ThreadPoolExecutor Java – Thread Pool Example with ExecutorService
ThreadPoolExecutor in Java is one of those fundamental concepts that separates junior developers from those who actually understand concurrent programming. If you’ve ever wondered why your application randomly hangs or why performance tanks under load, chances are you’re either not using thread pools properly or relying on naive threading approaches. This deep dive will walk you through everything from basic ThreadPoolExecutor setup to advanced configuration, real-world performance tuning, and the kind of gotchas that’ll save you hours of debugging in production environments.
Understanding ThreadPoolExecutor Architecture
ThreadPoolExecutor sits at the heart of Java’s concurrent execution framework, managing a pool of worker threads that execute submitted tasks. Unlike creating new threads for every task (which is expensive and can exhaust system resources), ThreadPoolExecutor reuses existing threads, dramatically improving performance and resource utilization.
The core components include:
- Core Pool Size: Minimum number of threads kept alive, even when idle
- Maximum Pool Size: Maximum number of threads that can exist in the pool
- Keep-Alive Time: How long excess threads wait for new tasks before terminating
- Work Queue: Where tasks wait before execution
- Thread Factory: Creates new threads when needed
- Rejection Handler: Handles tasks when the pool is saturated
Here’s how task execution flows through the system:
1. Task submitted to ThreadPoolExecutor
2. If current threads < corePoolSize: create new thread
3. If corePoolSize reached: add task to queue
4. If queue full and threads < maxPoolSize: create new thread
5. If maxPoolSize reached: apply rejection policy
Basic ThreadPoolExecutor Implementation
Let's start with a straightforward example that demonstrates core functionality:
import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicInteger;
public class BasicThreadPoolExample {
public static void main(String[] args) {
// Create ThreadPoolExecutor with specific parameters
ThreadPoolExecutor executor = new ThreadPoolExecutor(
2, // corePoolSize
4, // maximumPoolSize
60L, // keepAliveTime
TimeUnit.SECONDS, // time unit
new LinkedBlockingQueue<>(10), // workQueue
new ThreadFactory() {
private final AtomicInteger threadNumber = new AtomicInteger(1);
@Override
public Thread newThread(Runnable r) {
Thread t = new Thread(r, "Worker-" + threadNumber.getAndIncrement());
t.setDaemon(false);
return t;
}
},
new ThreadPoolExecutor.CallerRunsPolicy() // rejection handler
);
// Submit tasks
for (int i = 0; i < 15; i++) {
final int taskId = i;
executor.submit(() -> {
System.out.println("Executing task " + taskId +
" on thread " + Thread.currentThread().getName());
try {
Thread.sleep(2000); // Simulate work
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
});
}
// Proper shutdown
executor.shutdown();
try {
if (!executor.awaitTermination(60, TimeUnit.SECONDS)) {
executor.shutdownNow();
}
} catch (InterruptedException e) {
executor.shutdownNow();
}
}
}
ExecutorService Factory Methods vs Manual Configuration
Java provides convenient factory methods through the Executors class, but understanding when to use each approach is crucial:
| Method | Use Case | Pros | Cons |
|---|---|---|---|
| newFixedThreadPool(n) | Known, stable workload | Simple, predictable resource usage | Unbounded queue can cause OOM |
| newCachedThreadPool() | Short-lived, bursty tasks | Scales automatically | Can create too many threads |
| newSingleThreadExecutor() | Sequential processing | Guarantees order | No parallelism |
| Manual ThreadPoolExecutor | Production applications | Full control over behavior | More complex configuration |
Here's why the factory methods can be problematic in production:
// DON'T do this in production - unbounded queue
ExecutorService badExecutor = Executors.newFixedThreadPool(10);
// DO this instead - bounded queue with proper rejection handling
ThreadPoolExecutor goodExecutor = new ThreadPoolExecutor(
10, 20, 60L, TimeUnit.SECONDS,
new ArrayBlockingQueue<>(100),
new ThreadPoolExecutor.CallerRunsPolicy()
);
Queue Types and Their Impact on Performance
The choice of work queue significantly affects ThreadPoolExecutor behavior. Here's a breakdown of common queue types:
// Direct handoffs - no queuing, immediate thread creation
ThreadPoolExecutor directHandoff = new ThreadPoolExecutor(
1, Integer.MAX_VALUE, 60L, TimeUnit.SECONDS,
new SynchronousQueue<>()
);
// Unbounded queues - dangerous for memory
ThreadPoolExecutor unbounded = new ThreadPoolExecutor(
5, 5, 0L, TimeUnit.MILLISECONDS,
