kafka Histogram 源码

  • 2022-10-20
  • 浏览 (307)

kafka Histogram 代码

文件路径:/trogdor/src/main/java/org/apache/kafka/trogdor/workload/Histogram.java

/*
 * 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.kafka.trogdor.workload;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.ArrayList;
import java.util.Collections;
import java.util.List;

/**
 * A histogram that can easily find the average, median etc of a large number of samples in a
 * restricted domain.
 */
public class Histogram {
    private final int[] counts;

    private final Logger log = LoggerFactory.getLogger(Histogram.class);

    public Histogram(int maxValue) {
        this.counts = new int[maxValue + 1];
    }

    /**
     * Add a new value to the histogram.
     *
     * Note that the value will be clipped to the maximum value available in the Histogram instance.
     * So if the histogram has 100 buckets, inserting 101 will increment the last bucket.
     */
    public void add(int value) {
        if (value < 0) {
            throw new RuntimeException("invalid negative value.");
        }
        if (value >= counts.length) {
            value = counts.length - 1;
        }
        synchronized (this) {
            int curCount = counts[value];
            if (curCount < Integer.MAX_VALUE) {
                counts[value] = counts[value] + 1;
            }
        }
    }

    /**
     * Add a new value to the histogram.
     *
     * Note that the value will be clipped to the maximum value available in the Histogram instance.
     * This method is provided for convenience, but handles the same numeric range as the method which
     * takes an int.
     */
    public void add(long value) {
        if (value > Integer.MAX_VALUE) {
            add(Integer.MAX_VALUE);
        } else if (value < Integer.MIN_VALUE) {
            add(Integer.MIN_VALUE);
        } else {
            add((int) value);
        }
    }

    public static class Summary {
        /**
         * The total number of samples.
         */
        private final long numSamples;

        /**
         * The average of all samples.
         */
        private final float average;

        /**
         * Percentile information.
         *
         * percentile(fraction=0.99) will have a value which is greater than or equal to 99%
         * of the samples.  percentile(fraction=0.5) is the median sample.  And so forth.
         */
        private final List<PercentileSummary> percentiles;

        Summary(long numSamples, float average, List<PercentileSummary> percentiles) {
            this.numSamples = numSamples;
            this.average = average;
            this.percentiles = percentiles;
        }

        public long numSamples() {
            return numSamples;
        }

        public float average() {
            return average;
        }

        public List<PercentileSummary> percentiles() {
            return percentiles;
        }
    }

    /**
     * Information about a percentile.
     */
    public static class PercentileSummary {
        /**
         * The fraction of samples which are less than or equal to the value of this percentile.
         */
        private final float fraction;

        /**
         * The value of this percentile.
         */
        private final int value;

        PercentileSummary(float fraction, int value) {
            this.fraction = fraction;
            this.value = value;
        }

        public float fraction() {
            return fraction;
        }

        public int value() {
            return value;
        }
    }

    public Summary summarize() {
        return summarize(new float[0]);
    }

    public Summary summarize(float[] percentiles) {
        int[] countsCopy = new int[counts.length];
        synchronized (this) {
            System.arraycopy(counts, 0, countsCopy, 0, counts.length);
        }
        // Verify that the percentiles array is sorted and positive.
        float prev = 0f;
        for (int i = 0; i < percentiles.length; i++) {
            if (percentiles[i] < prev) {
                throw new RuntimeException("Invalid percentiles fraction array.  Bad element " +
                    percentiles[i] + ".  The array must be sorted and non-negative.");
            }
            if (percentiles[i] > 1.0f) {
                throw new RuntimeException("Invalid percentiles fraction array.  Bad element " +
                    percentiles[i] + ".  Elements must be less than or equal to 1.");
            }
        }
        // Find out how many total samples we have, and what the average is.
        long numSamples = 0;
        float total = 0f;
        for (int i = 0; i < countsCopy.length; i++) {
            long count = countsCopy[i];
            numSamples = numSamples + count;
            total = total + (i * count);
        }
        float average = (numSamples == 0) ? 0.0f : (total / numSamples);

        List<PercentileSummary> percentileSummaries =
            summarizePercentiles(countsCopy, percentiles, numSamples);
        return new Summary(numSamples, average, percentileSummaries);
    }

    private List<PercentileSummary> summarizePercentiles(int[] countsCopy, float[] percentiles,
                                                         long numSamples) {
        if (percentiles.length == 0) {
            return Collections.emptyList();
        }
        List<PercentileSummary> summaries = new ArrayList<>(percentiles.length);
        int i = 0, j = 0;
        long seen = 0, next = (long) (numSamples * percentiles[0]);
        while (true) {
            if (i == countsCopy.length - 1) {
                for (; j < percentiles.length; j++) {
                    summaries.add(new PercentileSummary(percentiles[j], i));
                }
                return summaries;
            }
            seen += countsCopy[i];
            while (seen >= next) {
                summaries.add(new PercentileSummary(percentiles[j], i));
                j++;
                if (j == percentiles.length) {
                    return summaries;
                }
                next = (long) (numSamples * percentiles[j]);
            }
            i++;
        }
    }
}

相关信息

kafka 源码目录

相关文章

kafka ConfigurableProducerSpec 源码

kafka ConfigurableProducerWorker 源码

kafka ConnectionStressSpec 源码

kafka ConnectionStressWorker 源码

kafka ConstantFlushGenerator 源码

kafka ConstantPayloadGenerator 源码

kafka ConstantThroughputGenerator 源码

kafka ConsumeBenchSpec 源码

kafka ConsumeBenchWorker 源码

kafka ExternalCommandSpec 源码

0  赞