JMH Performance Testing InfinityDB

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JMH Performance Testing InfinityDB

The Java Microbenchmarking Harness is a widely used, precise test for performance-critical code from OpenJDK. It is simple and fair. Let's write a test!

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The Java Microbenchmarking Harness is a widely used, precise test for performance-critical code from OpenJDK. It is simple and fair. Let's write a test!

We will test InfinityDB, which is a performance-oriented extended persistent ConcurrentNavigableMap used for example in database caching, time-series data capture, and text indexes. It is the DBMS of the Atlassian Fisheye repository browser.

In this article we will show:

  • How to write simple but fair performance benchmarks for critical code

  • Code for the tests for InfinityDB

  • Results of the InfinityDB test

Simple Code and Convenience

JMH simplifies test code greatly by observing Annotations on the test class, state variables, and test methods. It provides parameterization, per-trial, per-benchmark, and per-thread state setup, forking, overhead minimization, warmup iterations, thread control, and data gathering.  It is a simple Maven-driven tool, requiring only a few commands. To start, add the code to be tested in a jar into your local maven repository ~/.m2/repository:

mvn install:install-file -DgroupId=com.infinitydb -DartifactId=infinitydb
-Dversion=4.1.0 -Dpackaging=jar -Dfile=../../infinitydb.jar

Then compile and prepare:

 mvn clean install 

and finally, run the test, for example using one fork, two measurement iterations per trial, 5 warmup iterations per trial, and 8 threads:

 java  -Xmx4g -jar target/benchmarks.jar -f 1 -i 2 -wi 5 -t 8 

At the end of this command, you can include a regex that selects a subset of the test or ‘benchmark’ methods to run by method name. Creative method naming can make this very convenient.

Note that installing the framework from scratch requires an additional mvn command shown at OpenJDK, but the InfinityDB download has done this for you.

Annotation-Based Configuration

At the class level, you have control over all the default global parameters of the run, and these can be overridden at runtime:

Using a @State(Scope.Benchmark) allows each report line or "trial" to be parameterized by a different Map size, in which the parameter variable is static. 

@Warmup(iterations = 2, time = 1, timeUnit = TimeUnit.SECONDS)
@Measurement(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS)
public class InfinityDBJMHTest {

A "benchmark" corresponds to a test method, which examines a particular aspect of performance. The test methods are all Annotated with @Benchmark.

Next, we declare the static method that sets up the static variables that are configured on each trial using  @Setup(Level.Trial). The trials can be repeated with parameterization using @Param on a static variable to run multiple trials per benchmark. We use this for spanning a range of scales logarithmically, also including 0.

The Simple "ItemSpace" Data Model

Because InfinityDB has a faster, more flexible lower-level "ItemSpace" API as well, we specifically test that in testPutAndRemoveItemSpace() , where it is able to avoid a retrieval of the previous value. The "ItemSpace" model is an extremely simple one: a database is solely an ordered set of ‘Items," each Item being a character array from 0 to 1665 characters long. Unlike Strings, an Item is never constructed but contains a sequence of binary self-delimiting compressed strongly-typed primitive "components" in a standard format. Thus an "Item" corresponds to an encoded tuple of variable arity. Logically, an ItemSpace is a variable-arity ordered Tuple space. The ConcurrentNavigableMap wrapper exposes the Tuple space as nestable multi-Maps and Sets with composite keys and values. InfinityDB implements the ItemSpace using a B-Tree.

The Code

The code here tests InfinityDB as a ConcurrentNavigableMap with a benchmark method for each of  get(), put(), remove(), iterators, forEach(), and streams. The code and infinitydb.jar is in the InfinityDB trial download at https://boilerbay.com.

By declaring a static inner class LocalRandom with the @State(Scope.Thread) we can avoid sharing a Random between threads, which is very slow in critical code, creating a bottleneck.  JMH will instantiate this for us, and provide it as a parameter to the test benchmark methods. 

We return a long value in the benchmarks so that there is a ‘tangible’ result of the computation and the methods will not be optimized away - this may not be needed actually as there may be some way for JMH to handle this problem. JMH rewrites the benchmarks in a complex mysterious way and compiles that to minimize overhead and to do the instrumentation.

Below the code are the results.

