/**
* 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.hadoop.hbase.metrics;
import java.util.Arrays;
import java.util.Random;
import org.apache.hadoop.hbase.metrics.histogram.MetricsHistogram;
import com.yammer.metrics.stats.Snapshot;
import org.apache.hadoop.hbase.SmallTests;
import org.junit.Assert;
import org.junit.Test;
import org.junit.experimental.categories.Category;
@Deprecated
@Category(SmallTests.class)
public class TestMetricsHistogram {
@Test
public void testBasicUniform() {
MetricsHistogram h = new MetricsHistogram("testHistogram", null);
for (int i = 0; i < 100; i++) {
h.update(i);
}
Assert.assertEquals(100, h.getCount());
Assert.assertEquals(0, h.getMin());
Assert.assertEquals(99, h.getMax());
}
private static int safeIndex(int i, int len) {
if (i < len && i>= 0) {
return i;
} else if (i >= len) {
return len - 1;
} else {
return 0;
}
}
@Test
public void testRandom() {
final Random r = new Random();
final MetricsHistogram h = new MetricsHistogram("testHistogram", null);
final long[] data = new long[1000];
for (int i = 0; i < data.length; i++) {
data[i] = (long) (r.nextGaussian() * 10000.0);
h.update(data[i]);
}
final Snapshot s = h.getSnapshot();
Arrays.sort(data);
// as long as the histogram chooses an item with index N+/-slop, accept it
final int slop = 20;
// make sure the median, 75th percentile and 95th percentile are good
final int medianIndex = data.length / 2;
final long minAcceptableMedian = data[safeIndex(medianIndex - slop,
data.length)];
final long maxAcceptableMedian = data[safeIndex(medianIndex + slop,
data.length)];
Assert.assertTrue(s.getMedian() >= minAcceptableMedian
&& s.getMedian() <= maxAcceptableMedian);
final int seventyFifthIndex = (int) (data.length * 0.75);
final long minAcceptableseventyFifth = data[safeIndex(seventyFifthIndex
- slop, data.length)];
final long maxAcceptableseventyFifth = data[safeIndex(seventyFifthIndex
+ slop, data.length)];
Assert.assertTrue(s.get75thPercentile() >= minAcceptableseventyFifth
&& s.get75thPercentile() <= maxAcceptableseventyFifth);
final int ninetyFifthIndex = (int) (data.length * 0.95);
final long minAcceptableninetyFifth = data[safeIndex(ninetyFifthIndex
- slop, data.length)];
final long maxAcceptableninetyFifth = data[safeIndex(ninetyFifthIndex
+ slop, data.length)];
Assert.assertTrue(s.get95thPercentile() >= minAcceptableninetyFifth
&& s.get95thPercentile() <= maxAcceptableninetyFifth);
}
}