#4059

Committed 13 Feb 2025 08:38AM CUT coverage: 35.283% (+0.002%) from 35.281%

Build # #4059

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push

github

Committed by

topframe

Commit Message

Update

Run Details

0 of 17 new or added lines in 2 files covered. (0.0%)

2 existing lines in 2 files now uncovered.

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/utils/src/main/java/com/aspectran/utils/statistic/SampleStatistic.java

/*
 * Copyright (c) 2008-2025 The Aspectran Project
 *
 * Licensed 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 com.aspectran.utils.statistic;

import com.aspectran.utils.ObjectUtils;
import com.aspectran.utils.ToStringBuilder;

import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.LongAccumulator;
import java.util.concurrent.atomic.LongAdder;

/**
 * <p>This class is a clone of org.eclipse.jetty.util.statistic.SampleStatistic</p>
 *
 * <p>Statistics on a sampled value.</p>
 * <p>Provides max, total, mean, count, variance, and standard deviation of continuous sequence of samples.</p>
 * <p>Calculates estimates of mean, variance, and standard deviation characteristics of a sample using a non synchronized
 * approximation of the on-line algorithm presented in <cite>Donald Knuth's Art of Computer Programming, Volume 2,
 * Semi numerical Algorithms, 3rd edition, page 232, Boston: Addison-Wesley</cite>. That cites a 1962 paper by B.P. Welford:
 * <a href="http://www.jstor.org/pss/1266577">Note on a Method for Calculating Corrected Sums of Squares and Products</a></p>
 * <p>This algorithm is also described in Wikipedia in the section "Online algorithm":
 * <a href="https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance">Algorithms for calculating variance</a>.</p>
 */
public class SampleStatistic {

    private final LongAccumulator max = new LongAccumulator(Math::max,0L);

    private final AtomicLong total = new AtomicLong();

    private final AtomicLong count = new AtomicLong();

    private final LongAdder totalVariance100 = new LongAdder();

    /**
     * Resets the statistics.
     */
    public void reset() {
        max.reset();
        total.set(0);
        count.set(0);
        totalVariance100.reset();
    }

    /**
     * Records a sample value.
     * @param sample the value to record.
     */
    public void record(long sample) {
        long total = this.total.addAndGet(sample);
        long count = this.count.incrementAndGet();
        if (count > 1) {
            long mean10 = total * 10 / count;
            long delta10 = sample * 10 - mean10;
            totalVariance100.add(delta10 * delta10);
        }
        max.accumulate(sample);
    }

    /**
     * @return the max value of the recorded samples
     */
    public long getMax() {
        return max.get();
    }

    /**
     * @return the sum of all the recorded samples
     */
    public long getTotal() {
        return total.get();
    }

    /**
     * @return the number of samples recorded
     */
    public long getCount() {
        return count.get();
    }

    /**
     * @return the average value of the samples recorded, or zero if there are no samples
     */
    public double getMean() {
        long count = getCount();
        return (count > 0 ? (double)this.total.get() / this.count.get() : 0.0D);
    }

    /**
     * @return the variance of the samples recorded, or zero if there are less than 2 samples
     */
    public double getVariance() {
        long variance100 = totalVariance100.sum();
        long count = getCount();
        return (count > 1 ? variance100 / 100.0D / (count - 1) : 0.0D);
    }

    /**
     * @return the standard deviation of the samples recorded
     */
    public double getStdDev() {
        return Math.sqrt(getVariance());
    }

    @Override
    public String toString() {
        ToStringBuilder tsb = new ToStringBuilder(ObjectUtils.simpleIdentityToString(this));
        tsb.append("max", getMax());
        tsb.append("total", getTotal());
        tsb.append("count", getCount());
        tsb.append("mean", getMean());
        tsb.append("stddev", getStdDev());
        return tsb.toString();
    }

