/*
 * 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.Arrays;
import java.util.concurrent.atomic.AtomicLongArray;

import com.google.common.base.Objects;

import org.apache.cassandra.db.TypeSizes;
import org.apache.cassandra.io.ISerializer;
import org.apache.cassandra.io.util.DataInputPlus;
import org.apache.cassandra.io.util.DataOutputPlus;
import org.slf4j.Logger;

public class EstimatedHistogram
{
    public static final EstimatedHistogramSerializer serializer = new EstimatedHistogramSerializer();

    
The series of values to which the counts in `buckets` correspond:
1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 17, 20, etc.
Thus, a `buckets` of [0, 0, 1, 10] would mean we had seen one value of 3 and 10 values of 4.
The series starts at 1 and grows by 1.2 each time (rounding and removing duplicates). It goes from 1
to around 36M by default (creating 90+1 buckets), which will give us timing resolution from microseconds to
36 seconds, with less precision as the numbers get larger.
Each bucket represents values from (previous bucket offset, current offset].
/**
     * The series of values to which the counts in `buckets` correspond:
     * 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 17, 20, etc.
     * Thus, a `buckets` of [0, 0, 1, 10] would mean we had seen one value of 3 and 10 values of 4.
     *
     * The series starts at 1 and grows by 1.2 each time (rounding and removing duplicates). It goes from 1
     * to around 36M by default (creating 90+1 buckets), which will give us timing resolution from microseconds to
     * 36 seconds, with less precision as the numbers get larger.
     *
     * Each bucket represents values from (previous bucket offset, current offset].
     */
    private final long[] bucketOffsets;

    // buckets is one element longer than bucketOffsets -- the last element is values greater than the last offset
    final AtomicLongArray buckets;

    public EstimatedHistogram()
    {
        this(90);
    }

    public EstimatedHistogram(int bucketCount)
    {
        this(bucketCount, false);
    }

    public EstimatedHistogram(int bucketCount, boolean considerZeroes)
    {
        bucketOffsets = newOffsets(bucketCount, considerZeroes);
        buckets = new AtomicLongArray(bucketOffsets.length + 1);
    }

    
Create EstimatedHistogram from only bucket data.
Params:
bucketData – bucket data/**
     * Create EstimatedHistogram from only bucket data.
     *
     * @param bucketData bucket data
     */
    public EstimatedHistogram(long[] bucketData)
    {
        assert bucketData != null && bucketData.length > 0 : "Bucket data must be an array of size more than 0";
        bucketOffsets = newOffsets(bucketData.length - 1, false);
        buckets = new AtomicLongArray(bucketData);
    }

    public EstimatedHistogram(long[] offsets, long[] bucketData)
    {
        assert bucketData.length == offsets.length +1;
        bucketOffsets = offsets;
        buckets = new AtomicLongArray(bucketData);
    }

    public static long[] newOffsets(int size, boolean considerZeroes)
    {
        long[] result = new long[size + (considerZeroes ? 1 : 0)];
        int i = 0;
        if (considerZeroes)
            result[i++] = 0;
        long last = 1;
        result[i++] = last;
        for (; i < result.length; i++)
        {
            long next = Math.round(last * 1.2);
            if (next == last)
                next++;
            result[i] = next;
            last = next;
        }

        return result;
    }

    
Returns:
the histogram values corresponding to each bucket index/**
     * @return the histogram values corresponding to each bucket index
     */
    public long[] getBucketOffsets()
    {
        return bucketOffsets;
    }

    
Increments the count of the bucket closest to n, rounding UP.
Params:
n – /**
     * Increments the count of the bucket closest to n, rounding UP.
     * @param n
     */
    public void add(long n)
    {
        int index = Arrays.binarySearch(bucketOffsets, n);
        if (index < 0)
        {
            // inexact match, take the first bucket higher than n
            index = -index - 1;
        }
        // else exact match; we're good
        buckets.incrementAndGet(index);
    }

    
Returns:
the count in the given bucket/**
     * @return the count in the given bucket
     */
    long get(int bucket)
    {
        return buckets.get(bucket);
    }

