blob: ca5c6a4766857978bbabcab73aabe84cf8cc4d40 [file] [log] [blame]
/*
* 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.impl;
import static org.junit.Assert.assertEquals;
import java.util.Arrays;
import java.util.Random;
import java.util.concurrent.ThreadLocalRandom;
import org.apache.hadoop.hbase.HBaseClassTestRule;
import org.apache.hadoop.hbase.testclassification.MiscTests;
import org.apache.hadoop.hbase.testclassification.SmallTests;
import org.junit.Assert;
import org.junit.ClassRule;
import org.junit.Test;
import org.junit.experimental.categories.Category;
/**
* Testcases for FastLongHistogram.
*/
@Category({ MiscTests.class, SmallTests.class })
public class TestFastLongHistogram {
@ClassRule
public static final HBaseClassTestRule CLASS_RULE =
HBaseClassTestRule.forClass(TestFastLongHistogram.class);
private static void doTestUniform(FastLongHistogram hist) {
long[] VALUES = { 0, 10, 20, 30, 40, 50 };
double[] qs = new double[VALUES.length];
for (int i = 0; i < qs.length; i++) {
qs[i] = (double) VALUES[i] / VALUES[VALUES.length - 1];
}
for (int i = 0; i < 10; i++) {
for (long v : VALUES) {
hist.add(v, 1);
}
long[] vals = hist.getQuantiles(qs);
System.out.println(Arrays.toString(vals));
for (int j = 0; j < qs.length; j++) {
Assert.assertTrue(j + "-th element org: " + VALUES[j] + ", act: " + vals[j],
Math.abs(vals[j] - VALUES[j]) <= 10);
}
hist.snapshotAndReset();
}
}
@Test
public void testUniform() {
FastLongHistogram hist = new FastLongHistogram(100, 0, 50);
doTestUniform(hist);
}
@Test
public void testAdaptionOfChange() {
// assumes the uniform distribution
FastLongHistogram hist = new FastLongHistogram(100, 0, 100);
Random rand = ThreadLocalRandom.current();
for (int n = 0; n < 10; n++) {
for (int i = 0; i < 900; i++) {
hist.add(rand.nextInt(100), 1);
}
// add 10% outliers, this breaks the assumption, hope bin10xMax works
for (int i = 0; i < 100; i++) {
hist.add(1000 + rand.nextInt(100), 1);
}
long[] vals = hist.getQuantiles(new double[] { 0.25, 0.75, 0.95 });
System.out.println(Arrays.toString(vals));
if (n == 0) {
Assert.assertTrue("Out of possible value", vals[0] >= 0 && vals[0] <= 50);
Assert.assertTrue("Out of possible value", vals[1] >= 50 && vals[1] <= 100);
Assert.assertTrue("Out of possible value", vals[2] >= 900 && vals[2] <= 1100);
}
hist.snapshotAndReset();
}
}
@Test
public void testGetNumAtOrBelow() {
long[] VALUES = { 1, 10, 20, 30, 40, 50 };
FastLongHistogram h = new FastLongHistogram();
for (long v : VALUES) {
for (int i = 0; i < 100; i++) {
h.add(v, 1);
}
}
h.add(Integer.MAX_VALUE, 1);
h.snapshotAndReset();
for (long v : VALUES) {
for (int i = 0; i < 100; i++) {
h.add(v, 1);
}
}
// Add something way out there to make sure it doesn't throw off the counts.
h.add(Integer.MAX_VALUE, 1);
assertEquals(100, h.getNumAtOrBelow(1));
assertEquals(200, h.getNumAtOrBelow(11));
assertEquals(601, h.getNumAtOrBelow(Long.MAX_VALUE));
}
@Test
public void testSameValues() {
FastLongHistogram hist = new FastLongHistogram(100);
hist.add(50, 100);
hist.snapshotAndReset();
doTestUniform(hist);
}
}