content: /**
   * 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 com.alibaba.jstorm.common.metric.codahale;
  
  import com.codahale.metrics.Reservoir;
  import com.codahale.metrics.Snapshot;
  import com.codahale.metrics.WeightedSnapshot;
  import java.util.ArrayList;
  import java.util.concurrent.ConcurrentSkipListMap;
  import java.util.concurrent.TimeUnit;
  import java.util.concurrent.atomic.AtomicLong;
  import java.util.concurrent.locks.ReentrantReadWriteLock;
  
  import static java.lang.Math.exp;
  import static java.lang.Math.min;
  
  public class ExponentiallyDecayingReservoir implements Reservoir {
      private static final int DEFAULT_SIZE = 1028;
      private static final double DEFAULT_ALPHA = 0.015;
      private static final long RESCALE_THRESHOLD = TimeUnit.HOURS.toMillis(1);
  
      private final ConcurrentSkipListMap<Double, WeightedSnapshot.WeightedSample> values;
      private final ReentrantReadWriteLock lock;
      private final double alpha;
      private final int size;
      private final AtomicLong count;
  
      /**
       * start time, epoch seconds
       */
      private volatile long startTime;
  
      /**
       * next scale time, epoch milliseconds
       */
      private final AtomicLong nextScaleTime;
  
      /**
       * Creates a new {@link ExponentiallyDecayingReservoir} of 1028 elements, which offers a 99.9%
       * confidence level with a 5% margin of error assuming a normal distribution, and an alpha
       * factor of 0.015, which heavily biases the reservoir to the past 5 minutes of measurements.
       */
      public ExponentiallyDecayingReservoir() {
          this(DEFAULT_SIZE, DEFAULT_ALPHA);
      }
  
      /**
       * Creates a new {@link ExponentiallyDecayingReservoir}.
       *
       * @param size  the number of samples to keep in the sampling reservoir
       * @param alpha the exponential decay factor; the higher this is, the more biased the reservoir
       *              will be towards newer values
       */
      public ExponentiallyDecayingReservoir(int size, double alpha) {
          this.values = new ConcurrentSkipListMap<>();
          this.lock = new ReentrantReadWriteLock();
          this.alpha = alpha;
          this.size = size;
          this.count = new AtomicLong(0);
          this.startTime = currentTimeInSeconds();
          this.nextScaleTime = new AtomicLong(System.currentTimeMillis() + RESCALE_THRESHOLD);
      }
  
      @Override
      public int size() {
          return (int) min(size, count.get());
      }
  
      @Override
      public void update(long value) {
          update(value, currentTimeInSeconds());
      }
  
      /**
       * Adds an old value with a fixed timestamp to the reservoir.
       *
       * @param value     the value to be added
       * @param timestamp the epoch timestamp of {@code value} in seconds
       */
      public void update(long value, long timestamp) {
          rescaleIfNeeded();
          lockForRegularUsage();
          try {
              final double itemWeight = weight(timestamp - startTime);
              final WeightedSnapshot.WeightedSample sample = new WeightedSnapshot.WeightedSample(value, itemWeight);
              final double priority = itemWeight / ThreadLocalRandom.current().nextDouble();
  
              final long newCount = count.incrementAndGet();
              if (newCount <= size) {
                  values.put(priority, sample);
              } else {
                  Double first = values.firstKey();
                  if (first < priority && values.putIfAbsent(priority, sample) == null) {
                      // ensure we always remove an item
                      while (values.remove(first) == null) {
                          first = values.firstKey();
                      }
                  }
              }
          } finally {
              unlockForRegularUsage();
          }
      }
  
      private void rescaleIfNeeded() {
          final long now = System.currentTimeMillis();
          final long next = nextScaleTime.get();
          if (now >= next) {
              rescale(now, next);
          }
      }
  
      @Override
      public Snapshot getSnapshot() {
          lockForRegularUsage();
          try {
              return new WeightedSnapshot(values.values());
          } finally {
              unlockForRegularUsage();
          }
      }
  
      private long currentTimeInSeconds() {
          return TimeUnit.MILLISECONDS.toSeconds(System.currentTimeMillis());
      }
  
      private double weight(long t) {
          return exp(alpha * t);
      }
  
      /* "A common feature of the above techniques—indeed, the key technique that
       * allows us to track the decayed weights efficiently—is that they maintain
       * counts and other quantities based on g(ti − L), and only scale by g(t − L)
       * at query time. But while g(ti −L)/g(t−L) is guaranteed to lie between zero
       * and one, the intermediate values of g(ti − L) could become very large. For
       * polynomial functions, these values should not grow too large, and should be
       * effectively represented in practice by floating point values without loss of
       * precision. For exponential functions, these values could grow quite large as
       * new values of (ti − L) become large, and potentially exceed the capacity of
       * common floating point types. However, since the values stored by the
       * algorithms are linear combinations of g values (scaled sums), they can be
       * rescaled relative to a new landmark. That is, by the analysis of exponential
       * decay in Section III-A, the choice of L does not affect the final result. We
       * can therefore multiply each value based on L by a factor of exp(−α(L′ − L)),
       * and obtain the correct value as if we had instead computed relative to a new
       * landmark L′ (and then use this new L′ at query time). This can be done with
       * a linear pass over whatever data structure is being used."
       */
      private void rescale(long now, long next) {
          if (nextScaleTime.compareAndSet(next, now + RESCALE_THRESHOLD)) {
              lockForRescale();
              try {
                  final long oldStartTime = startTime;
                  this.startTime = currentTimeInSeconds();
                  final double scalingFactor = exp(-alpha * (startTime - oldStartTime));
  
                  final ArrayList<Double> keys = new ArrayList<>(values.keySet());
                  for (Double key : keys) {
                      final WeightedSnapshot.WeightedSample sample = values.remove(key);
                      final WeightedSnapshot.WeightedSample newSample =
                              new WeightedSnapshot.WeightedSample(sample.value, sample.weight * scalingFactor);
                      values.put(key * scalingFactor, newSample);
                  }
  
                  // make sure the counter is in sync with the number of stored samples.
                  count.set(values.size());
              } finally {
                  unlockForRescale();
              }
          }
      }
  
      private void unlockForRescale() {
          lock.writeLock().unlock();
      }
  
      private void lockForRescale() {
          lock.writeLock().lock();
      }
  
      private void lockForRegularUsage() {
          lock.readLock().lock();
      }
  
      private void unlockForRegularUsage() {
          lock.readLock().unlock();
      }
  }
  