/*
* 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.utils;
import org.apache.cassandra.db.TypeSizes;
import org.apache.cassandra.io.ISerializer;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.*;
import com.google.common.base.Objects;
/**
* Histogram that can be constructed from streaming of data.
*
* The algorithm is taken from following paper:
* Yael Ben-Haim and Elad Tom-Tov, "A Streaming Parallel Decision Tree Algorithm" (2010)
* http://jmlr.csail.mit.edu/papers/volume11/ben-haim10a/ben-haim10a.pdf
*/
public class StreamingHistogram
{
public static final StreamingHistogramSerializer serializer = new StreamingHistogramSerializer();
// TreeMap to hold bins of histogram.
private final TreeMap<Double, Long> bin;
// maximum bin size for this histogram
private final int maxBinSize;
/**
* Creates a new histogram with max bin size of maxBinSize
* @param maxBinSize maximum number of bins this histogram can have
*/
public StreamingHistogram(int maxBinSize)
{
this.maxBinSize = maxBinSize;
bin = new TreeMap<Double, Long>();
}
private StreamingHistogram(int maxBinSize, Map<Double, Long> bin)
{
this.maxBinSize = maxBinSize;
this.bin = new TreeMap<Double, Long>(bin);
}
/**
* Adds new point p to this histogram.
* @param p
*/
public void update(double p)
{
update(p, 1);
}
/**
* Adds new point p with value m to this histogram.
* @param p
* @param m
*/
public void update(double p, long m)
{
Long mi = bin.get(p);
if (mi != null)
{
// we found the same p so increment that counter
bin.put(p, mi + m);
}
else
{
bin.put(p, m);
// if bin size exceeds maximum bin size then trim down to max size
while (bin.size() > maxBinSize)
{
// find points p1, p2 which have smallest difference
Iterator<Double> keys = bin.keySet().iterator();
double p1 = keys.next();
double p2 = keys.next();
double smallestDiff = p2 - p1;
double q1 = p1, q2 = p2;
while (keys.hasNext())
{
p1 = p2;
p2 = keys.next();
double diff = p2 - p1;
if (diff < smallestDiff)
{
smallestDiff = diff;
q1 = p1;
q2 = p2;
}
}
// merge those two
long k1 = bin.remove(q1);
long k2 = bin.remove(q2);
bin.put((q1 * k1 + q2 * k2) / (k1 + k2), k1 + k2);
}
}
}
/**
* Merges given histogram with this histogram.
*
* @param other histogram to merge
*/
public void merge(StreamingHistogram other)
{
if (other == null)
return;
for (Map.Entry<Double, Long> entry : other.getAsMap().entrySet())
update(entry.getKey(), entry.getValue());
}
/**
* Calculates estimated number of points in interval [-inf,b].
*
* @param b upper bound of a interval to calculate sum
* @return estimated number of points in a interval [-inf,b].
*/
public double sum(double b)
{
double sum = 0;
// find the points pi, pnext which satisfy pi <= b < pnext
Map.Entry<Double, Long> pnext = bin.higherEntry(b);
if (pnext == null)
{
// if b is greater than any key in this histogram,
// just count all appearance and return
for (Long value : bin.values())
sum += value;
}
else
{
Map.Entry<Double, Long> pi = bin.floorEntry(b);
if (pi == null)
return 0;
// calculate estimated count mb for point b
double weight = (b - pi.getKey()) / (pnext.getKey() - pi.getKey());
double mb = pi.getValue() + (pnext.getValue() - pi.getValue()) * weight;
sum += (pi.getValue() + mb) * weight / 2;
sum += pi.getValue() / 2.0;
for (Long value : bin.headMap(pi.getKey(), false).values())
sum += value;
}
return sum;
}
public Map<Double, Long> getAsMap()
{
return Collections.unmodifiableMap(bin);
}
public static class StreamingHistogramSerializer implements ISerializer<StreamingHistogram>
{
public void serialize(StreamingHistogram histogram, DataOutput out) throws IOException
{
out.writeInt(histogram.maxBinSize);
Map<Double, Long> entries = histogram.getAsMap();
out.writeInt(entries.size());
for (Map.Entry<Double, Long> entry : entries.entrySet())
{
out.writeDouble(entry.getKey());
out.writeLong(entry.getValue());
}
}
public StreamingHistogram deserialize(DataInput in) throws IOException
{
int maxBinSize = in.readInt();
int size = in.readInt();
Map<Double, Long> tmp = new HashMap<Double, Long>(size);
for (int i = 0; i < size; i++)
{
tmp.put(in.readDouble(), in.readLong());
}
return new StreamingHistogram(maxBinSize, tmp);
}
public long serializedSize(StreamingHistogram histogram, TypeSizes typeSizes)
{
throw new UnsupportedOperationException();
}
}
@Override
public boolean equals(Object o)
{
if (this == o)
return true;
if (!(o instanceof StreamingHistogram))
return false;
StreamingHistogram that = (StreamingHistogram) o;
return maxBinSize == that.maxBinSize && bin.equals(that.bin);
}
@Override
public int hashCode()
{
return Objects.hashCode(bin.hashCode(), maxBinSize);
}
}