Prompt: Write a Java benchmark program using JMH, that benchmarks TreeSet vs PriorityQueue.
Here is a complete Java benchmark program using JMH (Java Microbenchmark Harness) to compare the performance of TreeSet and PriorityQueue. The benchmark focuses on three key operations: adding elements, adding and removing the smallest element, and checking for element existence. Benchmark Overview We will measure the performance of TreeSet and PriorityQueue for the following operations:
Adding Elements:
- TreeSet.add(): O(log n) per insertion.
- PriorityQueue.offer(): O(log n) per insertion. Adding and Removing the Smallest Element:
- Add N elements and then remove the smallest element N times.
- TreeSet.pollFirst(): O(log n) per removal.
- PriorityQueue.poll(): O(log n) per removal. Checking for Element Existence:
- TreeSet.contains(): O(log n) per check.
- PriorityQueue.contains(): O(n) per check (linear search).
The benchmark uses JMH annotations to configure the execution, including multiple input sizes (N = 1000, 10000, 100000) to observe scaling behavior.
import org.openjdk.jmh.annotations.*;
import org.openjdk.jmh.infra.Blackhole;
import java.util.*;
import java.util.concurrent.TimeUnit;
import java.util.stream.*;
@State(Scope.Benchmark)
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.MILLISECONDS)
@Fork(1)
@Warmup(iterations = 5, time = 1)
@Measurement(iterations = 5, time = 1)
public class TreeSetVsPriorityQueueBenchmark {
/** Size of the data structure, parameterized to test multiple values */
@Param({"1000", "10000", "100000"})
public int N;
/** List of unique elements to add to the data structures */
public List<Integer> elementsToAdd;
/** List of elements to check for existence (mix of present and absent) */
public List<Integer> elementsToCheck;
/**
* Setup method to prepare the input data before each trial.
* Runs once per fork for each value of N.
*/
@Setup(Level.Trial)
public void setup() {
// Generate N unique integers (0 to N-1) and shuffle them
elementsToAdd = IntStream.range(0, N)
.boxed()
.collect(Collectors.toList());
Collections.shuffle(elementsToAdd);
// Generate N elements to check: ~half present, ~half absent
List<Integer> present = new ArrayList<>(elementsToAdd); // Copy of elementsToAdd
List<Integer> absent = IntStream.range(N, 2 * N)
.boxed()
.collect(Collectors.toList());
elementsToCheck = new ArrayList<>();
Random rand = new Random(0); // Fixed seed for reproducibility
for (int i = 0; i < N; i++) {
if (rand.nextBoolean()) {
elementsToCheck.add(present.get(i % present.size()));
} else {
elementsToCheck.add(absent.get(i % absent.size()));
}
}
}
/** Benchmark adding elements to TreeSet */
@Benchmark
public void treeSetAdd(Blackhole blackhole) {
TreeSet<Integer> set = new TreeSet<>();
for (Integer e : elementsToAdd) {
set.add(e);
}
blackhole.consume(set); // Prevent dead code elimination
}
/** Benchmark adding elements to PriorityQueue */
@Benchmark
public void priorityQueueOffer(Blackhole blackhole) {
PriorityQueue<Integer> pq = new PriorityQueue<>();
for (Integer e : elementsToAdd) {
pq.offer(e);
}
blackhole.consume(pq);
}
/** Benchmark adding and removing smallest elements from TreeSet */
@Benchmark
public void treeSetAddAndRemove(Blackhole blackhole) {
TreeSet<Integer> set = new TreeSet<>();
for (Integer e : elementsToAdd) {
set.add(e);
}
while (!set.isEmpty()) {
blackhole.consume(set.pollFirst());
}
}
/** Benchmark adding and removing smallest elements from PriorityQueue */
@Benchmark
public void priorityQueueOfferAndPoll(Blackhole blackhole) {
PriorityQueue<Integer> pq = new PriorityQueue<>();
for (Integer e : elementsToAdd) {
pq.offer(e);
}
while (!pq.isEmpty()) {
blackhole.consume(pq.poll());
}
}
/** Benchmark checking element existence in TreeSet */
@Benchmark
public void treeSetContains(Blackhole blackhole) {
TreeSet<Integer> set = new TreeSet<>();
for (Integer e : elementsToAdd) {
set.add(e);
}
for (Integer e : elementsToCheck) {
blackhole.consume(set.contains(e));
}
}
/** Benchmark checking element existence in PriorityQueue */
@Benchmark
public void priorityQueueContains(Blackhole blackhole) {
PriorityQueue<Integer> pq = new PriorityQueue<>();
for (Integer e : elementsToAdd) {
pq.offer(e);
}
for (Integer e : elementsToCheck) {
blackhole.consume(pq.contains(e));
}
}
/** Main method to run the benchmark (optional for manual execution) */
public static void main(String[] args) throws Exception {
org.openjdk.jmh.Main.main(args);
}
}Explanation of Key Components Annotations
- @State(Scope.Benchmark): Defines a shared state across all benchmark threads for a given trial, holding the input data.
