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* 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.cassandra.metrics;
import java.io.OutputStream;
import java.io.OutputStreamWriter;
import java.io.PrintWriter;
import java.nio.charset.StandardCharsets;
import java.util.Arrays;
import java.util.concurrent.atomic.AtomicBoolean;
import java.util.concurrent.atomic.AtomicLongArray;
import java.util.concurrent.locks.ReentrantReadWriteLock;
import com.google.common.annotations.VisibleForTesting;
import com.codahale.metrics.Clock;
import com.codahale.metrics.Reservoir;
import com.codahale.metrics.Snapshot;
import org.apache.cassandra.utils.EstimatedHistogram;
/**
* A decaying histogram reservoir where values collected during each minute will be twice as significant as the values
* collected in the previous minute. Measured values are collected in variable sized buckets, using small buckets in the
* lower range and larger buckets in the upper range. Use this histogram when you want to know if the distribution of
* the underlying data stream has changed recently and you want high resolution on values in the lower range.
*
* The histogram use forward decay [1] to make recent values more significant. The forward decay factor will be doubled
* every minute (half-life time set to 60 seconds) [2]. The forward decay landmark is reset every 30 minutes (or at
* first read/update after 30 minutes). During landmark reset, updates and reads in the reservoir will be blocked in a
* fashion similar to the one used in the metrics library [3]. The 30 minute rescale interval is used based on the
* assumption that in an extreme case we would have to collect a metric 1M times for a single bucket each second. By the
* end of the 30:th minute all collected values will roughly add up to 1.000.000 * 60 * pow(2, 30) which can be
* represented with 56 bits giving us some head room in a signed 64 bit long.
*
* Internally two reservoirs are maintained, one with decay and one without decay. All public getters in a {@link Snapshot}
* will expose the decay functionality with the exception of the {@link Snapshot#getValues()} which will return values
* from the reservoir without decay. This makes it possible for the caller to maintain precise deltas in an interval of
* its choise.
*
* The bucket size starts at 1 and grows by 1.2 each time (rounding and removing duplicates). It goes from 1 to around
* 18T by default (creating 164+1 buckets), which will give a timing resolution from microseconds to roughly 210 days,
* with less precision as the numbers get larger.
*
* The series of values to which the counts in `decayingBuckets` correspond:
* 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 17, 20, 24, 29, 35, 42, 50, 60, 72 etc.
* Thus, a `decayingBuckets` of [0, 0, 1, 10] would mean we had seen 1 value of 3 and 10 values of 4.
*
* Each bucket represents values from (previous bucket offset, current offset].
*
* [1]: http://dimacs.rutgers.edu/~graham/pubs/papers/fwddecay.pdf
* [2]: https://en.wikipedia.org/wiki/Half-life
* [3]: https://github.com/dropwizard/metrics/blob/v3.1.2/metrics-core/src/main/java/com/codahale/metrics/ExponentiallyDecayingReservoir.java
*/
public class DecayingEstimatedHistogramReservoir implements Reservoir
{
/**
* The default number of decayingBuckets. Use this bucket count to reduce memory allocation for bucket offsets.
*/
public static final int DEFAULT_BUCKET_COUNT = 164;
public static final boolean DEFAULT_ZERO_CONSIDERATION = false;
// The offsets used with a default sized bucket array without a separate bucket for zero values.
public static final long[] DEFAULT_WITHOUT_ZERO_BUCKET_OFFSETS = EstimatedHistogram.newOffsets(DEFAULT_BUCKET_COUNT, false);
// The offsets used with a default sized bucket array with a separate bucket for zero values.
public static final long[] DEFAULT_WITH_ZERO_BUCKET_OFFSETS = EstimatedHistogram.newOffsets(DEFAULT_BUCKET_COUNT, true);
// Represents the bucket offset as created by {@link EstimatedHistogram#newOffsets()}
private final long[] bucketOffsets;
// decayingBuckets and buckets are one element longer than bucketOffsets -- the last element is values greater than the last offset
private final AtomicLongArray decayingBuckets;
private final AtomicLongArray buckets;
public static final long HALF_TIME_IN_S = 60L;
public static final double MEAN_LIFETIME_IN_S = HALF_TIME_IN_S / Math.log(2.0);
public static final long LANDMARK_RESET_INTERVAL_IN_MS = 30L * 60L * 1000L;
private final AtomicBoolean rescaling = new AtomicBoolean(false);
private volatile long decayLandmark;
private final ReentrantReadWriteLock lock = new ReentrantReadWriteLock();
// Wrapper around System.nanoTime() to simplify unit testing.
private final Clock clock;
/**
* Construct a decaying histogram with default number of buckets and without considering zeroes.
*/
public DecayingEstimatedHistogramReservoir()
{
this(DEFAULT_ZERO_CONSIDERATION, DEFAULT_BUCKET_COUNT, Clock.defaultClock());
}
/**
* Construct a decaying histogram with default number of buckets.
