[GitHub] [kafka] Hangleton commented on a diff in pull request #13177: [KAFKA-14441] Benchmark performance impact of metrics library

["Hangleton (via GitHub)" <gi...@apache.org>]

Hangleton commented on code in PR #13177:
URL: https://github.com/apache/kafka/pull/13177#discussion_r1094530705


##########
jmh-benchmarks/src/main/java/org/apache/kafka/jmh/common/metrics/SensorBenchmark.java:
##########
@@ -0,0 +1,145 @@
+package org.apache.kafka.jmh.common.metrics;
+
+import org.apache.kafka.common.metrics.Metrics;
+import org.apache.kafka.common.metrics.Sensor;
+import org.apache.kafka.common.metrics.stats.Avg;
+import org.apache.kafka.common.metrics.stats.Max;
+import org.apache.kafka.common.metrics.stats.Min;
+import org.openjdk.jmh.annotations.Benchmark;
+import org.openjdk.jmh.annotations.BenchmarkMode;
+import org.openjdk.jmh.annotations.Fork;
+import org.openjdk.jmh.annotations.Measurement;
+import org.openjdk.jmh.annotations.Mode;
+import org.openjdk.jmh.annotations.OutputTimeUnit;
+import org.openjdk.jmh.annotations.Param;
+import org.openjdk.jmh.annotations.Scope;
+import org.openjdk.jmh.annotations.Setup;
+import org.openjdk.jmh.annotations.State;
+import org.openjdk.jmh.annotations.Warmup;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.concurrent.TimeUnit;
+
+@State(Scope.Benchmark)
+@Fork(value = 1)
+@Warmup(iterations = 5)
+@Measurement(iterations = 5)

Review Comment:
   Is the number of warm-up and iterations really enough? 5 may be a bit low for the code path exercised. Unsure how representative the benchmark is for high-throughput invocations in production environments. 



##########
jmh-benchmarks/src/main/java/org/apache/kafka/jmh/common/metrics/SensorBenchmark.java:
##########
@@ -0,0 +1,145 @@
+package org.apache.kafka.jmh.common.metrics;
+
+import org.apache.kafka.common.metrics.Metrics;
+import org.apache.kafka.common.metrics.Sensor;
+import org.apache.kafka.common.metrics.stats.Avg;
+import org.apache.kafka.common.metrics.stats.Max;
+import org.apache.kafka.common.metrics.stats.Min;
+import org.openjdk.jmh.annotations.Benchmark;
+import org.openjdk.jmh.annotations.BenchmarkMode;
+import org.openjdk.jmh.annotations.Fork;
+import org.openjdk.jmh.annotations.Measurement;
+import org.openjdk.jmh.annotations.Mode;
+import org.openjdk.jmh.annotations.OutputTimeUnit;
+import org.openjdk.jmh.annotations.Param;
+import org.openjdk.jmh.annotations.Scope;
+import org.openjdk.jmh.annotations.Setup;
+import org.openjdk.jmh.annotations.State;
+import org.openjdk.jmh.annotations.Warmup;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.concurrent.TimeUnit;
+
+@State(Scope.Benchmark)
+@Fork(value = 1)
+@Warmup(iterations = 5)
+@Measurement(iterations = 5)
+@BenchmarkMode(Mode.AverageTime)

Review Comment:
   What is the variance of the distribution? `SampleTime` may be useful to review the resulting percentiles. 



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