Java data structures and algorithms: practical optimization of concurrent programming

In Java, concurrent programming optimizes multi-threaded application performance through concurrent data structures and algorithms: Atomic operations: Using atomic variables (such as AtomicInteger) guarantees that the operation is executed as a whole. Concurrent data structures: Use thread-safe data structures such as ConcurrentHashMap, ConcurrentLinkedQueue, and CopyOnWriteArrayList. Lock: Use synchronized and Lock interfaces to protect critical section code.

Java Data Structures and Algorithms: Practical Optimization of Concurrent Programming

In multi-threaded applications, concurrent programming is important for improving performance and Responsiveness is critical. In Java, we can use concurrent data structures and algorithms to optimize concurrent scenarios.

Atomic Operations

Atomic operations ensure that a set of operations are performed as a whole, or not performed at all. Atomic variables are provided by classes AtomicInteger, AtomicReference and AtomicBoolean in Java.

AtomicInteger counter = new AtomicInteger();
counter.incrementAndGet();

Concurrent data structure

• ConcurrentHashMap: A thread-safe hash table that allows multiple threads to access and update simultaneously.
• ConcurrentLinkedQueue: A thread-safe queue that supports multi-threaded insertion and deletion.
• CopyOnWriteArrayList: A thread-safe list that creates a copy of the list on each iteration.

ConcurrentHashMap<String, Integer> map = new ConcurrentHashMap<>();
map.put("key", 10);

Lock

• synchronized: A built-in lock that can be used to protect critical section code.
• Lock interface: Provides more fine-grained lock control, such as reentrant locks and read-write locks.

synchronized (lock) {
    // 临界区代码
}

Practical case: Concurrency counter

Consider a web application that needs to count requests. Since requests may occur concurrently, a thread-safe counter is required:

import java.util.concurrent.atomic.AtomicLong;

public class Counter {

    private AtomicLong count = new AtomicLong();

    public long increment() {
        return count.incrementAndGet();
    }

    public long getCount() {
        return count.get();
    }
}

In the increment() method, we use AtomicLong's incrementAndGet () method atomically increases the count by 1. In the getCount() method we return the current value of the count.

By using concurrent data structures and locks, we ensure that the application's counts remain accurate and consistent in a concurrent environment.

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