*
* @param considerZeroes when true, 0-value measurements in a separate bucket, otherwise they will be collected in
* same bucket as 1-value measurements
*/
public DecayingEstimatedHistogramReservoir(boolean considerZeroes)
{
this(considerZeroes, DEFAULT_BUCKET_COUNT, Clock.defaultClock());
}
/**
* Construct a decaying histogram.
*
* @param considerZeroes when true, 0-value measurements in a separate bucket, otherwise they will be collected in
* same bucket as 1-value measurements
* @param bucketCount number of buckets used to collect measured values
*/
public DecayingEstimatedHistogramReservoir(boolean considerZeroes, int bucketCount)
{
this(considerZeroes, bucketCount, Clock.defaultClock());
}
@VisibleForTesting
DecayingEstimatedHistogramReservoir(boolean considerZeroes, int bucketCount, Clock clock)
{
if (bucketCount == DEFAULT_BUCKET_COUNT)
{
if (considerZeroes == true)
{
bucketOffsets = DEFAULT_WITH_ZERO_BUCKET_OFFSETS;
}
else
{
bucketOffsets = DEFAULT_WITHOUT_ZERO_BUCKET_OFFSETS;
}
}
else
{
bucketOffsets = EstimatedHistogram.newOffsets(bucketCount, considerZeroes);
}
decayingBuckets = new AtomicLongArray(bucketOffsets.length + 1);
buckets = new AtomicLongArray(bucketOffsets.length + 1);
this.clock = clock;
decayLandmark = clock.getTime();
}
/**
* Increments the count of the bucket closest to n, rounding UP.
*
* @param value the data point to add to the histogram
*/
public void update(long value)
{
long now = clock.getTime();
rescaleIfNeeded(now);
int index = Arrays.binarySearch(bucketOffsets, value);
if (index < 0)
{
// inexact match, take the first bucket higher than n
index = -index - 1;
}
// else exact match; we're good
lockForRegularUsage();
try
{
decayingBuckets.getAndAdd(index, Math.round(forwardDecayWeight(now)));
}
finally
{
unlockForRegularUsage();
}
buckets.getAndIncrement(index);
}
private double forwardDecayWeight(long now)
{
return Math.exp(((now - decayLandmark) / 1000.0) / MEAN_LIFETIME_IN_S);
}
/**
* Return the number of buckets where recorded values are stored.
*
* This method does not return the number of recorded values as suggested by the {@link Reservoir} interface.
*
* @return the number of buckets
*/
public int size()
{
return decayingBuckets.length();
}
/**
* Returns a snapshot of the decaying values in this reservoir.
*
* Non-decaying reservoir will not be included in the snapshot.
*
* @return the snapshot
*/
public Snapshot getSnapshot()
{
rescaleIfNeeded();
lockForRegularUsage();
try
{
return new EstimatedHistogramReservoirSnapshot(this);
}
finally
{
unlockForRegularUsage();
}
}
/**
* @return true if this histogram has overflowed -- that is, a value larger than our largest bucket could bound was added
*/
@VisibleForTesting
boolean isOverflowed()
{
return decayingBuckets.get(decayingBuckets.length() - 1) > 0;
}
private void rescaleIfNeeded()
{
rescaleIfNeeded(clock.getTime());
}
private void rescaleIfNeeded(long now)
{
if (needRescale(now))
{
if (rescaling.compareAndSet(false, true))
{
try
{
rescale(now);
}
finally
{
rescaling.set(false);
}
}
}
}
private void rescale(long now)
{
// Check again to make sure that another thread didn't complete rescale already
if (needRescale(now))
{
lockForRescale();
try
{
final double rescaleFactor = forwardDecayWeight(now);
decayLandmark = now;
final int bucketCount = decayingBuckets.length();
for (int i = 0; i < bucketCount; i++)
{
long newValue = Math.round((decayingBuckets.get(i) / rescaleFactor));
decayingBuckets.set(i, newValue);
}
}
finally
{
unlockForRescale();
}
}
}
private boolean needRescale(long now)
{
return (now - decayLandmark) > LANDMARK_RESET_INTERVAL_IN_MS;
}
@VisibleForTesting
public void clear()
{
lockForRescale();
try
{
final int bucketCount = decayingBuckets.length();
for (int i = 0; i < bucketCount; i++)
{
decayingBuckets.set(i, 0L);
buckets.set(i, 0L);
}
}
finally
{
unlockForRescale();
}
}
private void lockForRegularUsage()
{
this.lock.readLock().lock();
}
private void unlockForRegularUsage()
{
this.lock.readLock().unlock();
}
private void lockForRescale()
{
this.lock.writeLock().lock();
}
private void unlockForRescale()
{
this.lock.writeLock().unlock();
}
/**
* Represents a snapshot of the decaying histogram.
*
* The decaying buckets are copied into a snapshot array to give a consistent view for all getters. However, the
* copy is made without a write-lock and so other threads may change the buckets while the array is copied,
* probably causign a slight skew up in the quantiles and mean values.
*
* The decaying buckets will be used for quantile calculations and mean values, but the non decaying buckets will be
* exposed for calls to {@link Snapshot#getValues()}.
