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The Apache Cassandra Project develops a highly scalable second-generation distributed database, bringing together Dynamo's fully distributed design and Bigtable's ColumnFamily-based data model.
/*
* 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 java.io.IOException;
import java.util.*;
import com.google.common.base.Objects;
import org.apache.cassandra.db.TypeSizes;
import org.apache.cassandra.io.ISerializer;
import org.apache.cassandra.io.sstable.SSTable;
import org.apache.cassandra.io.util.DataInputPlus;
import org.apache.cassandra.io.util.DataOutputPlus;
/**
* 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.
// The key is a numeric type so we can avoid boxing/unboxing streams of different key types
// The value is a unboxed long array always of length == 1
// Serialized Histograms always writes with double keys for backwards compatibility
private final TreeMap 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
* @param source the existing bins in map form
*/
private StreamingHistogram(int maxBinSize, Map source)
{
this.maxBinSize = maxBinSize;
this.bin = new TreeMap<>((o1, o2) -> {
if (o1.getClass().equals(o2.getClass()))
return ((Comparable)o1).compareTo(o2);
else
return Double.compare(o1.doubleValue(), o2.doubleValue());
});
for (Map.Entry entry : source.entrySet())
this.bin.put(entry.getKey(), new long[]{entry.getValue()[0]});
}
/**
* 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 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[0];
}
else
{
Map.Entry pi = bin.floorEntry(b);
if (pi == null)
return 0;
// calculate estimated count mb for point b
double weight = (b - pi.getKey().doubleValue()) / (pnext.getKey().doubleValue() - pi.getKey().doubleValue());
double mb = pi.getValue()[0] + (pnext.getValue()[0] - pi.getValue()[0]) * weight;
sum += (pi.getValue()[0] + mb) * weight / 2;
sum += pi.getValue()[0] / 2.0;
for (long[] value : bin.headMap(pi.getKey(), false).values())
sum += value[0];
}
return sum;
}
public Map getAsMap()
{
return Collections.unmodifiableMap(bin);
}
public static class StreamingHistogramBuilder
{
// TreeMap to hold bins of histogram.
// The key is a numeric type so we can avoid boxing/unboxing streams of different key types
// The value is a unboxed long array always of length == 1
// Serialized Histograms always writes with double keys for backwards compatibility
private final TreeMap bin;
// Keep a second, larger buffer to spool data in, before finalizing it into `bin`
private final TreeMap spool;
// maximum bin size for this histogram
private final int maxBinSize;
// maximum size of the spool
private final int maxSpoolSize;
// voluntarily give up resolution for speed
private final int roundSeconds;
/**
* Creates a new histogram with max bin size of maxBinSize
* @param maxBinSize maximum number of bins this histogram can have
*/
public StreamingHistogramBuilder(int maxBinSize, int maxSpoolSize, int roundSeconds)
{
this.maxBinSize = maxBinSize;
this.maxSpoolSize = maxSpoolSize;
this.roundSeconds = roundSeconds;
bin = new TreeMap<>((o1, o2) -> {
if (o1.getClass().equals(o2.getClass()))
return ((Comparable)o1).compareTo(o2);
else
return Double.compare(o1.doubleValue(), o2.doubleValue());
});
spool = new TreeMap<>((o1, o2) -> {
if (o1.getClass().equals(o2.getClass()))
return ((Comparable)o1).compareTo(o2);
else
return Double.compare(o1.doubleValue(), o2.doubleValue());
});
}
public StreamingHistogram build()
{
flushHistogram();
return new StreamingHistogram(maxBinSize, bin);
}
/**
* Adds new point p to this histogram.
* @param p
*/
public void update(Number p)
{
update(p, 1L);
}
/**
* Adds new point p with value m to this histogram.
* @param p
* @param m
*/
public void update(Number p, long m)
{
Number d = p.longValue() % this.roundSeconds;
if (d.longValue() > 0)
p =p.longValue() + (this.roundSeconds - d.longValue());
long[] mi = spool.get(p);
if (mi != null)
{
// we found the same p so increment that counter
mi[0] += m;
}
else
{
mi = new long[]{m};
spool.put(p, mi);
}
// If spool has overflowed, compact it
if(spool.size() > maxSpoolSize)
flushHistogram();
}
/**
* Drain the temporary spool into the final bins
*/
public void flushHistogram()
{
if (spool.size() > 0)
{
long[] spoolValue;
long[] binValue;
// Iterate over the spool, copying the value into the primary bin map
// and compacting that map as necessary
for (Map.Entry entry : spool.entrySet())
{
Number key = entry.getKey();
spoolValue = entry.getValue();
binValue = bin.get(key);
// If this value is already in the final histogram bins
// Simply increment and update, otherwise, insert a new long[1] value
if(binValue != null)
{
binValue[0] += spoolValue[0];
bin.put(key, binValue);
}
else
{
bin.put(key, new long[]{spoolValue[0]});
}
if (bin.size() > maxBinSize)
{
// find points p1, p2 which have smallest difference
Iterator keys = bin.keySet().iterator();
double p1 = keys.next().doubleValue();
double p2 = keys.next().doubleValue();
double smallestDiff = p2 - p1;
double q1 = p1, q2 = p2;
while (keys.hasNext())
{
p1 = p2;
p2 = keys.next().doubleValue();
double diff = p2 - p1;
if (diff < smallestDiff)
{
smallestDiff = diff;
q1 = p1;
q2 = p2;
}
}
// merge those two
long[] a1 = bin.remove(q1);
long[] a2 = bin.remove(q2);
long k1 = a1[0];
long k2 = a2[0];
a1[0] += k2;
bin.put((q1 * k1 + q2 * k2) / (k1 + k2), a1);
}
}
spool.clear();
}
}
/**
* Merges given histogram with this histogram.
*
* @param other histogram to merge
*/
public void merge(StreamingHistogram other)
{
if (other == null)
return;
for (Map.Entry entry : other.getAsMap().entrySet())
update(entry.getKey(), entry.getValue()[0]);
}
}
public static class StreamingHistogramSerializer implements ISerializer
{
public void serialize(StreamingHistogram histogram, DataOutputPlus out) throws IOException
{
out.writeInt(histogram.maxBinSize);
Map entries = histogram.getAsMap();
out.writeInt(entries.size());
for (Map.Entry entry : entries.entrySet())
{
out.writeDouble(entry.getKey().doubleValue());
out.writeLong(entry.getValue()[0]);
}
}
public StreamingHistogram deserialize(DataInputPlus in) throws IOException
{
int maxBinSize = in.readInt();
int size = in.readInt();
Map tmp = new HashMap<>(size);
for (int i = 0; i < size; i++)
{
tmp.put(in.readDouble(), new long[]{in.readLong()});
}
return new StreamingHistogram(maxBinSize, tmp);
}
public long serializedSize(StreamingHistogram histogram)
{
long size = TypeSizes.sizeof(histogram.maxBinSize);
Map entries = histogram.getAsMap();
size += TypeSizes.sizeof(entries.size());
// size of entries = size * (8(double) + 8(long))
size += entries.size() * (8L + 8L);
return size;
}
}
@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);
}
}