/**
    * 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.impl;
  
  import java.util.concurrent.atomic.AtomicBoolean;
  import java.util.concurrent.atomic.AtomicLong;
  
  import org.apache.hadoop.hbase.classification.InterfaceAudience;
  import org.apache.hadoop.hbase.classification.InterfaceStability;
  import org.apache.hadoop.hbase.metrics.Snapshot;
  import org.apache.hadoop.hbase.util.AtomicUtils;
  import org.apache.hadoop.hbase.util.LongAdder;
  
  /**
   * FastLongHistogram is a thread-safe class that estimate distribution of data and computes the
   * quantiles.
   */
  @InterfaceAudience.Private
  @InterfaceStability.Evolving
  public class FastLongHistogram {
  
    /**
     * Default number of bins.
     */
    public static final int DEFAULT_NBINS = 255;
  
    public static final double[] DEFAULT_QUANTILES =
        new double[]{0.25, 0.5, 0.75, 0.90, 0.95, 0.98, 0.99, 0.999};
  
    /**
     * Bins is a class containing a list of buckets(or bins) for estimation histogram of some data.
     */
    private static class Bins {
      private final LongAdder[] counts;
      // inclusive
      private final long binsMin;
      // exclusive
      private final long binsMax;
      private final long bins10XMax;
      private final AtomicLong min = new AtomicLong(Long.MAX_VALUE);
      private final AtomicLong max = new AtomicLong(0L);
  
      private final LongAdder count = new LongAdder();
      private final LongAdder total = new LongAdder();
  
      // set to true when any of data has been inserted to the Bins. It is set after the counts are
      // updated.
      private final AtomicBoolean hasData = new AtomicBoolean(false);
  
      /**
       * The constructor for creating a Bins without any prior data.
       */
      public Bins(int numBins) {
        counts = createCounters(numBins + 3);
        this.binsMin = 1L;
  
        // These two numbers are total guesses
        // and should be treated as highly suspect.
        this.binsMax = 1000;
        this.bins10XMax = binsMax * 10;
      }
  
      /**
       * The constructor for creating a Bins with last Bins.
       */
      public Bins(Bins last, int numOfBins, double minQ, double maxQ) {
        long[] values = last.getQuantiles(new double[] { minQ, maxQ });
        long wd = values[1] - values[0] + 1;
        // expand minQ and maxQ in two ends back assuming uniform distribution
        this.binsMin = Math.max(0L, (long) (values[0] - wd * minQ));
        long binsMax = (long) (values[1] + wd * (1 - maxQ)) + 1;
        // make sure each of bins is at least of width 1
        this.binsMax = Math.max(binsMax, this.binsMin + numOfBins);
        this.bins10XMax = Math.max((long) (values[1] + (binsMax - 1) * 9), this.binsMax + 1);
  
        this.counts = createCounters(numOfBins + 3);
      }
  
      private LongAdder[] createCounters(int num) {
        LongAdder[] counters = new LongAdder[num];
        for (int i = 0; i < num; i++) {
          counters[i] = new LongAdder();
        }
        return counters;
     }
 
     private int getIndex(long value) {
       if (value < this.binsMin) {
         return 0;
       } else if (value > this.bins10XMax) {
         return this.counts.length - 1;
       } else if (value >= this.binsMax) {
         return this.counts.length - 2;
       }
       // compute the position
       return 1 + (int) ((value - this.binsMin) * (this.counts.length - 3) /
           (this.binsMax - this.binsMin));
 
     }
 
     /**
      * Adds a value to the histogram.
      */
     public void add(long value, long count) {
       if (value < 0) {
         // The whole computation is completely thrown off if there are negative numbers
         //
         // Normally we would throw an IllegalArgumentException however this is the metrics
         // system and it should be completely safe at all times.
         // So silently throw it away.
         return;
       }
       AtomicUtils.updateMin(min, value);
       AtomicUtils.updateMax(max, value);
 
       this.count.add(count);
       this.total.add(value * count);
 
       int pos = getIndex(value);
       this.counts[pos].add(count);
 
       // hasData needs to be updated as last
       this.hasData.set(true);
     }
 
     /**
      * Computes the quantiles give the ratios.
      */
     public long[] getQuantiles(double[] quantiles) {
       if (!this.hasData.get()) {
         // No data yet.
         return new long[quantiles.length];
       }
 
       // Make a snapshot of lowerCounter, higherCounter and bins.counts to counts.
       // This is not synchronized, but since the counter are accumulating, the result is a good
       // estimation of a snapshot.
       long[] counts = new long[this.counts.length];
       long total = 0L;
       for (int i = 0; i < this.counts.length; i++) {
         counts[i] = this.counts[i].sum();
         total += counts[i];
       }
 
       int rIndex = 0;
       double qCount = total * quantiles[0];
       long cum = 0L;
 
       long[] res = new long[quantiles.length];
       countsLoop: for (int i = 0; i < counts.length; i++) {
         // mn and mx define a value range
         long mn, mx;
         if (i == 0) {
           mn = this.min.get();
           mx = this.binsMin;
         } else if (i == counts.length - 1) {
           mn = this.bins10XMax;
           mx = this.max.get();
         } else if (i == counts.length - 2) {
           mn = this.binsMax;
           mx = this.bins10XMax;
         } else {
           mn = this.binsMin + (i - 1) * (this.binsMax - this.binsMin) / (this.counts.length - 3);
           mx = this.binsMin + i * (this.binsMax - this.binsMin) / (this.counts.length - 3);
         }
 
         if (mx < this.min.get()) {
           continue;
         }
         if (mn > this.max.get()) {
           break;
         }
         mn = Math.max(mn, this.min.get());
         mx = Math.min(mx, this.max.get());
 
