ableegoldman commented on a change in pull request #8697:
URL: https://github.com/apache/kafka/pull/8697#discussion_r429461550
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File path:
clients/src/test/java/org/apache/kafka/common/metrics/MetricsTest.java
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@@ -492,6 +493,52 @@ public void testPercentiles() {
assertEquals(75, (Double) p75.metricValue(), 1.0);
}
+ @Test
+ public void testPercentilesWithRandomNumbersAndLinearBucketing() {
+ long seed = new Random().nextLong();
+ int sizeInBytes = 1000 * 1000; // 1MB
+ long maximumValue = 1000 * 24 * 60 * 60 * 1000L; // if values are ms,
max is 1000 days
+
+ try {
+ Random prng = new Random(seed);
+ int numberOfValues = 5000 + prng.nextInt(10_000); // ranges is
[5000, 15000]
+
+ Percentiles percs = new Percentiles(sizeInBytes,
+ maximumValue,
+ BucketSizing.LINEAR,
+ new
Percentile(metrics.metricName("test.p90", "grp1"), 90),
+ new
Percentile(metrics.metricName("test.p99", "grp1"), 99));
+ MetricConfig config = new
MetricConfig().eventWindow(50).samples(2);
+ Sensor sensor = metrics.sensor("test", config);
+ sensor.add(percs);
+ Metric p90 =
this.metrics.metrics().get(metrics.metricName("test.p90", "grp1"));
+ Metric p99 =
this.metrics.metrics().get(metrics.metricName("test.p99", "grp1"));
+
+ final List<Long> values = new ArrayList<>(numberOfValues);
+ // record two windows worth of sequential values
+ for (int i = 0; i < numberOfValues; ++i) {
+ long value = Math.abs(prng.nextLong()) % maximumValue;
+ values.add(value);
+ sensor.record(value);
+ }
+
+ Collections.sort(values);
+
+ int p90Index = (int)Math.ceil(((double)(90 * numberOfValues)) /
100);
+ int p99Index = (int)Math.ceil(((double)(99 * numberOfValues)) /
100);
+
+ double expectedP90 = values.get(p90Index - 1);
+ double expectedP99 = values.get(p99Index - 1);
+
+ assertEquals(expectedP90, (Double) p90.metricValue(), expectedP90
/ 10);
Review comment:
Trying to build confidence in the `Percentiles` implementation and gauge
the accuracy with a more complicated test.
I found it was accurate to within 5% maybe 2/3 or 3/4 of the time, but it
seems reasonable to expect it to be accurate to within 10%
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