    
Params:
reset – zero out buckets afterwards if true
Returns:
a long[] containing the current histogram buckets/**
     * @param reset zero out buckets afterwards if true
     * @return a long[] containing the current histogram buckets
     */
    public long[] getBuckets(boolean reset)
    {
        final int len = buckets.length();
        long[] rv = new long[len];

        if (reset)
            for (int i = 0; i < len; i++)
                rv[i] = buckets.getAndSet(i, 0L);
        else
            for (int i = 0; i < len; i++)
                rv[i] = buckets.get(i);

        return rv;
    }

    
Returns:
the smallest value that could have been added to this histogram/**
     * @return the smallest value that could have been added to this histogram
     */
    public long min()
    {
        for (int i = 0; i < buckets.length(); i++)
        {
            if (buckets.get(i) > 0)
                return i == 0 ? 0 : 1 + bucketOffsets[i - 1];
        }
        return 0;
    }

    
Returns:
the largest value that could have been added to this histogram.  If the histogram
overflowed, returns Long.MAX_VALUE./**
     * @return the largest value that could have been added to this histogram.  If the histogram
     * overflowed, returns Long.MAX_VALUE.
     */
    public long max()
    {
        int lastBucket = buckets.length() - 1;
        if (buckets.get(lastBucket) > 0)
            return Long.MAX_VALUE;

        for (int i = lastBucket - 1; i >= 0; i--)
        {
            if (buckets.get(i) > 0)
                return bucketOffsets[i];
        }
        return 0;
    }

    
Params:
percentile – 
Returns:
estimated value at given percentile/**
     * @param percentile
     * @return estimated value at given percentile
     */
    public long percentile(double percentile)
    {
        assert percentile >= 0 && percentile <= 1.0;
        int lastBucket = buckets.length() - 1;
        if (buckets.get(lastBucket) > 0)
            throw new IllegalStateException("Unable to compute when histogram overflowed");

        long pcount = (long) Math.ceil(count() * percentile);
        if (pcount == 0)
            return 0;

        long elements = 0;
        for (int i = 0; i < lastBucket; i++)
        {
            elements += buckets.get(i);
            if (elements >= pcount)
                return bucketOffsets[i];
        }
        return 0;
    }

    
Throws:
IllegalStateException – if any values were greater than the largest bucket threshold
Returns:
the ceil of mean histogram value (average of bucket offsets, weighted by count)/**
     * @return the ceil of mean histogram value (average of bucket offsets, weighted by count)
     * @throws IllegalStateException if any values were greater than the largest bucket threshold
     */
    public long mean()
    {
        return (long) Math.ceil(rawMean());
    }

    
Throws:
IllegalStateException – if any values were greater than the largest bucket threshold
Returns:
the mean histogram value (average of bucket offsets, weighted by count)/**
     * @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 rawMean()
    {
        int lastBucket = buckets.length() - 1;
        if (buckets.get(lastBucket) > 0)
            throw new IllegalStateException("Unable to compute ceiling for max when histogram overflowed");

        long elements = 0;
        long sum = 0;
        for (int i = 0; i < lastBucket; i++)
        {
            long bCount = buckets.get(i);
            elements += bCount;
            sum += bCount * bucketOffsets[i];
        }

        return (double) sum / elements;
    }

    
Returns:
the total number of non-zero values/**
     * @return the total number of non-zero values
     */
    public long count()
    {
       long sum = 0L;
       for (int i = 0; i < buckets.length(); i++)
           sum += buckets.get(i);
       return sum;
    }

    
Returns:
the largest bucket offset/**
     * @return the largest bucket offset
     */
    public long getLargestBucketOffset()
    {
        return bucketOffsets[bucketOffsets.length - 1];
    }