//Copyright (C) 2018 Roger L. Deran, All rights reserved
import java.io.File;
import java.io.FileOutputStream;
import java.io.IOError;
import java.io.IOException;
import java.io.PrintStream;
import java.nio.file.Files;
import java.util.Date;
import java.util.Map;
import java.util.Map.Entry;
import java.util.Random;
import java.util.concurrent.ConcurrentMap;
import java.util.concurrent.TimeUnit;
import java.util.function.BiConsumer;
import java.util.stream.LongStream;
import java.util.stream.Stream

import org.openjdk.jmh.annotations.Benchmark;
import org.openjdk.jmh.annotations.BenchmarkMode;
import org.openjdk.jmh.annotations.Fork;
import org.openjdk.jmh.annotations.Level;
import org.openjdk.jmh.annotations.Measurement;
import org.openjdk.jmh.annotations.Mode;
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 com.infinitydb.map.db.InfinityDBMap;

package com.infinitydb;

 * Test the performance of InfinityDB using JMH.
public class InfinityDBJMHTest {

    // Maximum size in bytes. It increases as needed, then spills to disk.
    // For this in-cache testing, we make it big.
    static final long CACHE_SIZE = 100_000_000;

    // Different database sizes
    @Param({ "0", "1", "10", "100", "1000", "10000", "100000", "1000000" })
    static long dbSize;

    // The database itself. This presents an ItemSpace model
    static InfinityDB db;

    // Optional wrapper for the db that presents a ConcurrentNavigableMap model.
    static ConcurrentMap<Long, Long> map;

    static boolean isParallelStreams = System.getProperty("parallel") != null;

    static public void setup() throws IOException {
        File infinityDBFile =
                Files.createTempFile("InfinityDBJMHTest_", "").toFile();
        db = InfinityDB.create(infinityDBFile.toString(), true, CACHE_SIZE);
        // Wrap the ItemSpace model to access as a standard
        // ConcurrentNagivableMap.
        map = new InfinityDBMap(db);
        Random random = new Random(System.nanoTime());
        // Load up the Map
        for (long i = 0; i < dbSize; i++) {
            long v = random.nextLong();
            map.put(v, v);
        // Show whether we had -Dparallel=true
        if (isParallelStreams)
            System.out.println("using parallel streams");

    // Randomize between invocations.
    // A Thread-local Random is fast.
    public static class LocalRandom {
        Random random = new Random(System.nanoTime());

     * Modify the database, adding a key/value association and removing it. We
     * have to do the removes too, to keep the Map the same size.
    public static long testPutAndRemove(LocalRandom localRandom) {
        long k = localRandom.random.nextLong();
        long v = localRandom.random.nextLong();
        map.put(k, v);
        return k;

     * Modify the database adding a key/value association and removing it.
     * This uses the low-level 'ItemSpace' API that underlies the Map-Based API
     * for speed. We have to do the deletes too, to keep the Map the same size.
     * The Map-based API is slower because it has to retrieve the old value on
     * each iteration to return.
    public static long testPutAndRemoveItemSpace(LocalRandom localRandom)
            throws IOException {
        long k = localRandom.random.nextLong();
        long v = localRandom.random.nextLong();
        // Allocate a temporary cursor from an internal pool.
        try (Cu cu = Cu.alloc()) {
             * Set the Cu cursor to contain the key and value. A Cu is a
             * sequence of 0 to 1665 chars, like a StringBuffer, but binary.
             * Each appended 'component' is a binary self-delimiting char
             * sequence. Nothing is constructed or GC'ed.

            // This is the equivalent of map.put(k, v). The Map wraps the db.

            // Have the Cu contain just the key component

            // Remove all Items starting with k
            // This is the equivalent of map.remove(k);
        return k;

    public static long testGet(LocalRandom localRandom) {
        long k = localRandom.random.nextLong();
        // v is almost always null.
        Long v = map.get(k);
        if (v != null)
            System.out.println("v != null");
        return v == null ? 0 : v.longValue();

    public static long testIterateKeySet() {
        long sum = 0;
        for (Long k : map.keySet()) {
            sum += k.longValue();
        return sum;

    public static long testIterateEntrySet() {
        long sum = 0;
        for (Entry<Long, Long> e : map.entrySet()) {
            sum += e.getKey().longValue();
        return sum;

    public static long testIterateValues() {
        long sum = 0;
        for (Long v : map.values()) {
            sum += v.longValue();
        return sum;

     * Multiply the ops/sec by Map size to get iterations/sec.
    public static long testForEach() {
        // the normal way
        class SummingBiConsumer implements BiConsumer<Long, Long> {
            long sum = 0;
            public void accept(Long k, Long v) {
                sum += v.longValue();
        SummingBiConsumer summingBiConsumer = new SummingBiConsumer();
        return summingBiConsumer.sum;

    public static long testStreams() {
        if (true) {
            Stream<Long> stream = map.keySet().stream();
            // Stream<Long> stream = map.values().stream();

            if (isParallelStreams)
                stream = stream.parallel();
            long sum = stream.reduce(0L, (x, y) -> x + y).longValue();
            return sum;
        } else {
            // Use a LongStream for the reduce.
            // This is apparently the best case for long streams.
            LongStream stream = map.values().stream()
                    .mapToLong(v -> ((Long) v).longValue());

            if (isParallelStreams)
                stream = stream.parallel();