- @BenchmarkMode(Mode.AverageTime): Measures the average execution time of each operation.
- @OutputTimeUnit(TimeUnit.MILLISECONDS): Reports results in milliseconds.
- @Fork(1): Runs the benchmark in one JVM fork to isolate results.
- @Warmup and @Measurement: Configures 5 warmup iterations and 5 measurement iterations, each lasting 1 second, to stabilize and measure performance.
State Setup
- N: Parameterized with @Param to test different sizes (1000, 10000, 100000).
- elementsToAdd: A shuffled list of N unique integers (0 to N-1) ensures random insertion order. Using unique elements makes the comparison fair, as TreeSet rejects duplicates while PriorityQueue allows them.
- elementsToCheck: A list of N integers, approximately half present (from elementsToAdd) and half absent (from N to 2N-1), generated with a fixed-seed Random for reproducibility.
Benchmark Methods Each method follows a similar pattern:
- Create a new instance of the data structure (TreeSet or PriorityQueue). 2.Perform the operation(s) being benchmarked.
- Use Blackhole.consume() to prevent the JVM from optimizing away the computation.
Adding Elements:
- treeSetAdd: Adds N elements to a TreeSet.
- priorityQueueOffer: Adds N elements to a PriorityQueue. Adding and Removing:
- treeSetAddAndRemove: Adds N elements, then removes the smallest element N times.
- priorityQueueOfferAndPoll: Same, but for PriorityQueue. Contains Checks:
- treeSetContains: Adds N elements, then checks N elements for existence.
- priorityQueueContains: Same, but for PriorityQueue.
Running the Benchmark To run the benchmark, you need the JMH library. If you're using Maven, add the following dependencies to your pom.xml:
<dependencies>
<dependency>
<groupId>org.openjdk.jmh</groupId>
<artifactId>jmh-core</artifactId>
<version>1.36</version>
</dependency>
<dependency>
<groupId>org.openjdk.jmh</groupId>
<artifactId>jmh-generator-annprocess</artifactId>
<version>1.36</version>
</dependency>
</dependencies>Run via Maven:
mvn clean install
java -jar target/benchmarks.jarExpected Results
- Adding Elements: Both TreeSet and PriorityQueue should perform similarly (O(N log N) total time), though PriorityQueue (binary heap) may have a lower constant factor than TreeSet (red-black tree) due to simpler balancing.
- Adding and Removing: Similar performance is expected (O(N log N) total time), with PriorityQueue potentially faster due to heap operations being lighter than tree rebalancing.
- Contains Checks: TreeSet should significantly outperform PriorityQueue (O(N log N) vs. O(N²) total time) because PriorityQueue uses a linear search for contains().
These results will help you choose between TreeSet and PriorityQueue based on your application's needs (e.g., fast membership checks vs. priority-based removal).
This benchmark provides a comprehensive comparison of TreeSet and PriorityQueue for common operations, suitable for evaluating their performance in various scenarios.
Prompt:
show me the complete pom.xml to run this benchmark
To run a JMH (Java Microbenchmark Harness) benchmark, you need a properly configured pom.xml file that includes the necessary dependencies and plugins. Below is a complete pom.xml file tailored for running a JMH benchmark, such as one comparing TreeSet and PriorityQueue. This configuration ensures that the benchmark is compiled with annotation processing and packaged into an executable JAR for easy execution.
Complete pom.xml
Explanation of the Configuration
How to Use This pom.xml
Notes
This pom.xml provides a robust setup for running JMH benchmarks, handling both compilation and execution seamlessly.