         // lastCum/cum are the corresponding counts to mn/mx
         double lastCum = cum;
         cum += counts[i];
 
         // fill the results for qCount is within current range.
         while (qCount <= cum) {
           if (cum == lastCum) {
             res[rIndex] = mn;
           } else {
             res[rIndex] = (long) ((qCount - lastCum) * (mx - mn) / (cum - lastCum) + mn);
           }
 
           // move to next quantile
           rIndex++;
           if (rIndex >= quantiles.length) {
             break countsLoop;
           }
           qCount = total * quantiles[rIndex];
         }
       }
       // In case quantiles contains values >= 100%
       for (; rIndex < quantiles.length; rIndex++) {
         res[rIndex] = this.max.get();
       }
 
       return res;
     }
 
 
     long getNumAtOrBelow(long val) {
       final int targetIndex = getIndex(val);
       long totalToCurrentIndex = 0;
       for (int i = 0; i <= targetIndex; i++) {
         totalToCurrentIndex += this.counts[i].sum();
       }
       return  totalToCurrentIndex;
     }
 
     public long getMin() {
       long min = this.min.get();
       return min == Long.MAX_VALUE ? 0 : min; // in case it is not initialized
     }
 
     public long getMean() {
       long count = this.count.sum();
       long total = this.total.sum();
       if (count == 0) {
         return 0;
       }
       return total / count;
     }
   }
 
   // The bins counting values. It is replaced with a new one in calling of reset().
   private volatile Bins bins;
 
   /**
    * Constructor.
    */
   public FastLongHistogram() {
     this(DEFAULT_NBINS);
   }
 
   /**
    * Constructor.
    * @param numOfBins the number of bins for the histogram. A larger value results in more precise
    *          results but with lower efficiency, and vice versus.
    */
   public FastLongHistogram(int numOfBins) {
     this.bins = new Bins(numOfBins);
   }
 
   /**
    * Constructor setting the bins assuming a uniform distribution within a range.
    * @param numOfBins the number of bins for the histogram. A larger value results in more precise
    *          results but with lower efficiency, and vice versus.
    * @param min lower bound of the region, inclusive.
    * @param max higher bound of the region, inclusive.
    */
   public FastLongHistogram(int numOfBins, long min, long max) {
     this(numOfBins);
     Bins bins = new Bins(numOfBins);
     bins.add(min, 1);
     bins.add(max, 1);
     this.bins = new Bins(bins, numOfBins, 0.01, 0.999);
   }
 
   private FastLongHistogram(Bins bins) {
     this.bins = bins;
   }
 
   /**
    * Adds a value to the histogram.
    */
   public void add(long value, long count) {
     this.bins.add(value, count);
   }
 
   /**
    * Computes the quantiles give the ratios.
    */
   public long[] getQuantiles(double[] quantiles) {
     return this.bins.getQuantiles(quantiles);
   }
 
   public long[] getQuantiles() {
     return this.bins.getQuantiles(DEFAULT_QUANTILES);
   }
 
   public long getMin() {
     return this.bins.getMin();
   }
 
   public long getMax() {
     return this.bins.max.get();
   }
 
   public long getCount() {
     return this.bins.count.sum();
   }
 
   public long getMean() {
     return this.bins.getMean();
   }
 
   public long getNumAtOrBelow(long value) {
     return this.bins.getNumAtOrBelow(value);
   }
 
   /**
    * Resets the histogram for new counting.
    */
   public Snapshot snapshotAndReset() {
     final Bins oldBins = this.bins;
     this.bins = new Bins(this.bins, this.bins.counts.length - 3, 0.01, 0.99);
     final long[] percentiles = oldBins.getQuantiles(DEFAULT_QUANTILES);
     final long count = oldBins.count.sum();
 
     return new Snapshot() {
       @Override
       public long[] getQuantiles(double[] quantiles) {
         return oldBins.getQuantiles(quantiles);
       }
 
       @Override
       public long[] getQuantiles() {
         return percentiles;
       }
 
       @Override
       public long getCount() {
         return count;
       }
 
       @Override
       public long getCountAtOrBelow(long val) {
         return oldBins.getNumAtOrBelow(val);
       }
 
       @Override
       public long get25thPercentile() {
         return percentiles[0];
       }
 
       @Override
       public long get75thPercentile() {
         return percentiles[2];
       }
 
       @Override
       public long get90thPercentile() {
         return percentiles[3];
       }
 
       @Override
       public long get95thPercentile() {
         return percentiles[4];
       }
 
       @Override
       public long get98thPercentile() {
         return percentiles[5];
       }
 
       @Override
       public long get99thPercentile() {
         return percentiles[6];
       }
 
       @Override
       public long get999thPercentile() {
         return percentiles[7];
       }
 
       @Override
       public long getMedian() {
         return percentiles[1];
       }
 
       @Override
       public long getMax() {
         return oldBins.max.get();
       }
 
       @Override
       public long getMean() {
         return oldBins.getMean();
       }
 
       @Override
       public long getMin() {
         return oldBins.getMin();
       }
     };
   }
 }