            // The code for sum() is just a reduce, giving the same
            // performance.
            long sum = stream.sum();
            // sum = stream.reduce(0L, (x, y) -> x + y);
            return sum;

Here is the final summary of the run results on a 3GHz X86 quad-core (hence there are 8 virtual cores due to hyperthreading). An individual trial output is shown below it. All of the tests except the put()/remove()are written to scan the entire database on each "operation" so the scores decrease in proportion to the database size. Just multiply dbSize by Score to get the true per-operation speed.  Note that testPutAndRemove() does both a put and remove for each iteration. ForEach()  is faster than iteration, as is true of most Maps.

As can be seen, Infinity DB generally reaches millions of operations per second. The effect of contention on individual blocks can be seen in the testPutAndRemove, which shows a performance jump between 1K and 10K Entries, when the database becomes multiple blocks in the cache.

# Run complete. Total time: 00:13:43

Benchmark                                    (dbSize)   Mode  Cnt        Score   Error  Units
InfinityDBJMHTest.testForEach                       0  thrpt    2   368667.732          ops/s
InfinityDBJMHTest.testForEach                       1  thrpt    2   634144.250          ops/s
InfinityDBJMHTest.testForEach                      10  thrpt    2   214008.986          ops/s
InfinityDBJMHTest.testForEach                     100  thrpt    2    17423.518          ops/s
InfinityDBJMHTest.testForEach                    1000  thrpt    2     2020.207          ops/s
InfinityDBJMHTest.testForEach                   10000  thrpt    2      677.259          ops/s
InfinityDBJMHTest.testForEach                  100000  thrpt    2       45.526          ops/s
InfinityDBJMHTest.testForEach                 1000000  thrpt    2        3.717          ops/s
InfinityDBJMHTest.testGet                           0  thrpt    2  1956460.272          ops/s
InfinityDBJMHTest.testGet                           1  thrpt    2  1809225.246          ops/s
InfinityDBJMHTest.testGet                          10  thrpt    2  1712550.418          ops/s
InfinityDBJMHTest.testGet                         100  thrpt    2  1102420.633          ops/s
InfinityDBJMHTest.testGet                        1000  thrpt    2  1207971.899          ops/s
InfinityDBJMHTest.testGet                       10000  thrpt    2  3266049.588          ops/s
InfinityDBJMHTest.testGet                      100000  thrpt    2  2648304.018          ops/s
InfinityDBJMHTest.testGet                     1000000  thrpt    2  2726656.997          ops/s
InfinityDBJMHTest.testIterateEntrySet               0  thrpt    2   367123.806          ops/s
InfinityDBJMHTest.testIterateEntrySet               1  thrpt    2   484646.862          ops/s
InfinityDBJMHTest.testIterateEntrySet              10  thrpt    2   121755.481          ops/s
InfinityDBJMHTest.testIterateEntrySet             100  thrpt    2    11345.324          ops/s
InfinityDBJMHTest.testIterateEntrySet            1000  thrpt    2     1374.924          ops/s
InfinityDBJMHTest.testIterateEntrySet           10000  thrpt    2      414.319          ops/s
InfinityDBJMHTest.testIterateEntrySet          100000  thrpt    2       32.797          ops/s
InfinityDBJMHTest.testIterateEntrySet         1000000  thrpt    2        2.810          ops/s
InfinityDBJMHTest.testIterateKeySet                 0  thrpt    2   371764.136          ops/s
InfinityDBJMHTest.testIterateKeySet                 1  thrpt    2   457224.769          ops/s
InfinityDBJMHTest.testIterateKeySet                10  thrpt    2   120137.571          ops/s
InfinityDBJMHTest.testIterateKeySet               100  thrpt    2    10874.475          ops/s
InfinityDBJMHTest.testIterateKeySet              1000  thrpt    2     1383.806          ops/s
InfinityDBJMHTest.testIterateKeySet             10000  thrpt    2      441.785          ops/s
InfinityDBJMHTest.testIterateKeySet            100000  thrpt    2       35.322          ops/s
InfinityDBJMHTest.testIterateKeySet           1000000  thrpt    2        2.937          ops/s