}

1	/*
2	* Copyright (c) 2008-2025 The Aspectran Project
3	*
4	* Licensed under the Apache License, Version 2.0 (the "License");
5	* you may not use this file except in compliance with the License.
6	* You may obtain a copy of the License at
7	*
8	* http://www.apache.org/licenses/LICENSE-2.0
9	*
10	* Unless required by applicable law or agreed to in writing, software
11	* distributed under the License is distributed on an "AS IS" BASIS,
12	* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13	* See the License for the specific language governing permissions and
14	* limitations under the License.
15	*/
16	package com.aspectran.utils.statistic;
17
18	import com.aspectran.utils.ObjectUtils;
19	import com.aspectran.utils.ToStringBuilder;
20
21	import java.util.concurrent.atomic.AtomicLong;
22	import java.util.concurrent.atomic.LongAccumulator;
23	import java.util.concurrent.atomic.LongAdder;
24
25	/**
26	* <p>This class is a clone of org.eclipse.jetty.util.statistic.SampleStatistic</p>
27	*
28	* <p>Statistics on a sampled value.</p>
29	* <p>Provides max, total, mean, count, variance, and standard deviation of continuous sequence of samples.</p>
30	* <p>Calculates estimates of mean, variance, and standard deviation characteristics of a sample using a non synchronized
31	* approximation of the on-line algorithm presented in <cite>Donald Knuth's Art of Computer Programming, Volume 2,
32	* Semi numerical Algorithms, 3rd edition, page 232, Boston: Addison-Wesley</cite>. That cites a 1962 paper by B.P. Welford:
33	* <a href="http://www.jstor.org/pss/1266577">Note on a Method for Calculating Corrected Sums of Squares and Products</a></p>
34	* <p>This algorithm is also described in Wikipedia in the section "Online algorithm":
35	* <a href="https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance">Algorithms for calculating variance</a>.</p>
36	*/
37	public class SampleStatistic {	×
38
39	private final LongAccumulator max = new LongAccumulator(Math::max,0L);	×
40
41	private final AtomicLong total = new AtomicLong();	×
42
43	private final AtomicLong count = new AtomicLong();	×
44
45	private final LongAdder totalVariance100 = new LongAdder();	×
46
47	/**
48	* Resets the statistics.
49	*/
50	public void reset() {
51	max.reset();	×
52	total.set(0);	×
53	count.set(0);	×
54	totalVariance100.reset();	×
55	}	×
56
57	/**
58	* Records a sample value.
59	* @param sample the value to record.
60	*/
61	public void record(long sample) {
UNCOV 62	long total = this.total.addAndGet(sample);	×
63	long count = this.count.incrementAndGet();	×
64	if (count > 1) {	×
65	long mean10 = total * 10 / count;	×
66	long delta10 = sample * 10 - mean10;	×
67	totalVariance100.add(delta10 * delta10);	×
68	}
69	max.accumulate(sample);	×
70	}	×
71
72	/**
73	* @return the max value of the recorded samples
74	*/
75	public long getMax() {
76	return max.get();	×
77	}
78
79	/**
80	* @return the sum of all the recorded samples
81	*/
82	public long getTotal() {
83	return total.get();	×
84	}
85
86	/**
87	* @return the number of samples recorded
88	*/
89	public long getCount() {
90	return count.get();	×
91	}
92
93	/**
94	* @return the average value of the samples recorded, or zero if there are no samples
95	*/
96	public double getMean() {
97	long count = getCount();	×
98	return (count > 0 ? (double)this.total.get() / this.count.get() : 0.0D);	×
99	}
100
101	/**
102	* @return the variance of the samples recorded, or zero if there are less than 2 samples
103	*/
104	public double getVariance() {
105	long variance100 = totalVariance100.sum();	×
106	long count = getCount();	×
107	return (count > 1 ? variance100 / 100.0D / (count - 1) : 0.0D);	×
108	}
109
110	/**
111	* @return the standard deviation of the samples recorded
112	*/
113	public double getStdDev() {
114	return Math.sqrt(getVariance());	×
115	}
116
117	@Override
118	public String toString() {
119	ToStringBuilder tsb = new ToStringBuilder(ObjectUtils.simpleIdentityToString(this));	×
120	tsb.append("max", getMax());	×
121	tsb.append("total", getTotal());	×
122	tsb.append("count", getCount());	×
123	tsb.append("mean", getMean());	×
124	tsb.append("stddev", getStdDev());	×
125	return tsb.toString();	×
126	}
127
128	}

aspectran / aspectran / #4059

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