reservoirSize;
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clear();
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}
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@Override
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public void clear() {
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lockForRescale();
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try {
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values.clear();
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count.set(0);
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this.startTime = CURRENT_TICK;
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nextScaleTime.set(System.nanoTime() + RESCALE_THRESHOLD);
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} finally {
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unlockForRescale();
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}
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}
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@Override
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public int size() {
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return (int) Math.min(reservoirSize, count.get());
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}
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@Override
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public void update(long value) {
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update(value, CURRENT_TICK);
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}
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/**
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* Adds an old value with a fixed timestamp to the sample.
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*
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* @param value the value to be added
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* @param timestamp the epoch timestamp of {@code value} in seconds
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*/
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public void update(long value, long timestamp) {
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lockForRegularUsage();
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try {
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final double priority = weight(timestamp - startTime)
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/ RANDOM.nextDouble();
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final long newCount = count.incrementAndGet();
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if (newCount <= reservoirSize) {
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values.put(priority, value);
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} else {
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Double first = values.firstKey();
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if (first < priority) {
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if (values.putIfAbsent(priority, value) == null) {
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// ensure we always remove an item
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while (values.remove(first) == null) {
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first = values.firstKey();
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}
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}
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}
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}
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} finally {
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unlockForRegularUsage();
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}
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final long now = System.nanoTime();
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final long next = nextScaleTime.get();
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if (now >= next) {
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rescale(now, next);
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}
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}
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@Override
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public Snapshot getSnapshot() {
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lockForRegularUsage();
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try {
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return new Snapshot(values.values());
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} finally {
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unlockForRegularUsage();
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}
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}
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private double weight(long t) {
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return Math.exp(alpha * t);
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}
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/* "A common feature of the above techniques—indeed, the key technique that
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* allows us to track the decayed weights efficiently—is that they maintain
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* counts and other quantities based on g(ti − L), and only scale by g(t − L)
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* at query time. But while g(ti −L)/g(t−L) is guaranteed to lie between zero
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* and one, the intermediate values of g(ti − L) could become very large. For
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* polynomial functions, these values should not grow too large, and should
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* be effectively represented in practice by floating point values without
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* loss of precision. For exponential functions, these values could grow
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* quite large as new values of (ti − L) become large, and potentially
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* exceed the capacity of common floating point types. However, since the
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* values stored by the algorithms are linear combinations of g values
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* (scaled sums), they can be rescaled relative to a new landmark. That is,
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* by the analysis of exponential decay in Section III-A, the choice of L
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* does not affect the final result. We can therefore multiply each value
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* based on L by a factor of exp(−α(L′ − L)), and obtain the correct value
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* as if we had instead computed relative to a new landmark L′ (and then use
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* this new L′ at query time). This can be done with a linear pass over
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* whatever data structure is being used."
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*/
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private void rescale(long now, long next) {
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if (nextScaleTime.compareAndSet(next, now + RESCALE_THRESHOLD)) {
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lockForRescale();
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try {
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final long oldStartTime = startTime;
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this.startTime = CURRENT_TICK;
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final ArrayList<Double> keys = new ArrayList<Double>(values.keySet());
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for (Double key : keys) {
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final Long value = values.remove(key);
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values.put(key * Math.exp(-alpha * (startTime - oldStartTime)),
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value);
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}
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} finally {
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unlockForRescale();
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}
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}
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}
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private void unlockForRescale() {
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lock.writeLock().unlock();
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}
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private void lockForRescale() {
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lock.writeLock().lock();
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}
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private void lockForRegularUsage() {
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lock.readLock().lock();
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}
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private void unlockForRegularUsage() {
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lock.readLock().unlock();
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}
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private static ThreadFactory getNamedDaemonThreadFactory(final String prefix) {
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return new ThreadFactory() {
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private final AtomicInteger threadNumber = new AtomicInteger(1);
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@Override
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public Thread newThread(Runnable r) {
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Thread t= new Thread(r, prefix + threadNumber.getAndIncrement());
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t.setDaemon(true);
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return t;
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}
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};
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}
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}
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@ -25,6 +25,11 @@ import org.apache.hadoop.metrics.MetricsRecord;
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import org.apache.hadoop.metrics.util.MetricsBase;
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import org.apache.hadoop.metrics.util.MetricsBase;
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import org.apache.hadoop.metrics.util.MetricsRegistry;
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import org.apache.hadoop.metrics.util.MetricsRegistry;
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import com.yammer.metrics.stats.Sample;
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import com.yammer.metrics.stats.Snapshot;
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import com.yammer.metrics.stats.UniformSample;
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import com.yammer.metrics.stats.ExponentiallyDecayingSample;
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public class MetricsHistogram extends MetricsBase {
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public class MetricsHistogram extends MetricsBase {
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// 1028 items implies 99.9% CI w/ 5% margin of error
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// 1028 items implies 99.9% CI w/ 5% margin of error
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@ -1,49 +0,0 @@
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/**
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.hadoop.hbase.metrics.histogram;
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/**
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* A statistically representative sample of items from a stream.