InfinityDBJMHTest.testIterateValues                 0  thrpt    2   374969.317          ops/s
InfinityDBJMHTest.testIterateValues                 1  thrpt    2   479385.263          ops/s
InfinityDBJMHTest.testIterateValues                10  thrpt    2   119844.074          ops/s
InfinityDBJMHTest.testIterateValues               100  thrpt    2    10432.263          ops/s
InfinityDBJMHTest.testIterateValues              1000  thrpt    2     1452.275          ops/s
InfinityDBJMHTest.testIterateValues             10000  thrpt    2      421.415          ops/s
InfinityDBJMHTest.testIterateValues            100000  thrpt    2       33.711          ops/s
InfinityDBJMHTest.testIterateValues           1000000  thrpt    2        2.835          ops/s
InfinityDBJMHTest.testPutAndRemove                  0  thrpt    2   210620.615          ops/s
InfinityDBJMHTest.testPutAndRemove                  1  thrpt    2   201967.304          ops/s
InfinityDBJMHTest.testPutAndRemove                 10  thrpt    2   198272.620          ops/s
InfinityDBJMHTest.testPutAndRemove                100  thrpt    2   147114.722          ops/s
InfinityDBJMHTest.testPutAndRemove               1000  thrpt    2   195863.534          ops/s
InfinityDBJMHTest.testPutAndRemove              10000  thrpt    2   488445.748          ops/s
InfinityDBJMHTest.testPutAndRemove             100000  thrpt    2   523575.919          ops/s
InfinityDBJMHTest.testPutAndRemove            1000000  thrpt    2   496160.074          ops/s
InfinityDBJMHTest.testPutAndRemoveItemSpace         0  thrpt    2   415157.833          ops/s
InfinityDBJMHTest.testPutAndRemoveItemSpace         1  thrpt    2   427640.348          ops/s
InfinityDBJMHTest.testPutAndRemoveItemSpace        10  thrpt    2   401591.174          ops/s
InfinityDBJMHTest.testPutAndRemoveItemSpace       100  thrpt    2   309627.808          ops/s
InfinityDBJMHTest.testPutAndRemoveItemSpace      1000  thrpt    2   383137.626          ops/s
InfinityDBJMHTest.testPutAndRemoveItemSpace     10000  thrpt    2   899923.410          ops/s
InfinityDBJMHTest.testPutAndRemoveItemSpace    100000  thrpt    2   939347.803          ops/s
InfinityDBJMHTest.testPutAndRemoveItemSpace   1000000  thrpt    2   999958.337          ops/s
InfinityDBJMHTest.testStreams                       0  thrpt    2   187775.359          ops/s
InfinityDBJMHTest.testStreams                       1  thrpt    2   239705.873          ops/s
InfinityDBJMHTest.testStreams                      10  thrpt    2    74142.603          ops/s
InfinityDBJMHTest.testStreams                     100  thrpt    2     6298.659          ops/s
InfinityDBJMHTest.testStreams                    1000  thrpt    2      854.418          ops/s
InfinityDBJMHTest.testStreams                   10000  thrpt    2      247.787          ops/s
InfinityDBJMHTest.testStreams                  100000  thrpt    2       20.221          ops/s
InfinityDBJMHTest.testStreams                 1000000  thrpt    2        1.586          ops/s

Here is the output of one trial of one benchmark — testStreams over a 1M Entry database. Because the test iterates the entire database on each ‘operation’, the streams are scanning at 1.5M/sec.

# JMH 1.13 (released 543 days ago, please consider updating!)
# VM version: JDK 1.8.0_131, VM 25.131-b11
# VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/bin/java
# VM options: -Xmx4g
# Warmup: 5 iterations, 1 s each
# Measurement: 2 iterations, 1 s each
# Timeout: 10 min per iteration
# Threads: 8 threads, will synchronize iterations
# Benchmark mode: Throughput, ops/time
# Benchmark: com.infinitydb.InfinityDBJMHTest.testStreams
# Parameters: (dbSize = 1000000)

# Run progress: 98.44% complete, ETA 00:00:11
# Fork: 1 of 1
# Warmup Iteration   1: 1.633 ops/s
# Warmup Iteration   2: 1.612 ops/s
# Warmup Iteration   3: 1.621 ops/s
# Warmup Iteration   4: 1.627 ops/s
# Warmup Iteration   5: 1.609 ops/s
Iteration   1: 1.607 ops/s
Iteration   2: 1.566 ops/s

Result "testStreams":
  1.586 ops/s

concurency, dbms, jmh, performance, performance analysis, performance and scalability

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