    
Returns:
true if this histogram has overflowed -- that is, a value larger than our largest bucket could bound was added/**
     * @return true if this histogram has overflowed -- that is, a value larger than our largest bucket could bound was added
     */
    public boolean isOverflowed()
    {
        return buckets.get(buckets.length() - 1) > 0;
    }

    
log.debug() every record in the histogram
Params:
log – /**
     * log.debug() every record in the histogram
     *
     * @param log
     */
    public void log(Logger log)
    {
        // only print overflow if there is any
        int nameCount;
        if (buckets.get(buckets.length() - 1) == 0)
            nameCount = buckets.length() - 1;
        else
            nameCount = buckets.length();
        String[] names = new String[nameCount];

        int maxNameLength = 0;
        for (int i = 0; i < nameCount; i++)
        {
            names[i] = nameOfRange(bucketOffsets, i);
            maxNameLength = Math.max(maxNameLength, names[i].length());
        }

        // emit log records
        String formatstr = "%" + maxNameLength + "s: %d";
        for (int i = 0; i < nameCount; i++)
        {
            long count = buckets.get(i);
            // sort-of-hack to not print empty ranges at the start that are only used to demarcate the
            // first populated range. for code clarity we don't omit this record from the maxNameLength
            // calculation, and accept the unnecessary whitespace prefixes that will occasionally occur
            if (i == 0 && count == 0)
                continue;
            log.debug(String.format(formatstr, names[i], count));
        }
    }

    private static String nameOfRange(long[] bucketOffsets, int index)
    {
        StringBuilder sb = new StringBuilder();
        appendRange(sb, bucketOffsets, index);
        return sb.toString();
    }

    private static void appendRange(StringBuilder sb, long[] bucketOffsets, int index)
    {
        sb.append("[");
        if (index == 0)
            if (bucketOffsets[0] > 0)
                // by original definition, this histogram is for values greater than zero only;
                // if values of 0 or less are required, an entry of lb-1 must be inserted at the start
                sb.append("1");
            else
                sb.append("-Inf");
        else
            sb.append(bucketOffsets[index - 1] + 1);
        sb.append("..");
        if (index == bucketOffsets.length)
            sb.append("Inf");
        else
            sb.append(bucketOffsets[index]);
        sb.append("]");
    }

    @Override
    public boolean equals(Object o)
    {
        if (this == o)
            return true;

        if (!(o instanceof EstimatedHistogram))
            return false;

        EstimatedHistogram that = (EstimatedHistogram) o;
        return Arrays.equals(getBucketOffsets(), that.getBucketOffsets()) &&
               Arrays.equals(getBuckets(false), that.getBuckets(false));
    }

    @Override
    public int hashCode()
    {
        return Objects.hashCode(getBucketOffsets(), getBuckets(false));
    }

    public static class EstimatedHistogramSerializer implements ISerializer<EstimatedHistogram>
    {
        public void serialize(EstimatedHistogram eh, DataOutputPlus out) throws IOException
        {
            long[] offsets = eh.getBucketOffsets();
            long[] buckets = eh.getBuckets(false);
            out.writeInt(buckets.length);
            for (int i = 0; i < buckets.length; i++)
            {
                out.writeLong(offsets[i == 0 ? 0 : i - 1]);
                out.writeLong(buckets[i]);
            }
        }

        public EstimatedHistogram deserialize(DataInputPlus in) throws IOException
        {
            int size = in.readInt();
            long[] offsets = new long[size - 1];
            long[] buckets = new long[size];

            for (int i = 0; i < size; i++)
            {
                offsets[i == 0 ? 0 : i - 1] = in.readLong();
                buckets[i] = in.readLong();
            }
            return new EstimatedHistogram(offsets, buckets);
        }

        public long serializedSize(EstimatedHistogram eh)
        {
            int size = 0;

            long[] offsets = eh.getBucketOffsets();
            long[] buckets = eh.getBuckets(false);
            size += TypeSizes.sizeof(buckets.length);
            for (int i = 0; i < buckets.length; i++)
            {
                size += TypeSizes.sizeof(offsets[i == 0 ? 0 : i - 1]);
                size += TypeSizes.sizeof(buckets[i]);
            }
            return size;
        }
    }
}