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*/
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public interface Sample {
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/**
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* Clears all recorded values.
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*/
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void clear();
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/**
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* Returns the number of values recorded.
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*
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* @return the number of values recorded
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*/
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int size();
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/**
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* Adds a new recorded value to the sample.
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*
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* @param value a new recorded value
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*/
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void update(long value);
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/**
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* Returns a snapshot of the sample's values.
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*
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* @return a snapshot of the sample's values
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*/
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Snapshot getSnapshot();
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}
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@ -1,166 +0,0 @@
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/**
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.hadoop.hbase.metrics.histogram;
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import java.io.File;
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import java.io.IOException;
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import java.io.PrintWriter;
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import java.util.Arrays;
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import java.util.Collection;
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/**
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* A snapshot of all the information seen in a Sample.
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*/
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public class Snapshot {
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private static final double MEDIAN_Q = 0.5;
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private static final double P75_Q = 0.75;
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private static final double P95_Q = 0.95;
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private static final double P98_Q = 0.98;
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private static final double P99_Q = 0.99;
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private static final double P999_Q = 0.999;
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private final double[] values;
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/**
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* Create a new {@link Snapshot} with the given values.
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*
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* @param values an unordered set of values in the sample
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*/
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public Snapshot(Collection<Long> values) {
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final Object[] copy = values.toArray();
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this.values = new double[copy.length];
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for (int i = 0; i < copy.length; i++) {
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this.values[i] = (Long) copy[i];
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}
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Arrays.sort(this.values);
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}
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/**
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* Create a new {@link Snapshot} with the given values.
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*
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* @param values an unordered set of values in the sample
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*/
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public Snapshot(double[] values) {
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this.values = new double[values.length];
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System.arraycopy(values, 0, this.values, 0, values.length);
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Arrays.sort(this.values);
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}
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/**
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* Returns the value at the given quantile.
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*
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* @param quantile a given quantile, in [0..1]
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* @return the value in the distribution at quantile
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*/
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public double getValue(double quantile) {
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if (quantile < 0.0 || quantile > 1.0) {
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throw new IllegalArgumentException(quantile + " is not in [0..1]");
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||||||
}
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||||||
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if (values.length == 0) {
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return 0.0;
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}
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final double pos = quantile * (values.length + 1);
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if (pos < 1) {
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return values[0];
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}
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if (pos >= values.length) {
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return values[values.length - 1];
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}
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final double lower = values[(int) pos - 1];
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final double upper = values[(int) pos];
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return lower + (pos - Math.floor(pos)) * (upper - lower);
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|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* Returns the number of values in the snapshot.
|
|
||||||
*
|
|
||||||
* @return the number of values in the snapshot
|
|
||||||
*/
|
|
||||||
public int size() {
|
|
||||||
return values.length;
|
|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* Returns the median value in the distribution.
|
|
||||||
*
|
|
||||||
* @return the median value in the distribution
|
|
||||||
*/
|
|
||||||
public double getMedian() {
|
|
||||||
return getValue(MEDIAN_Q);
|
|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* Returns the value at the 75th percentile in the distribution.
|
|
||||||
*
|
|
||||||
* @return the value at the 75th percentile in the distribution
|
|
||||||
*/
|
|
||||||
public double get75thPercentile() {
|
|
||||||
return getValue(P75_Q);
|
|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* Returns the value at the 95th percentile in the distribution.
|
|
||||||
*
|
|
||||||
* @return the value at the 95th percentile in the distribution
|
|
||||||
*/
|
|
||||||
public double get95thPercentile() {
|
|
||||||
return getValue(P95_Q);
|
|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* Returns the value at the 98th percentile in the distribution.
|
|
||||||
*
|
|
||||||
* @return the value at the 98th percentile in the distribution
|
|
||||||
*/
|
|
||||||
public double get98thPercentile() {
|
|
||||||
return getValue(P98_Q);
|
|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* Returns the value at the 99th percentile in the distribution.
|
|
||||||
*
|
|
||||||
* @return the value at the 99th percentile in the distribution
|
|
||||||
*/
|
|
||||||
public double get99thPercentile() {
|
|
||||||
return getValue(P99_Q);
|
|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* Returns the value at the 99.9th percentile in the distribution.
|
|
||||||
*
|
|
||||||
* @return the value at the 99.9th percentile in the distribution
|
|
||||||
*/
|
|
||||||
public double get999thPercentile() {
|
|
||||||
return getValue(P999_Q);
|
|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* Returns the entire set of values in the snapshot.