*/
private class EstimatedHistogramReservoirSnapshot extends Snapshot
{
private final long[] decayingBuckets;
public EstimatedHistogramReservoirSnapshot(DecayingEstimatedHistogramReservoir reservoir)
{
final int length = reservoir.decayingBuckets.length();
final double rescaleFactor = forwardDecayWeight(clock.getTime());
this.decayingBuckets = new long[length];
for (int i = 0; i < length; i++)
this.decayingBuckets[i] = Math.round(reservoir.decayingBuckets.get(i) / rescaleFactor);
}
/**
* Get the estimated value at the specified quantile in the distribution.
*
* @param quantile the quantile specified as a value between 0.0 (zero) and 1.0 (one)
* @return estimated value at given quantile
* @throws IllegalStateException in case the histogram overflowed
*/
public double getValue(double quantile)
{
assert quantile >= 0 && quantile <= 1.0;
final int lastBucket = decayingBuckets.length - 1;
if (decayingBuckets[lastBucket] > 0)
throw new IllegalStateException("Unable to compute when histogram overflowed");
final long qcount = (long) Math.ceil(count() * quantile);
if (qcount == 0)
return 0;
long elements = 0;
for (int i = 0; i < lastBucket; i++)
{
elements += decayingBuckets[i];
if (elements >= qcount)
return bucketOffsets[i];
}
return 0;
}
/**
* Will return a snapshot of the non-decaying buckets.
*
* The values returned will not be consistent with the quantile and mean values. The caller must be aware of the
* offsets created by {@link EstimatedHistogram#getBucketOffsets()} to make use of the values returned.
*
* @return a snapshot of the non-decaying buckets.
*/
public long[] getValues()
{
final int length = buckets.length();
long[] values = new long[length];
for (int i = 0; i < length; i++)
values[i] = buckets.get(i);
return values;
}
/**
* Return the number of buckets where recorded values are stored.
*
* This method does not return the number of recorded values as suggested by the {@link Snapshot} interface.
*
* @return the number of buckets
*/
public int size()
{
return decayingBuckets.length;
}
/**
* Return the number of registered values taking forward decay into account.
*
* @return the sum of all bucket values
*/
private long count()
{
long sum = 0L;
for (int i = 0; i < decayingBuckets.length; i++)
sum += decayingBuckets[i];
return sum;
}
/**
* Get the estimated max-value that could have been added to this reservoir.
*
* As values are collected in variable sized buckets, the actual max value recored in the reservoir may be less
* than the value returned.
*
* @return the largest value that could have been added to this reservoir, or Long.MAX_VALUE if the reservoir
* overflowed
*/
public long getMax()
{
final int lastBucket = decayingBuckets.length - 1;
if (decayingBuckets[lastBucket] > 0)
return Long.MAX_VALUE;
for (int i = lastBucket - 1; i >= 0; i--)
{
if (decayingBuckets[i] > 0)
return bucketOffsets[i];
}
return 0;
}
/**
* Get the estimated mean value in the distribution.
*
* @return the mean histogram value (average of bucket offsets, weighted by count)
* @throws IllegalStateException if any values were greater than the largest bucket threshold
*/
public double getMean()
{
final int lastBucket = decayingBuckets.length - 1;
if (decayingBuckets[lastBucket] > 0)
throw new IllegalStateException("Unable to compute when histogram overflowed");
long elements = 0;
long sum = 0;
for (int i = 0; i < lastBucket; i++)
{
long bCount = decayingBuckets[i];
elements += bCount;
sum += bCount * bucketOffsets[i];
}
return (double) sum / elements;
}
/**
* Get the estimated min-value that could have been added to this reservoir.
*
* As values are collected in variable sized buckets, the actual min value recored in the reservoir may be
* higher than the value returned.
*
* @return the smallest value that could have been added to this reservoir
*/
public long getMin()
{
for (int i = 0; i < decayingBuckets.length; i++)
{
if (decayingBuckets[i] > 0)
return i == 0 ? 0 : 1 + bucketOffsets[i - 1];
}
return 0;
}
/**
* Get the estimated standard deviation of the values added to this reservoir.
*
* As values are collected in variable sized buckets, the actual deviation may be more or less than the value
* returned.
*
* @return an estimate of the standard deviation
*/
public double getStdDev()
{
final int lastBucket = decayingBuckets.length - 1;
if (decayingBuckets[lastBucket] > 0)
throw new IllegalStateException("Unable to compute when histogram overflowed");
final long count = count();
if(count <= 1)
{
return 0.0D;
}
else
{
double mean = this.getMean();
double sum = 0.0D;
for(int i = 0; i < lastBucket; ++i)
{
long value = bucketOffsets[i];
double diff = value - mean;
sum += diff * diff * decayingBuckets[i];
}
return Math.sqrt(sum / (count - 1));
}
}
public void dump(OutputStream output)
{
try (PrintWriter out = new PrintWriter(new OutputStreamWriter(output, StandardCharsets.UTF_8)))
{
int length = decayingBuckets.length;
for(int i = 0; i < length; ++i)
{
out.printf("%d%n", decayingBuckets[i]);
}
}
}
}
}