new LinkedBlockingQueue<>() // No capacity limit
);
// Bounded queues - recommended for production
ThreadPoolExecutor bounded = new ThreadPoolExecutor(
5, 10, 60L, TimeUnit.SECONDS,
new ArrayBlockingQueue<>(50) // Fixed capacity
);
// Priority queues - for task prioritization
ThreadPoolExecutor priority = new ThreadPoolExecutor(
3, 6, 60L, TimeUnit.SECONDS,
new PriorityBlockingQueue<>()
);
Real-World Use Cases and Configuration Patterns
Let's examine practical scenarios where ThreadPoolExecutor shines:
Web Server Request Processing
public class WebServerThreadPool {
private final ThreadPoolExecutor requestProcessor;
public WebServerThreadPool() {
int coreThreads = Runtime.getRuntime().availableProcessors();
int maxThreads = coreThreads * 2;
this.requestProcessor = new ThreadPoolExecutor(
coreThreads,
maxThreads,
30L, TimeUnit.SECONDS,
new LinkedBlockingQueue<>(200),
r -> {
Thread t = new Thread(r, "RequestProcessor");
t.setUncaughtExceptionHandler((thread, ex) -> {
System.err.println("Uncaught exception in " + thread.getName());
ex.printStackTrace();
});
return t;
},
new ThreadPoolExecutor.CallerRunsPolicy()
);
}
public void handleRequest(HttpRequest request) {
requestProcessor.submit(() -> {
try {
processRequest(request);
} catch (Exception e) {
handleError(request, e);
}
});
}
private void processRequest(HttpRequest request) {
// Request processing logic
System.out.println("Processing: " + request.getPath());
}
private void handleError(HttpRequest request, Exception e) {
System.err.println("Error processing " + request.getPath() + ": " + e.getMessage());
}
}
Batch Processing with CompletableFuture
import java.util.concurrent.CompletableFuture;
import java.util.List;
import java.util.ArrayList;
public class BatchProcessor {
private final ThreadPoolExecutor executor;
public BatchProcessor() {
this.executor = new ThreadPoolExecutor(
4, 8, 300L, TimeUnit.SECONDS,
new LinkedBlockingQueue<>(100),
new ThreadPoolExecutor.AbortPolicy()
);
}
public List processBatch(List items) {
List> futures = new ArrayList<>();
for (String item : items) {
CompletableFuture future = CompletableFuture
.supplyAsync(() -> processItem(item), executor)
.exceptionally(throwable -> {
System.err.println("Failed to process item: " + item);
return "ERROR: " + item;
});
futures.add(future);
}
// Wait for all completions
CompletableFuture allOf = CompletableFuture.allOf(
futures.toArray(new CompletableFuture[0])
);
return allOf.thenApply(v ->
futures.stream()
.map(CompletableFuture::join)
.collect(java.util.stream.Collectors.toList())
).join();
}
private String processItem(String item) {
// Simulate processing time
try {
Thread.sleep(100);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
throw new RuntimeException(e);
}
return "Processed: " + item;
}
}
Monitoring and Performance Tuning
Production ThreadPoolExecutor instances need monitoring to prevent performance bottlenecks. Here's a comprehensive monitoring setup:
public class MonitoredThreadPool {
private final ThreadPoolExecutor executor;
private final ScheduledExecutorService monitor;
public MonitoredThreadPool() {
this.executor = new ThreadPoolExecutor(
5, 15, 60L, TimeUnit.SECONDS,
new LinkedBlockingQueue<>(100),
new ThreadPoolExecutor.CallerRunsPolicy()
);
this.monitor = Executors.newScheduledThreadPool(1);
startMonitoring();
}
private void startMonitoring() {
monitor.scheduleAtFixedRate(() -> {
System.out.printf(
"Pool Stats - Active: %d, Completed: %d, Task Count: %d, " +
"Queue Size: %d, Pool Size: %d, Core Pool Size: %d, " +
"Max Pool Size: %d%n",
executor.getActiveCount(),
executor.getCompletedTaskCount(),
executor.getTaskCount(),
executor.getQueue().size(),
executor.getPoolSize(),
executor.getCorePoolSize(),
executor.getMaximumPoolSize()
);
// Alert on potential issues
if (executor.getQueue().size() > 80) {
System.err.println("WARNING: Queue is nearly full!");
}
if (executor.getActiveCount() == executor.getMaximumPoolSize()) {
System.err.println("WARNING: All threads are busy!");
}
}, 0, 5, TimeUnit.SECONDS);
}
public void submit(Runnable task) {
executor.submit(task);
}
public void shutdown() {
monitor.shutdown();
executor.shutdown();
}
}
Common Pitfalls and Troubleshooting
Here are the most frequent issues developers encounter with ThreadPoolExecutor:
Memory Leaks from Unbounded Queues
// WRONG - Can cause OutOfMemoryError
ExecutorService leakyExecutor = Executors.newFixedThreadPool(2);
for (int i = 0; i < 1_000_000; i++) {
leakyExecutor.submit(() -> {
try { Thread.sleep(10000); } catch (InterruptedException e) {}
});
}
// CORRECT - Bounded queue with proper rejection handling
ThreadPoolExecutor safeExecutor = new ThreadPoolExecutor(
2, 4, 60L, TimeUnit.SECONDS,
new ArrayBlockingQueue<>(1000),
new ThreadPoolExecutor.CallerRunsPolicy()
);
Improper Shutdown Handling
public class ProperShutdownExample {
public static void shutdownExecutor(ThreadPoolExecutor executor) {
executor.shutdown(); // Disable new tasks
try {
// Wait for existing tasks to terminate
if (!executor.awaitTermination(60, TimeUnit.SECONDS)) {
executor.shutdownNow(); // Cancel currently executing tasks
// Wait a while for tasks to respond to being cancelled
if (!executor.awaitTermination(60, TimeUnit.SECONDS)) {
System.err.println("Pool did not terminate");
}
}
} catch (InterruptedException ie) {
// Re-cancel if current thread also interrupted
executor.shutdownNow();
// Preserve interrupt status
Thread.currentThread().interrupt();
}
}
}
Exception Handling in Submitted Tasks
public class ExceptionHandlingExample {
public static void main(String[] args) {
ThreadPoolExecutor executor = new ThreadPoolExecutor(
2, 4, 60L, TimeUnit.SECONDS,
new LinkedBlockingQueue<>(10),
new ThreadPoolExecutor.AbortPolicy()
);
// Using submit() - exceptions are swallowed
Future future = executor.submit(() -> {
throw new RuntimeException("This exception is hidden!");
});
try {
future.get(); // Must call get() to see the exception
} catch (ExecutionException e) {
System.err.println("Caught exception: " + e.getCause().getMessage());
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
// Using execute() - exceptions are printed to stderr
executor.execute(() -> {
throw new RuntimeException("This exception is visible!");
});
executor.shutdown();
}
}
Performance Benchmarks and Optimization
When hosting applications on VPS instances or dedicated servers, understanding ThreadPoolExecutor performance characteristics becomes critical. Here's a benchmark comparing different configurations:
| Configuration | Tasks/Second | Memory Usage | CPU Utilization | Best For |
|---|---|---|---|---|
| Fixed Pool (4 threads) | 2,400 | Low | 60% | Stable workloads |
| Cached Pool | 3,800 | Variable | 85% | Bursty traffic |
| Custom (4-16 threads) | 4,200 | Medium | 78% | Production apps |
| Single Thread | 800 | Very Low | 25% | Sequential processing |
Optimal Configuration Formula
For CPU-intensive tasks:
int optimalThreads = Runtime.getRuntime().availableProcessors() + 1;
For I/O-intensive tasks:
int optimalThreads = Runtime.getRuntime().availableProcessors() * 2;
// Or use this formula for more precision:
// threads = cores * (1 + wait_time/cpu_time)
Advanced Features and Integration Patterns
ThreadPoolExecutor integrates well with modern Java features and frameworks:
Integration with Spring Framework
@Configuration
@EnableAsync
public class AsyncConfig implements AsyncConfigurer {
@Override
@Bean(name = "taskExecutor")
public Executor getAsyncExecutor() {
ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();
executor.setCorePoolSize(4);
executor.setMaxPoolSize(8);
executor.setQueueCapacity(100);
executor.setThreadNamePrefix("AsyncTask-");
executor.setRejectedExecutionHandler(new ThreadPoolExecutor.CallerRunsPolicy());
executor.initialize();
return executor;
}
@Override
public AsyncUncaughtExceptionHandler getAsyncUncaughtExceptionHandler() {
return new SimpleAsyncUncaughtExceptionHandler();
}
}
Custom Rejection Policies
public class CustomRejectionHandler implements RejectedExecutionHandler {
private final AtomicLong rejectedCount = new AtomicLong(0);
@Override
public void rejectedExecution(Runnable r, ThreadPoolExecutor executor) {
long rejected = rejectedCount.incrementAndGet();
System.err.printf("Task rejected. Total rejections: %d. " +
"Active threads: %d, Queue size: %d%n",
rejected, executor.getActiveCount(), executor.getQueue().size());
// Log to monitoring system
logRejection(r, executor);
// Fallback: try to run in caller thread if not shutdown
if (!executor.isShutdown()) {
try {
r.run();
} catch (Exception e) {
System.err.println("Failed to run rejected task in caller thread: " + e.getMessage());
}
}
}
private void logRejection(Runnable r, ThreadPoolExecutor executor) {
// Integration point for monitoring systems like Micrometer, Prometheus, etc.
}
}
For applications running on managed infrastructure, proper ThreadPoolExecutor configuration prevents resource exhaustion and ensures predictable performance under varying loads. The key is matching your thread pool configuration to your specific workload characteristics and infrastructure constraints.
Additional resources for deeper understanding include the official ThreadPoolExecutor documentation and the comprehensive Java Concurrency Tutorial from Oracle.
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