|
|
||||||
*
|
|
||||||
* @return the entire set of values in the snapshot
|
|
||||||
*/
|
|
||||||
public double[] getValues() {
|
|
||||||
return Arrays.copyOf(values, values.length);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
@ -1,105 +0,0 @@
|
||||||
/**
|
|
||||||
* Licensed to the Apache Software Foundation (ASF) under one
|
|
||||||
* or more contributor license agreements. See the NOTICE file
|
|
||||||
* distributed with this work for additional information
|
|
||||||
* regarding copyright ownership. The ASF licenses this file
|
|
||||||
* to you under the Apache License, Version 2.0 (the
|
|
||||||
* "License"); you may not use this file except in compliance
|
|
||||||
* with the License. You may obtain a copy of the License at
|
|
||||||
*
|
|
||||||
* http://www.apache.org/licenses/LICENSE-2.0
|
|
||||||
*
|
|
||||||
* Unless required by applicable law or agreed to in writing, software
|
|
||||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
||||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
||||||
* See the License for the specific language governing permissions and
|
|
||||||
* limitations under the License.
|
|
||||||
*/
|
|
||||||
package org.apache.hadoop.hbase.metrics.histogram;
|
|
||||||
|
|
||||||
import java.util.ArrayList;
|
|
||||||
import java.util.List;
|
|
||||||
import java.util.Random;
|
|
||||||
import java.util.concurrent.atomic.AtomicLong;
|
|
||||||
import java.util.concurrent.atomic.AtomicLongArray;
|
|
||||||
|
|
||||||
/**
|
|
||||||
* A random sample of a stream of longs. Uses Vitter's Algorithm R to produce a
|
|
||||||
* statistically representative sample.
|
|
||||||
*
|
|
||||||
* see: http://www.cs.umd.edu/~samir/498/vitter.pdf
|
|
||||||
*/
|
|
||||||
public class UniformSample implements Sample {
|
|
||||||
|
|
||||||
private static final Random RANDOM = new Random();
|
|
||||||
private static final int BITS_PER_LONG = 63;
|
|
||||||
|
|
||||||
private final AtomicLong count = new AtomicLong();
|
|
||||||
private final AtomicLongArray values;
|
|
||||||
|
|
||||||
/**
|
|
||||||
* Creates a new UniformSample
|
|
||||||
*
|
|
||||||
* @param reservoirSize the number of samples to keep
|
|
||||||
*/
|
|
||||||
public UniformSample(int reservoirSize) {
|
|
||||||
this.values = new AtomicLongArray(reservoirSize);
|
|
||||||
clear();
|
|
||||||
}
|
|
||||||
|
|
||||||
@Override
|
|
||||||
public void clear() {
|
|
||||||
for (int i = 0; i < values.length(); i++) {
|
|
||||||
values.set(i, 0);
|
|
||||||
}
|
|
||||||
count.set(0);
|
|
||||||
}
|
|
||||||
|
|
||||||
@Override
|
|
||||||
public int size() {
|
|
||||||
final long c = count.get();
|
|
||||||
if (c > values.length()) {
|
|
||||||
return values.length();
|
|
||||||
}
|
|
||||||
return (int) c;
|
|
||||||
}
|
|
||||||
|
|
||||||
@Override
|
|
||||||
public void update(long value) {
|
|
||||||
final long c = count.incrementAndGet();
|
|
||||||
if (c <= values.length()) {
|
|
||||||
values.set((int) c - 1, value);
|
|
||||||
} else {
|
|
||||||
final long r = nextLong(c);
|
|
||||||
if (r < values.length()) {
|
|
||||||
values.set((int) r, value);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* Get a pseudo-random long uniformly between 0 and n-1. Stolen from
|
|
||||||
* {@link java.util.Random#nextInt()}.
|
|
||||||
*
|
|
||||||
* @param n the bound
|
|
||||||
* @return a value select randomly from the range {@code [0..n)}.
|
|
||||||
*/
|
|
||||||
private static long nextLong(long n) {
|
|
||||||
long bits, val;
|
|
||||||
do {
|
|
||||||
bits = RANDOM.nextLong() & (~(1L << BITS_PER_LONG));
|
|
||||||
val = bits % n;
|
|
||||||
} while (bits - val + (n - 1) < 0L);
|
|
||||||
return val;
|
|
||||||
}
|
|
||||||
|
|
||||||
@Override
|
|
||||||
public Snapshot getSnapshot() {
|
|
||||||
final int s = size();
|
|
||||||
final List<Long> copy = new ArrayList<Long>(s);
|
|
||||||
for (int i = 0; i < s; i++) {
|
|
||||||
copy.add(values.get(i));
|
|
||||||
}
|
|
||||||
return new Snapshot(copy);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
@ -33,7 +33,7 @@ import org.apache.hadoop.hbase.metrics.HBaseInfo;
|
||||||
import org.apache.hadoop.hbase.metrics.MetricsRate;
|
import org.apache.hadoop.hbase.metrics.MetricsRate;
|
||||||
import org.apache.hadoop.hbase.metrics.PersistentMetricsTimeVaryingRate;
|
import org.apache.hadoop.hbase.metrics.PersistentMetricsTimeVaryingRate;
|
||||||
import org.apache.hadoop.hbase.metrics.histogram.MetricsHistogram;
|
import org.apache.hadoop.hbase.metrics.histogram.MetricsHistogram;
|
||||||
import org.apache.hadoop.hbase.metrics.histogram.Snapshot;
|
import com.yammer.metrics.stats.Snapshot;
|
||||||
import org.apache.hadoop.hbase.regionserver.wal.HLog;
|
import org.apache.hadoop.hbase.regionserver.wal.HLog;
|
||||||
import org.apache.hadoop.hbase.util.Pair;
|
import org.apache.hadoop.hbase.util.Pair;
|
||||||
import org.apache.hadoop.hbase.util.Strings;
|
import org.apache.hadoop.hbase.util.Strings;
|
||||||
|
|
|
||||||
|
|
@ -20,8 +20,8 @@ package org.apache.hadoop.hbase.metrics;
|
||||||
|
|
||||||
import junit.framework.Assert;
|
import junit.framework.Assert;
|
||||||
|
|
||||||
import org.apache.hadoop.hbase.metrics.histogram.ExponentiallyDecayingSample;
|
import com.yammer.metrics.stats.ExponentiallyDecayingSample;
|
||||||
import org.apache.hadoop.hbase.metrics.histogram.Snapshot;
|
import com.yammer.metrics.stats.Snapshot;
|
||||||
import org.apache.hadoop.hbase.SmallTests;
|
import org.apache.hadoop.hbase.SmallTests;
|
||||||
import org.junit.Test;
|
import org.junit.Test;
|
||||||
import org.junit.experimental.categories.Category;
|
import org.junit.experimental.categories.Category;
|
||||||
|
|
|
||||||
|
|
@ -22,7 +22,7 @@ import java.util.Arrays;
|
||||||
import java.util.Random;
|
import java.util.Random;
|
||||||
|
|
||||||
import org.apache.hadoop.hbase.metrics.histogram.MetricsHistogram;
|
import org.apache.hadoop.hbase.metrics.histogram.MetricsHistogram;
|
||||||
import org.apache.hadoop.hbase.metrics.histogram.Snapshot;
|
import com.yammer.metrics.stats.Snapshot;
|
||||||
import org.apache.hadoop.hbase.SmallTests;
|
import org.apache.hadoop.hbase.SmallTests;
|
||||||
import org.junit.Assert;
|
import org.junit.Assert;
|
||||||
import org.junit.Test;
|
import org.junit.Test;
|
||||||
|
|
|
||||||
6
pom.xml
6
pom.xml
|
|
@ -658,6 +658,7 @@
|
||||||
<commons-logging.version>1.1.1</commons-logging.version>
|
<commons-logging.version>1.1.1</commons-logging.version>
|
||||||
<commons-math.version>2.1</commons-math.version>
|
<commons-math.version>2.1</commons-math.version>
|
||||||
<commons-configuration.version>1.6</commons-configuration.version>
|
<commons-configuration.version>1.6</commons-configuration.version>
|
||||||
|
<metrics-core.version>2.1.2</metrics-core.version>
|
||||||
<guava.version>11.0.2</guava.version>
|
<guava.version>11.0.2</guava.version>
|
||||||
<jackson.version>1.8.8</jackson.version>
|
<jackson.version>1.8.8</jackson.version>
|
||||||
<jasper.version>5.5.23</jasper.version>
|
<jasper.version>5.5.23</jasper.version>
|
||||||
|
|
@ -743,6 +744,11 @@
|
||||||
</dependency>
|
</dependency>
|
||||||
|
|
||||||
<!-- General dependencies -->
|
<!-- General dependencies -->
|
||||||
|
<dependency>
|
||||||
|
<groupId>com.yammer.metrics</groupId>
|
||||||
|
<artifactId>metrics-core</artifactId>
|
||||||
|
<version>${metrics-core.version}</version>
|
||||||
|
</dependency>
|
||||||
<dependency>
|
<dependency>
|
||||||
<groupId>com.google.guava</groupId>
|
<groupId>com.google.guava</groupId>
|
||||||
<artifactId>guava</artifactId>
|
<artifactId>guava</artifactId>
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue