Java 동시성 컬렉션과 유틸리티

들어가며

멀티스레드 환경에서 일반 컬렉션(ArrayList, HashMap)을 사용하면 **경쟁 조건(Race Condition)**이 발생해요. synchronized로 모든 메서드를 감싸는 Vector나 Hashtable은 성능이 떨어지죠.

Java는 java.util.concurrent 패키지에서 고성능 동시성 컬렉션을 제공해요. 이 문서에서는 실무에서 자주 사용되는 동시성 자료구조와 락 메커니즘을 다뤄볼게요.

출처: Java Documentation - Concurrent Collections

1. ConcurrentHashMap

1.1 문제: HashMap의 동시성 이슈

// 일반 HashMap: 멀티스레드 환경에서 안전하지 않음
Map<String, Integer> map = new HashMap<>();

// 스레드 1
map.put("count", 1);

// 스레드 2 (동시 실행)
map.put("count", 2);  // Race Condition!

// 최악의 경우 내부 구조가 깨져서 무한 루프 발생 가능 (Java 7 이전)

Hashtable의 문제:

// Hashtable: 모든 메서드가 synchronized
Hashtable<String, Integer> table = new Hashtable<>();

// 문제: 모든 연산이 전체 테이블을 잠금
table.put("key1", 1);  // 전체 락
table.get("key2");     // 읽기도 전체 락 (비효율적!)

1.2 ConcurrentHashMap의 해결책: 세그먼트 락 (Java 8 이전)

Java 7까지는 세그먼트(Segment) 락을 사용했어요.

// 개념적 구조 (실제 코드는 아님)
class ConcurrentHashMap<K, V> {
    Segment<K, V>[] segments = new Segment[16];  // 기본 16개 세그먼트

    static class Segment<K, V> {
        ReentrantLock lock = new ReentrantLock();
        HashEntry<K, V>[] table;
    }
}

동작 방식:

예시:

1.3 ConcurrentHashMap의 개선: CAS 기반 (Java 8 이후)

Java 8부터는 세그먼트를 제거하고 CAS(Compare-And-Swap) + synchronized를 사용해요.

// 간소화된 put 구조
public V put(K key, V value) {
    int hash = hash(key);

    for (Node<K,V>[] tab = table;;) {
        Node<K,V> f = tabAt(tab, i);  // volatile read

        if (f == null) {
            // 빈 버킷: CAS로 원자적 삽입
            if (casTabAt(tab, i, null, new Node<K,V>(hash, key, value))) {
                break;  // 성공!
            }
        } else {
            // 충돌 발생: 해당 버킷만 synchronized
            synchronized (f) {
                // 버킷 내에서만 락 (매우 세밀한 락)
                // 체이닝 또는 트리에 삽입
            }
        }
    }
}

핵심 아이디어:

빈 버킷: CAS로 락 없이 삽입 (빠름)
충돌 버킷: 해당 버킷 헤드 노드만 락 (세그먼트보다 세밀)
읽기: volatile read로 락 없이 수행

성능 비교:

// Hashtable: 전체 락 (최악)
synchronized (전체 테이블) {
    // 모든 연산
}

// ConcurrentHashMap (Java 7): 세그먼트 락 (중간)
synchronized (segments[i]) {
    // 해당 세그먼트만
}

// ConcurrentHashMap (Java 8+): 버킷 락 (최고)
synchronized (bucket[i]) {
    // 해당 버킷만
}

1.4 실무 사용 예제

import java.util.concurrent.ConcurrentHashMap;

// 사용자 세션 관리
public class SessionManager {
    private ConcurrentHashMap<String, UserSession> sessions = new ConcurrentHashMap<>();

    public void createSession(String sessionId, UserSession session) {
        // putIfAbsent: 없을 때만 삽입 (원자적)
        UserSession existing = sessions.putIfAbsent(sessionId, session);
        if (existing != null) {
            throw new IllegalStateException("Session already exists");
        }
    }

    public void updateLastAccessTime(String sessionId) {
        // compute: 키의 값을 원자적으로 계산
        sessions.computeIfPresent(sessionId, (id, session) -> {
            session.setLastAccessTime(System.currentTimeMillis());
            return session;
        });
    }

    public void removeExpiredSessions() {
        long now = System.currentTimeMillis();
        long timeout = 30 * 60 * 1000;  // 30분

        // 스레드 안전하게 순회하면서 삭제
        sessions.entrySet().removeIf(entry ->
            now - entry.getValue().getLastAccessTime() > timeout
        );
    }
}

// 카운터 (원자적 증가)
public class UrlClickCounter {
    private ConcurrentHashMap<String, Long> clicks = new ConcurrentHashMap<>();

    public void recordClick(String url) {
        // merge: 키가 있으면 함수 적용, 없으면 초기값 삽입
        clicks.merge(url, 1L, (oldValue, one) -> oldValue + 1);

        // 또는 compute 사용
        // clicks.compute(url, (k, v) -> (v == null) ? 1L : v + 1);
    }

    public long getClicks(String url) {
        return clicks.getOrDefault(url, 0L);
    }
}

주의사항:

ConcurrentHashMap<String, List<String>> map = new ConcurrentHashMap<>();

// 잘못된 코드: 경쟁 조건 발생!
if (!map.containsKey("users")) {
    map.put("users", new ArrayList<>());  // 사이에 다른 스레드가 끼어들 수 있음
}
map.get("users").add("Alice");

// 올바른 코드: 원자적 연산 사용
map.computeIfAbsent("users", k -> new ArrayList<>()).add("Alice");

출처: Java Documentation - ConcurrentHashMap, Baeldung - Guide to ConcurrentHashMap

2. CopyOnWriteArrayList

2.1 개념: 쓰기 시 복사

읽기가 압도적으로 많고 쓰기가 드문 경우에 사용해요.

import java.util.concurrent.CopyOnWriteArrayList;

public class CopyOnWriteArrayList<E> {
    private volatile Object[] array;

    public boolean add(E e) {
        synchronized (lock) {
            Object[] oldArray = array;
            int len = oldArray.length;

            // 배열 전체를 복사 (비용이 큼!)
            Object[] newArray = Arrays.copyOf(oldArray, len + 1);
            newArray[len] = e;

            // volatile write로 배열 교체
            array = newArray;
        }
        return true;
    }

    public E get(int index) {
        // 락 없음! volatile read만
        return (E) array[index];
    }
}

동작 원리:

2.2 사용 사례

// 이벤트 리스너 관리 (전형적인 사용 사례)
public class EventBus {
    private CopyOnWriteArrayList<EventListener> listeners = new CopyOnWriteArrayList<>();

    // 리스너 등록: 드물게 발생
    public void addListener(EventListener listener) {
        listeners.add(listener);  // 배열 복사 발생
    }

    // 이벤트 발생: 매우 자주 발생
    public void publishEvent(Event event) {
        // 락 없이 빠르게 순회
        for (EventListener listener : listeners) {
            listener.onEvent(event);
        }
    }
}

// 설정 관리
public class ConfigurationManager {
    private CopyOnWriteArrayList<String> allowedIps = new CopyOnWriteArrayList<>();

    // 설정 변경: 드물게 발생 (관리자가 수동으로)
    public void addAllowedIp(String ip) {
        allowedIps.add(ip);
    }

    // IP 검증: 매 요청마다 발생 (매우 빈번)
    public boolean isAllowed(String ip) {
        return allowedIps.contains(ip);  // 락 없이 빠르게 확인
    }
}

2.3 장단점

장점:

읽기가 완전히 락 프리 (매우 빠름)
Iterator가 ConcurrentModificationException을 던지지 않음
스냅샷 격리 (읽는 동안 일관된 뷰 보장)

단점:

쓰기마다 전체 배열 복사 (느림, 메모리 사용 증가)
메모리 오버헤드 (두 개 버전이 잠시 공존)
쓰기가 많으면 성능 급격히 저하

사용 기준:

출처: Java Documentation - CopyOnWriteArrayList, Baeldung - CopyOnWriteArrayList Guide

3. BlockingQueue

3.1 개념: Producer-Consumer 패턴

BlockingQueue는 큐가 비었을 때 대기, 큐가 꽉 찼을 때 대기하는 기능을 제공해요.

import java.util.concurrent.BlockingQueue;
import java.util.concurrent.ArrayBlockingQueue;

public class ProducerConsumer {
    private BlockingQueue<Task> queue = new ArrayBlockingQueue<>(10);

    // Producer 스레드
    public void producer() throws InterruptedException {
        Task task = createTask();
        queue.put(task);  // 큐가 꽉 차면 대기 (블록)
    }

    // Consumer 스레드
    public void consumer() throws InterruptedException {
        Task task = queue.take();  // 큐가 비면 대기 (블록)
        process(task);
    }
}

내부 구조 (간소화):

public class ArrayBlockingQueue<E> {
    private final Object[] items;
    private int count;  // 현재 요소 수

    private final Lock lock = new ReentrantLock();
    private final Condition notEmpty = lock.newCondition();
    private final Condition notFull = lock.newCondition();

    public void put(E e) throws InterruptedException {
        lock.lock();
        try {
            while (count == items.length) {
                notFull.await();  // 꽉 참: 대기
            }
            enqueue(e);
            notEmpty.signal();  // Consumer 깨우기
        } finally {
            lock.unlock();
        }
    }

    public E take() throws InterruptedException {
        lock.lock();
        try {
            while (count == 0) {
                notEmpty.await();  // 비어있음: 대기
            }
            E item = dequeue();
            notFull.signal();  // Producer 깨우기
            return item;
        } finally {
            lock.unlock();
        }
    }
}

3.2 BlockingQueue 구현체들

3.2.1 ArrayBlockingQueue

특징: 고정 크기 배열 기반

BlockingQueue<Task> queue = new ArrayBlockingQueue<>(100);  // 최대 100개

// 용량 초과 시 대기
queue.put(task);  // 블록
queue.offer(task, 1, TimeUnit.SECONDS);  // 1초 타임아웃

사용 사례: 백프레셔(Backpressure) 제어가 필요한 경우

// API 요청 처리 제한
public class RateLimitedApiHandler {
    private BlockingQueue<ApiRequest> queue = new ArrayBlockingQueue<>(1000);

    public boolean handleRequest(ApiRequest request) {
        // 큐가 꽉 차면 거부 (서버 과부하 방지)
        return queue.offer(request);
    }
}

3.2.2 LinkedBlockingQueue

특징: 링크 노드 기반, 용량 제한 가능 (기본 무제한)

BlockingQueue<Task> unbounded = new LinkedBlockingQueue<>();  // 무제한
BlockingQueue<Task> bounded = new LinkedBlockingQueue<>(100);  // 100개 제한

내부 최적화:

// 두 개의 락으로 성능 향상
class LinkedBlockingQueue<E> {
    private final ReentrantLock takeLock = new ReentrantLock();
    private final ReentrantLock putLock = new ReentrantLock();

    // put과 take가 동시에 실행 가능!
}

사용 사례: ThreadPoolExecutor의 기본 작업 큐

3.2.3 PriorityBlockingQueue

특징: 우선순위 기반 (힙 구조)

// 우선순위 작업 큐
BlockingQueue<Task> queue = new PriorityBlockingQueue<>(10,
    (t1, t2) -> Integer.compare(t2.priority, t1.priority)  // 높은 우선순위 먼저
);

queue.put(new Task("Low", 1));
queue.put(new Task("High", 10));
queue.put(new Task("Medium", 5));

queue.take();  // "High" (우선순위 10)
queue.take();  // "Medium" (우선순위 5)
queue.take();  // "Low" (우선순위 1)

3.2.4 SynchronousQueue

특징: 용량이 0인 큐 (직접 핸드오프)

BlockingQueue<Task> queue = new SynchronousQueue<>();

// Producer
queue.put(task);  // Consumer가 take() 호출할 때까지 블록

// Consumer
Task task = queue.take();  // Producer가 put() 호출할 때까지 블록

사용 사례: Cached ThreadPool

// Executors.newCachedThreadPool() 내부 구조
ThreadPoolExecutor executor = new ThreadPoolExecutor(
    0, Integer.MAX_VALUE,
    60L, TimeUnit.SECONDS,
    new SynchronousQueue<>()  // 작업을 바로 스레드에 전달
);

3.3 실무 예제: 비동기 로그 처리

import java.util.concurrent.*;

public class AsyncLogger {
    private BlockingQueue<LogMessage> queue = new LinkedBlockingQueue<>(10000);
    private volatile boolean running = true;

    // 로거 시작
    public void start() {
        Thread writerThread = new Thread(() -> {
            while (running || !queue.isEmpty()) {
                try {
                    LogMessage msg = queue.poll(1, TimeUnit.SECONDS);
                    if (msg != null) {
                        writeToFile(msg);  // I/O 작업
                    }
                } catch (InterruptedException e) {
                    Thread.currentThread().interrupt();
                    break;
                }
            }
        });
        writerThread.setDaemon(false);
        writerThread.start();
    }

    // 로그 기록 (논블로킹)
    public void log(String message) {
        LogMessage msg = new LogMessage(message, System.currentTimeMillis());

        // offer: 큐가 꽉 차면 false 반환 (블록하지 않음)
        if (!queue.offer(msg)) {
            System.err.println("Log queue full, dropping message: " + message);
        }
    }

    // 종료
    public void shutdown() {
        running = false;
    }

    private void writeToFile(LogMessage msg) {
        // 파일에 쓰기 (느린 I/O)
    }
}

출처: Java Documentation - BlockingQueue, Baeldung - Guide to BlockingQueue

4. 고급 락 메커니즘

4.1 ReentrantLock

synchronized보다 더 유연한 락이에요.

import java.util.concurrent.locks.ReentrantLock;

public class Counter {
    private final ReentrantLock lock = new ReentrantLock();
    private int count = 0;

    // 기본 사용
    public void increment() {
        lock.lock();
        try {
            count++;
        } finally {
            lock.unlock();  // 반드시 finally에서 해제
        }
    }

    // tryLock: 데드락 방지
    public boolean tryIncrement() {
        if (lock.tryLock()) {
            try {
                count++;
                return true;
            } finally {
                lock.unlock();
            }
        }
        return false;  // 락 획득 실패
    }

    // 타임아웃
    public boolean incrementWithTimeout() throws InterruptedException {
        if (lock.tryLock(1, TimeUnit.SECONDS)) {
            try {
                count++;
                return true;
            } finally {
                lock.unlock();
            }
        }
        return false;  // 1초 안에 락 획득 못함
    }

    // 인터럽트 가능
    public void incrementInterruptibly() throws InterruptedException {
        lock.lockInterruptibly();  // 인터럽트 가능
        try {
            count++;
        } finally {
            lock.unlock();
        }
    }
}

synchronized vs ReentrantLock:

기능	synchronized	ReentrantLock
자동 해제	O (예외 시 자동)	X (finally 필수)
tryLock	X	O
타임아웃	X	O
인터럽트	X	O
공정성(fairness)	X	O
Condition 변수	X	O (여러 개 가능)

공정한 락 (Fair Lock):

ReentrantLock fairLock = new ReentrantLock(true);  // FIFO 순서 보장

// 대기 순서대로 락 획득
// 단점: 성능 저하 (컨텍스트 스위칭 증가)

4.2 ReadWriteLock

읽기는 동시에, 쓰기는 배타적으로

import java.util.concurrent.locks.ReadWriteLock;
import java.util.concurrent.locks.ReentrantReadWriteLock;

public class CachedData {
    private final ReadWriteLock rwLock = new ReentrantReadWriteLock();
    private Map<String, String> cache = new HashMap<>();

    // 읽기: 여러 스레드 동시 실행 가능
    public String get(String key) {
        rwLock.readLock().lock();
        try {
            return cache.get(key);
        } finally {
            rwLock.readLock().unlock();
        }
    }

    // 쓰기: 배타적 실행
    public void put(String key, String value) {
        rwLock.writeLock().lock();
        try {
            cache.put(key, value);
        } finally {
            rwLock.writeLock().unlock();
        }
    }
}

동작 원리:

읽기 락 보유 중:
  - 다른 읽기 락 획득 가능
  - 쓰기 락 획득 불가능

쓰기 락 보유 중:
  - 다른 읽기 락 획득 불가능
  - 다른 쓰기 락 획득 불가능

실무 예제: 캐시 구현

public class Cache<K, V> {
    private final ReadWriteLock rwLock = new ReentrantReadWriteLock();
    private final Map<K, V> cache = new HashMap<>();

    public V get(K key) {
        rwLock.readLock().lock();
        try {
            V value = cache.get(key);
            if (value != null) {
                return value;
            }
        } finally {
            rwLock.readLock().unlock();
        }

        // 캐시 미스: 쓰기 락으로 전환
        rwLock.writeLock().lock();
        try {
            // Double-check (다른 스레드가 이미 로드했을 수 있음)
            V value = cache.get(key);
            if (value == null) {
                value = loadFromDatabase(key);
                cache.put(key, value);
            }
            return value;
        } finally {
            rwLock.writeLock().unlock();
        }
    }

    public void invalidate(K key) {
        rwLock.writeLock().lock();
        try {
            cache.remove(key);
        } finally {
            rwLock.writeLock().unlock();
        }
    }
}

4.3 StampedLock (Java 8+)

ReadWriteLock보다 더 빠른 낙관적 읽기를 제공해요.

import java.util.concurrent.locks.StampedLock;

public class Point {
    private final StampedLock sl = new StampedLock();
    private double x, y;

    // 낙관적 읽기: 가장 빠름
    public double distanceFromOrigin() {
        long stamp = sl.tryOptimisticRead();  // 락 없이 stamp 획득
        double currentX = x;
        double currentY = y;

        if (!sl.validate(stamp)) {
            // 쓰기가 발생했음: 읽기 락으로 재시도
            stamp = sl.readLock();
            try {
                currentX = x;
                currentY = y;
            } finally {
                sl.unlockRead(stamp);
            }
        }

        return Math.sqrt(currentX * currentX + currentY * currentY);
    }

    // 쓰기
    public void move(double deltaX, double deltaY) {
        long stamp = sl.writeLock();
        try {
            x += deltaX;
            y += deltaY;
        } finally {
            sl.unlockWrite(stamp);
        }
    }
}

세 가지 모드:

Optimistic Read: 락 없음 (가장 빠름, 검증 필요)
Read Lock: 공유 락 (여러 스레드 가능)
Write Lock: 배타 락 (하나만 가능)

주의사항:

ReentrantLock과 달리 재진입 불가능
쓰기 락 보유 중 읽기 락 획득 불가능 (데드락 주의)

출처: Java Documentation - ReentrantLock, Java Documentation - ReadWriteLock, Java Documentation - StampedLock

5. Atomic 클래스들

5.1 AtomicInteger / AtomicLong

락 없이 원자적 연산을 수행해요.

import java.util.concurrent.atomic.AtomicInteger;

public class AtomicCounter {
    private AtomicInteger count = new AtomicInteger(0);

    public void increment() {
        count.incrementAndGet();  // 원자적 증가
    }

    public int get() {
        return count.get();
    }

    // CAS 사용 예제
    public boolean compareAndSetTo100() {
        int current = count.get();
        return count.compareAndSet(current, 100);
    }
}

내부 구현 (간소화):

public class AtomicInteger {
    private volatile int value;

    public final int incrementAndGet() {
        int current, next;
        do {
            current = value;
            next = current + 1;
        } while (!compareAndSet(current, next));  // CAS 루프
        return next;
    }

    // native 메서드 (하드웨어 CAS 사용)
    public final native boolean compareAndSet(int expect, int update);
}

성능 비교:

// 1. synchronized: 느림 (락 오버헤드)
private int count = 0;
public synchronized void increment() {
    count++;
}

// 2. AtomicInteger: 빠름 (락 프리)
private AtomicInteger count = new AtomicInteger(0);
public void increment() {
    count.incrementAndGet();
}

// 3. LongAdder: 매우 빠름 (경쟁이 심할 때)
private LongAdder count = new LongAdder();
public void increment() {
    count.increment();
}

5.2 LongAdder (Java 8+)

고경쟁 상황에서 AtomicLong보다 빨라요.

import java.util.concurrent.atomic.LongAdder;

public class HighContentionCounter {
    private LongAdder count = new LongAdder();

    // 여러 스레드가 동시에 호출해도 매우 빠름
    public void increment() {
        count.increment();
    }

    // 최종 합계
    public long sum() {
        return count.sum();  // 느림 (모든 셀 합산)
    }
}

동작 원리: ActomicLong

LongAdder

사용 기준:

AtomicLong: 읽기가 많고 경쟁이 적을 때
LongAdder: 쓰기가 많고 경쟁이 심할 때 (sum() 호출 드물어야 함)

5.3 AtomicReference

객체 참조를 원자적으로 업데이트해요.

import java.util.concurrent.atomic.AtomicReference;

public class ImmutableCache {
    private AtomicReference<ImmutableMap<String, String>> cache
        = new AtomicReference<>(ImmutableMap.of());

    public void put(String key, String value) {
        ImmutableMap<String, String> oldCache, newCache;
        do {
            oldCache = cache.get();
            newCache = ImmutableMap.<String, String>builder()
                .putAll(oldCache)
                .put(key, value)
                .build();
        } while (!cache.compareAndSet(oldCache, newCache));
    }

    public String get(String key) {
        return cache.get().get(key);
    }
}

출처: Java Documentation - AtomicInteger, Java Documentation - LongAdder

6. 동시성 유틸리티

6.1 CountDownLatch

여러 스레드가 특정 개수만큼 완료될 때까지 대기해요.

import java.util.concurrent.CountDownLatch;

public class ParallelTaskRunner {
    public void runTasks() throws InterruptedException {
        int numTasks = 5;
        CountDownLatch latch = new CountDownLatch(numTasks);

        // 5개 작업 시작
        for (int i = 0; i < numTasks; i++) {
            new Thread(() -> {
                doWork();
                latch.countDown();  // 카운트 감소
            }).start();
        }

        // 모든 작업이 완료될 때까지 대기
        latch.await();
        System.out.println("All tasks completed!");
    }
}

// 실무 예제: 애플리케이션 시작 대기
public class Application {
    private CountDownLatch startupLatch = new CountDownLatch(3);

    public void start() throws InterruptedException {
        // 3개 서비스 시작
        new Thread(() -> {
            initDatabase();
            startupLatch.countDown();
        }).start();

        new Thread(() -> {
            initCache();
            startupLatch.countDown();
        }).start();

        new Thread(() -> {
            initMessageQueue();
            startupLatch.countDown();
        }).start();

        // 모든 서비스가 준비될 때까지 대기
        startupLatch.await();
        System.out.println("Application ready!");
    }
}

6.2 CyclicBarrier

여러 스레드가 특정 지점에서 만날 때까지 대기 (재사용 가능)

import java.util.concurrent.CyclicBarrier;

public class ParallelMergeSort {
    public void sort(int[] array) throws Exception {
        int numThreads = 4;
        CyclicBarrier barrier = new CyclicBarrier(numThreads, () -> {
            // 모든 스레드가 barrier에 도달하면 실행
            System.out.println("All threads reached barrier!");
        });

        for (int i = 0; i < numThreads; i++) {
            int start = i * (array.length / numThreads);
            int end = (i + 1) * (array.length / numThreads);

            new Thread(() -> {
                sortPartition(array, start, end);
                try {
                    barrier.await();  // 다른 스레드 대기
                } catch (Exception e) {
                    e.printStackTrace();
                }
            }).start();
        }
    }
}

CountDownLatch vs CyclicBarrier:

CountDownLatch: 한 번만 사용, 작업 완료 대기
CyclicBarrier: 재사용 가능, 스레드 동기화 지점

6.3 Semaphore

제한된 자원에 대한 접근 제어

import java.util.concurrent.Semaphore;

// 커넥션 풀
public class ConnectionPool {
    private Semaphore semaphore;
    private List<Connection> connections;

    public ConnectionPool(int size) {
        this.semaphore = new Semaphore(size);
        this.connections = new ArrayList<>(size);
        for (int i = 0; i < size; i++) {
            connections.add(createConnection());
        }
    }

    public Connection acquire() throws InterruptedException {
        semaphore.acquire();  // 허가 획득 (없으면 대기)
        return getConnection();
    }

    public void release(Connection conn) {
        returnConnection(conn);
        semaphore.release();  // 허가 반환
    }
}

// API Rate Limiter
public class RateLimiter {
    private Semaphore semaphore;

    public RateLimiter(int requestsPerSecond) {
        this.semaphore = new Semaphore(requestsPerSecond);

        // 1초마다 허가 리필
        ScheduledExecutorService scheduler = Executors.newScheduledThreadPool(1);
        scheduler.scheduleAtFixedRate(() -> {
            semaphore.release(requestsPerSecond - semaphore.availablePermits());
        }, 1, 1, TimeUnit.SECONDS);
    }

    public boolean tryAcquire() {
        return semaphore.tryAcquire();
    }
}

출처: Java Documentation - CountDownLatch, Java Documentation - CyclicBarrier, Java Documentation - Semaphore

정리

Java 동시성 도구 선택 가이드:

컬렉션:

HashMap 대체 → ConcurrentHashMap
ArrayList 대체 (읽기 많음) → CopyOnWriteArrayList
작업 큐 → BlockingQueue (ArrayBlocking, LinkedBlocking, Priority)

락:

기본 → synchronized
tryLock, 타임아웃 필요 → ReentrantLock
읽기 많고 쓰기 적음 → ReadWriteLock
읽기 매우 많음 → StampedLock (낙관적 읽기)

Atomic:

단순 카운터 → AtomicInteger, AtomicLong
고경쟁 카운터 → LongAdder
객체 참조 → AtomicReference

동기화:

작업 완료 대기 → CountDownLatch
스레드 동기화 지점 → CyclicBarrier
자원 제한 → Semaphore

참고 자료

Introduction

Using plain collections (ArrayList, HashMap) in a multithreaded environment leads to race conditions. Vector and Hashtable, which wrap every method with synchronized, suffer from poor performance.

Java provides high-performance concurrent collections in the java.util.concurrent package. This article covers the concurrent data structures and lock mechanisms commonly used in practice.

Source: Java Documentation - Concurrent Collections

1. ConcurrentHashMap

1.1 Problem: Concurrency Issues with HashMap

// Regular HashMap: not safe in a multithreaded environment
Map<String, Integer> map = new HashMap<>();

// Thread 1
map.put("count", 1);

// Thread 2 (concurrent execution)
map.put("count", 2);  // Race Condition!

// In the worst case, the internal structure can break, causing an infinite loop (before Java 7)

Problems with Hashtable:

// Hashtable: every method is synchronized
Hashtable<String, Integer> table = new Hashtable<>();

// Problem: every operation locks the entire table
table.put("key1", 1);  // Full lock
table.get("key2");     // Even reads require a full lock (inefficient!)

1.2 ConcurrentHashMap’s Solution: Segment Locking (Before Java 8)

Until Java 7, segment locking was used.

// Conceptual structure (not actual code)
class ConcurrentHashMap<K, V> {
    Segment<K, V>[] segments = new Segment[16];  // 16 segments by default

    static class Segment<K, V> {
        ReentrantLock lock = new ReentrantLock();
        HashEntry<K, V>[] table;
    }
}

How it works:

Example:

1.3 ConcurrentHashMap Improvement: CAS-Based (Java 8+)

Starting from Java 8, segments were removed and CAS (Compare-And-Swap) + synchronized is used instead.

// Simplified put structure
public V put(K key, V value) {
    int hash = hash(key);

    for (Node<K,V>[] tab = table;;) {
        Node<K,V> f = tabAt(tab, i);  // volatile read

        if (f == null) {
            // Empty bucket: atomic insertion via CAS
            if (casTabAt(tab, i, null, new Node<K,V>(hash, key, value))) {
                break;  // Success!
            }
        } else {
            // Collision: synchronized on this bucket only
            synchronized (f) {
                // Lock only within the bucket (very fine-grained lock)
                // Insert into chain or tree
            }
        }
    }
}

Key ideas:

Empty bucket: Insert without lock using CAS (fast)
Collision bucket: Lock only the bucket’s head node (finer than segment locking)
Reads: Performed without lock using volatile read

Performance comparison:

// Hashtable: full lock (worst)
synchronized (전체 테이블) {
    // 모든 연산
}

// ConcurrentHashMap (Java 7): segment lock (middle)
synchronized (segments[i]) {
    // 해당 세그먼트만
}

// ConcurrentHashMap (Java 8+): bucket lock (best)
synchronized (bucket[i]) {
    // 해당 버킷만
}

1.4 Practical Usage Examples

import java.util.concurrent.ConcurrentHashMap;

// User session management
public class SessionManager {
    private ConcurrentHashMap<String, UserSession> sessions = new ConcurrentHashMap<>();

    public void createSession(String sessionId, UserSession session) {
        // putIfAbsent: insert only if absent (atomic)
        UserSession existing = sessions.putIfAbsent(sessionId, session);
        if (existing != null) {
            throw new IllegalStateException("Session already exists");
        }
    }

    public void updateLastAccessTime(String sessionId) {
        // compute: atomically compute the value for a key
        sessions.computeIfPresent(sessionId, (id, session) -> {
            session.setLastAccessTime(System.currentTimeMillis());
            return session;
        });
    }

    public void removeExpiredSessions() {
        long now = System.currentTimeMillis();
        long timeout = 30 * 60 * 1000;  // 30 minutes

        // Thread-safe iteration with removal
        sessions.entrySet().removeIf(entry ->
            now - entry.getValue().getLastAccessTime() > timeout
        );
    }
}

// Counter (atomic increment)
public class UrlClickCounter {
    private ConcurrentHashMap<String, Long> clicks = new ConcurrentHashMap<>();

    public void recordClick(String url) {
        // merge: apply function if key exists, insert initial value if absent
        clicks.merge(url, 1L, (oldValue, one) -> oldValue + 1);

        // Or use compute
        // clicks.compute(url, (k, v) -> (v == null) ? 1L : v + 1);
    }

    public long getClicks(String url) {
        return clicks.getOrDefault(url, 0L);
    }
}

Caveats:

ConcurrentHashMap<String, List<String>> map = new ConcurrentHashMap<>();

// Wrong code: race condition!
if (!map.containsKey("users")) {
    map.put("users", new ArrayList<>());  // Another thread can intervene in between
}
map.get("users").add("Alice");

// Correct code: use atomic operation
map.computeIfAbsent("users", k -> new ArrayList<>()).add("Alice");

Sources: Java Documentation - ConcurrentHashMap, Baeldung - Guide to ConcurrentHashMap

2. CopyOnWriteArrayList

2.1 Concept: Copy on Write

Used when reads vastly outnumber writes.

import java.util.concurrent.CopyOnWriteArrayList;

public class CopyOnWriteArrayList<E> {
    private volatile Object[] array;

    public boolean add(E e) {
        synchronized (lock) {
            Object[] oldArray = array;
            int len = oldArray.length;

            // Copy the entire array (expensive!)
            Object[] newArray = Arrays.copyOf(oldArray, len + 1);
            newArray[len] = e;

            // Replace the array via volatile write
            array = newArray;
        }
        return true;
    }

    public E get(int index) {
        // No lock! Only volatile read
        return (E) array[index];
    }
}

How it works:

2.2 Use Cases

// Event listener management (typical use case)
public class EventBus {
    private CopyOnWriteArrayList<EventListener> listeners = new CopyOnWriteArrayList<>();

    // Listener registration: occurs rarely
    public void addListener(EventListener listener) {
        listeners.add(listener);  // Array copy occurs
    }

    // Event publishing: occurs very frequently
    public void publishEvent(Event event) {
        // Fast iteration without lock
        for (EventListener listener : listeners) {
            listener.onEvent(event);
        }
    }
}

// Configuration management
public class ConfigurationManager {
    private CopyOnWriteArrayList<String> allowedIps = new CopyOnWriteArrayList<>();

    // Configuration change: occurs rarely (manual admin operation)
    public void addAllowedIp(String ip) {
        allowedIps.add(ip);
    }

    // IP validation: occurs on every request (very frequent)
    public boolean isAllowed(String ip) {
        return allowedIps.contains(ip);  // Fast check without lock
    }
}

2.3 Pros and Cons

Pros:

Reads are completely lock-free (very fast)
Iterator never throws ConcurrentModificationException
Snapshot isolation (guarantees a consistent view during reads)

Cons:

Full array copy on every write (slow, increased memory usage)
Memory overhead (two versions briefly coexist)
Performance degrades sharply with frequent writes

Usage criteria:

Sources: Java Documentation - CopyOnWriteArrayList, Baeldung - CopyOnWriteArrayList Guide

3. BlockingQueue

3.1 Concept: Producer-Consumer Pattern

BlockingQueue provides the ability to wait when the queue is empty and wait when the queue is full.

import java.util.concurrent.BlockingQueue;
import java.util.concurrent.ArrayBlockingQueue;

public class ProducerConsumer {
    private BlockingQueue<Task> queue = new ArrayBlockingQueue<>(10);

    // Producer thread
    public void producer() throws InterruptedException {
        Task task = createTask();
        queue.put(task);  // Blocks if the queue is full
    }

    // Consumer thread
    public void consumer() throws InterruptedException {
        Task task = queue.take();  // Blocks if the queue is empty
        process(task);
    }
}

Internal structure (simplified):

public class ArrayBlockingQueue<E> {
    private final Object[] items;
    private int count;  // Current number of elements

    private final Lock lock = new ReentrantLock();
    private final Condition notEmpty = lock.newCondition();
    private final Condition notFull = lock.newCondition();

    public void put(E e) throws InterruptedException {
        lock.lock();
        try {
            while (count == items.length) {
                notFull.await();  // Full: wait
            }
            enqueue(e);
            notEmpty.signal();  // Wake up consumer
        } finally {
            lock.unlock();
        }
    }

    public E take() throws InterruptedException {
        lock.lock();
        try {
            while (count == 0) {
                notEmpty.await();  // Empty: wait
            }
            E item = dequeue();
            notFull.signal();  // Wake up producer
            return item;
        } finally {
            lock.unlock();
        }
    }
}

3.2 BlockingQueue Implementations

3.2.1 ArrayBlockingQueue

Characteristics: Fixed-size array-based

BlockingQueue<Task> queue = new ArrayBlockingQueue<>(100);  // Max 100

// Blocks when capacity is exceeded
queue.put(task);  // Block
queue.offer(task, 1, TimeUnit.SECONDS);  // 1-second timeout

Use case: When backpressure control is needed

// API request throttling
public class RateLimitedApiHandler {
    private BlockingQueue<ApiRequest> queue = new ArrayBlockingQueue<>(1000);

    public boolean handleRequest(ApiRequest request) {
        // Reject if queue is full (prevent server overload)
        return queue.offer(request);
    }
}

3.2.2 LinkedBlockingQueue

Characteristics: Linked-node based, optionally bounded (unbounded by default)

BlockingQueue<Task> unbounded = new LinkedBlockingQueue<>();  // Unbounded
BlockingQueue<Task> bounded = new LinkedBlockingQueue<>(100);  // Limited to 100

Internal optimization:

// Two locks for better performance
class LinkedBlockingQueue<E> {
    private final ReentrantLock takeLock = new ReentrantLock();
    private final ReentrantLock putLock = new ReentrantLock();

    // put and take can execute concurrently!
}

Use case: Default work queue for ThreadPoolExecutor

3.2.3 PriorityBlockingQueue

Characteristics: Priority-based (heap structure)

// Priority task queue
BlockingQueue<Task> queue = new PriorityBlockingQueue<>(10,
    (t1, t2) -> Integer.compare(t2.priority, t1.priority)  // Higher priority first
);

queue.put(new Task("Low", 1));
queue.put(new Task("High", 10));
queue.put(new Task("Medium", 5));

queue.take();  // "High" (priority 10)
queue.take();  // "Medium" (priority 5)
queue.take();  // "Low" (priority 1)

3.2.4 SynchronousQueue

Characteristics: Zero-capacity queue (direct handoff)

BlockingQueue<Task> queue = new SynchronousQueue<>();

// Producer
queue.put(task);  // Blocks until a consumer calls take()

// Consumer
Task task = queue.take();  // Blocks until a producer calls put()

Use case: Cached ThreadPool

// Internal structure of Executors.newCachedThreadPool()
ThreadPoolExecutor executor = new ThreadPoolExecutor(
    0, Integer.MAX_VALUE,
    60L, TimeUnit.SECONDS,
    new SynchronousQueue<>()  // Hands off tasks directly to threads
);

3.3 Practical Example: Asynchronous Log Processing

import java.util.concurrent.*;

public class AsyncLogger {
    private BlockingQueue<LogMessage> queue = new LinkedBlockingQueue<>(10000);
    private volatile boolean running = true;

    // Start logger
    public void start() {
        Thread writerThread = new Thread(() -> {
            while (running || !queue.isEmpty()) {
                try {
                    LogMessage msg = queue.poll(1, TimeUnit.SECONDS);
                    if (msg != null) {
                        writeToFile(msg);  // I/O operation
                    }
                } catch (InterruptedException e) {
                    Thread.currentThread().interrupt();
                    break;
                }
            }
        });
        writerThread.setDaemon(false);
        writerThread.start();
    }

    // Log message (non-blocking)
    public void log(String message) {
        LogMessage msg = new LogMessage(message, System.currentTimeMillis());

        // offer: returns false if queue is full (does not block)
        if (!queue.offer(msg)) {
            System.err.println("Log queue full, dropping message: " + message);
        }
    }

    // Shutdown
    public void shutdown() {
        running = false;
    }

    private void writeToFile(LogMessage msg) {
        // Write to file (slow I/O)
    }
}

Sources: Java Documentation - BlockingQueue, Baeldung - Guide to BlockingQueue

4. Advanced Lock Mechanisms

4.1 ReentrantLock

A more flexible lock than synchronized.

import java.util.concurrent.locks.ReentrantLock;

public class Counter {
    private final ReentrantLock lock = new ReentrantLock();
    private int count = 0;

    // Basic usage
    public void increment() {
        lock.lock();
        try {
            count++;
        } finally {
            lock.unlock();  // Must release in finally
        }
    }

    // tryLock: prevent deadlock
    public boolean tryIncrement() {
        if (lock.tryLock()) {
            try {
                count++;
                return true;
            } finally {
                lock.unlock();
            }
        }
        return false;  // Failed to acquire lock
    }

    // Timeout
    public boolean incrementWithTimeout() throws InterruptedException {
        if (lock.tryLock(1, TimeUnit.SECONDS)) {
            try {
                count++;
                return true;
            } finally {
                lock.unlock();
            }
        }
        return false;  // Could not acquire lock within 1 second
    }

    // Interruptible
    public void incrementInterruptibly() throws InterruptedException {
        lock.lockInterruptibly();  // Can be interrupted
        try {
            count++;
        } finally {
            lock.unlock();
        }
    }
}

synchronized vs ReentrantLock:

Feature	synchronized	ReentrantLock
Automatic release	Yes (auto on exception)	No (finally required)
tryLock	No	Yes
Timeout	No	Yes
Interruptible	No	Yes
Fairness	No	Yes
Condition variables	No	Yes (multiple)

Fair Lock:

ReentrantLock fairLock = new ReentrantLock(true);  // Guarantees FIFO order

// Acquires lock in waiting order
// Downside: performance degradation (increased context switching)

4.2 ReadWriteLock

Reads are concurrent, writes are exclusive.

import java.util.concurrent.locks.ReadWriteLock;
import java.util.concurrent.locks.ReentrantReadWriteLock;

public class CachedData {
    private final ReadWriteLock rwLock = new ReentrantReadWriteLock();
    private Map<String, String> cache = new HashMap<>();

    // Read: multiple threads can execute concurrently
    public String get(String key) {
        rwLock.readLock().lock();
        try {
            return cache.get(key);
        } finally {
            rwLock.readLock().unlock();
        }
    }

    // Write: exclusive execution
    public void put(String key, String value) {
        rwLock.writeLock().lock();
        try {
            cache.put(key, value);
        } finally {
            rwLock.writeLock().unlock();
        }
    }
}

How it works:

While holding a read lock:
  - Other read locks can be acquired
  - Write locks cannot be acquired

While holding a write lock:
  - Other read locks cannot be acquired
  - Other write locks cannot be acquired

Practical example: Cache implementation

public class Cache<K, V> {
    private final ReadWriteLock rwLock = new ReentrantReadWriteLock();
    private final Map<K, V> cache = new HashMap<>();

    public V get(K key) {
        rwLock.readLock().lock();
        try {
            V value = cache.get(key);
            if (value != null) {
                return value;
            }
        } finally {
            rwLock.readLock().unlock();
        }

        // Cache miss: upgrade to write lock
        rwLock.writeLock().lock();
        try {
            // Double-check (another thread may have already loaded it)
            V value = cache.get(key);
            if (value == null) {
                value = loadFromDatabase(key);
                cache.put(key, value);
            }
            return value;
        } finally {
            rwLock.writeLock().unlock();
        }
    }

    public void invalidate(K key) {
        rwLock.writeLock().lock();
        try {
            cache.remove(key);
        } finally {
            rwLock.writeLock().unlock();
        }
    }
}

4.3 StampedLock (Java 8+)

Provides faster optimistic reads than ReadWriteLock.

import java.util.concurrent.locks.StampedLock;

public class Point {
    private final StampedLock sl = new StampedLock();
    private double x, y;

    // Optimistic read: fastest
    public double distanceFromOrigin() {
        long stamp = sl.tryOptimisticRead();  // Acquire stamp without lock
        double currentX = x;
        double currentY = y;

        if (!sl.validate(stamp)) {
            // A write occurred: retry with read lock
            stamp = sl.readLock();
            try {
                currentX = x;
                currentY = y;
            } finally {
                sl.unlockRead(stamp);
            }
        }

        return Math.sqrt(currentX * currentX + currentY * currentY);
    }

    // Write
    public void move(double deltaX, double deltaY) {
        long stamp = sl.writeLock();
        try {
            x += deltaX;
            y += deltaY;
        } finally {
            sl.unlockWrite(stamp);
        }
    }
}

Three modes:

Optimistic Read: No lock (fastest, requires validation)
Read Lock: Shared lock (multiple threads allowed)
Write Lock: Exclusive lock (only one allowed)

Caveats:

Unlike ReentrantLock, not reentrant
Cannot acquire a read lock while holding a write lock (beware of deadlocks)

Sources: Java Documentation - ReentrantLock, Java Documentation - ReadWriteLock, Java Documentation - StampedLock

5. Atomic Classes

5.1 AtomicInteger / AtomicLong

Perform atomic operations without locks.

import java.util.concurrent.atomic.AtomicInteger;

public class AtomicCounter {
    private AtomicInteger count = new AtomicInteger(0);

    public void increment() {
        count.incrementAndGet();  // Atomic increment
    }

    public int get() {
        return count.get();
    }

    // CAS usage example
    public boolean compareAndSetTo100() {
        int current = count.get();
        return count.compareAndSet(current, 100);
    }
}

Internal implementation (simplified):

public class AtomicInteger {
    private volatile int value;

    public final int incrementAndGet() {
        int current, next;
        do {
            current = value;
            next = current + 1;
        } while (!compareAndSet(current, next));  // CAS loop
        return next;
    }

    // Native method (uses hardware CAS)
    public final native boolean compareAndSet(int expect, int update);
}

Performance comparison:

// 1. synchronized: slow (lock overhead)
private int count = 0;
public synchronized void increment() {
    count++;
}

// 2. AtomicInteger: fast (lock-free)
private AtomicInteger count = new AtomicInteger(0);
public void increment() {
    count.incrementAndGet();
}

// 3. LongAdder: very fast (under high contention)
private LongAdder count = new LongAdder();
public void increment() {
    count.increment();
}

5.2 LongAdder (Java 8+)

Faster than AtomicLong under high contention.

import java.util.concurrent.atomic.LongAdder;

public class HighContentionCounter {
    private LongAdder count = new LongAdder();

    // Very fast even with many threads calling simultaneously
    public void increment() {
        count.increment();
    }

    // Final sum
    public long sum() {
        return count.sum();  // Slow (sums all cells)
    }
}

How it works: AtomicLong

LongAdder

Usage criteria:

AtomicLong: When reads are frequent and contention is low
LongAdder: When writes are frequent and contention is high (sum() should be called rarely)

5.3 AtomicReference

Atomically updates object references.

import java.util.concurrent.atomic.AtomicReference;

public class ImmutableCache {
    private AtomicReference<ImmutableMap<String, String>> cache
        = new AtomicReference<>(ImmutableMap.of());

    public void put(String key, String value) {
        ImmutableMap<String, String> oldCache, newCache;
        do {
            oldCache = cache.get();
            newCache = ImmutableMap.<String, String>builder()
                .putAll(oldCache)
                .put(key, value)
                .build();
        } while (!cache.compareAndSet(oldCache, newCache));
    }

    public String get(String key) {
        return cache.get().get(key);
    }
}

Sources: Java Documentation - AtomicInteger, Java Documentation - LongAdder

6. Concurrency Utilities

6.1 CountDownLatch

Waits until a specified number of threads complete.

import java.util.concurrent.CountDownLatch;

public class ParallelTaskRunner {
    public void runTasks() throws InterruptedException {
        int numTasks = 5;
        CountDownLatch latch = new CountDownLatch(numTasks);

        // Start 5 tasks
        for (int i = 0; i < numTasks; i++) {
            new Thread(() -> {
                doWork();
                latch.countDown();  // Decrement count
            }).start();
        }

        // Wait until all tasks complete
        latch.await();
        System.out.println("All tasks completed!");
    }
}

// Practical example: waiting for application startup
public class Application {
    private CountDownLatch startupLatch = new CountDownLatch(3);

    public void start() throws InterruptedException {
        // Start 3 services
        new Thread(() -> {
            initDatabase();
            startupLatch.countDown();
        }).start();

        new Thread(() -> {
            initCache();
            startupLatch.countDown();
        }).start();

        new Thread(() -> {
            initMessageQueue();
            startupLatch.countDown();
        }).start();

        // Wait until all services are ready
        startupLatch.await();
        System.out.println("Application ready!");
    }
}

6.2 CyclicBarrier

Waits until multiple threads all reach a specific point (reusable).

import java.util.concurrent.CyclicBarrier;

public class ParallelMergeSort {
    public void sort(int[] array) throws Exception {
        int numThreads = 4;
        CyclicBarrier barrier = new CyclicBarrier(numThreads, () -> {
            // Executed when all threads reach the barrier
            System.out.println("All threads reached barrier!");
        });

        for (int i = 0; i < numThreads; i++) {
            int start = i * (array.length / numThreads);
            int end = (i + 1) * (array.length / numThreads);

            new Thread(() -> {
                sortPartition(array, start, end);
                try {
                    barrier.await();  // Wait for other threads
                } catch (Exception e) {
                    e.printStackTrace();
                }
            }).start();
        }
    }
}

CountDownLatch vs CyclicBarrier:

CountDownLatch: Single-use, waits for task completion
CyclicBarrier: Reusable, thread synchronization point

6.3 Semaphore

Controls access to limited resources.

import java.util.concurrent.Semaphore;

// Connection pool
public class ConnectionPool {
    private Semaphore semaphore;
    private List<Connection> connections;

    public ConnectionPool(int size) {
        this.semaphore = new Semaphore(size);
        this.connections = new ArrayList<>(size);
        for (int i = 0; i < size; i++) {
            connections.add(createConnection());
        }
    }

    public Connection acquire() throws InterruptedException {
        semaphore.acquire();  // Acquire permit (wait if none available)
        return getConnection();
    }

    public void release(Connection conn) {
        returnConnection(conn);
        semaphore.release();  // Return permit
    }
}

// API Rate Limiter
public class RateLimiter {
    private Semaphore semaphore;

    public RateLimiter(int requestsPerSecond) {
        this.semaphore = new Semaphore(requestsPerSecond);

        // Refill permits every second
        ScheduledExecutorService scheduler = Executors.newScheduledThreadPool(1);
        scheduler.scheduleAtFixedRate(() -> {
            semaphore.release(requestsPerSecond - semaphore.availablePermits());
        }, 1, 1, TimeUnit.SECONDS);
    }

    public boolean tryAcquire() {
        return semaphore.tryAcquire();
    }
}

Sources: Java Documentation - CountDownLatch, Java Documentation - CyclicBarrier, Java Documentation - Semaphore

Summary

Java concurrency tool selection guide:

Collections:

HashMap replacement -> ConcurrentHashMap
ArrayList replacement (read-heavy) -> CopyOnWriteArrayList
Work queue -> BlockingQueue (ArrayBlocking, LinkedBlocking, Priority)

Locks:

Default -> synchronized
Need tryLock, timeout -> ReentrantLock
Read-heavy, write-light -> ReadWriteLock
Very read-heavy -> StampedLock (optimistic read)

Atomic:

Simple counter -> AtomicInteger, AtomicLong
High-contention counter -> LongAdder
Object reference -> AtomicReference

Synchronization:

Wait for task completion -> CountDownLatch
Thread synchronization point -> CyclicBarrier
Resource limiting -> Semaphore

References

Official Documentation

Java Documentation - java.util.concurrent Package - Concurrent package overview
Java Documentation - ConcurrentHashMap - ConcurrentHashMap API
Java Documentation - CopyOnWriteArrayList - CopyOnWriteArrayList API
Java Documentation - BlockingQueue - BlockingQueue interface
Java Documentation - ReentrantLock - ReentrantLock API
Java Documentation - ReadWriteLock - ReadWriteLock interface
Java Documentation - StampedLock - StampedLock API (Java 8+)
Java Documentation - AtomicInteger - AtomicInteger API
Java Documentation - LongAdder - LongAdder API (Java 8+)

Theory and Concepts

Wikipedia - Concurrent data structure - Concurrent data structure overview
Wikipedia - Compare-and-swap - CAS algorithm
Wikipedia - Lock-free and wait-free algorithms - Lock-free algorithms

Technical Resources

Baeldung - Guide to ConcurrentHashMap - Detailed ConcurrentHashMap guide
Baeldung - CopyOnWriteArrayList Guide - CopyOnWriteArrayList usage
Baeldung - Guide to BlockingQueue - BlockingQueue implementation comparison
Baeldung - Guide to java.util.concurrent.Locks - Java Lock interface
Baeldung - Guide to CountDownLatch - CountDownLatch usage
GeeksforGeeks - ConcurrentHashMap in Java - ConcurrentHashMap basics
GeeksforGeeks - BlockingQueue Interface in Java - BlockingQueue concepts

Performance and Internals

DZone - How ConcurrentHashMap Works Internally in Java - ConcurrentHashMap internal workings
Java Concurrency in Practice - Brian Goetz’s Java concurrency bible

들어가며

1. ConcurrentHashMap

1.1 문제: HashMap의 동시성 이슈

1.2 ConcurrentHashMap의 해결책: 세그먼트 락 (Java 8 이전)

1.3 ConcurrentHashMap의 개선: CAS 기반 (Java 8 이후)

1.4 실무 사용 예제

2. CopyOnWriteArrayList

2.1 개념: 쓰기 시 복사

2.2 사용 사례

2.3 장단점

3. BlockingQueue

3.1 개념: Producer-Consumer 패턴

3.2 BlockingQueue 구현체들

3.2.1 ArrayBlockingQueue

3.2.2 LinkedBlockingQueue

3.2.3 PriorityBlockingQueue

3.2.4 SynchronousQueue

3.3 실무 예제: 비동기 로그 처리

4. 고급 락 메커니즘

4.1 ReentrantLock

4.2 ReadWriteLock

4.3 StampedLock (Java 8+)

5. Atomic 클래스들

5.1 AtomicInteger / AtomicLong

5.2 LongAdder (Java 8+)

5.3 AtomicReference

6. 동시성 유틸리티

6.1 CountDownLatch

6.2 CyclicBarrier

6.3 Semaphore

정리

참고 자료

공식 문서

이론 및 개념

기술 자료

성능 및 내부 구조

한국 기술 블로그

Introduction

1. ConcurrentHashMap

1.1 Problem: Concurrency Issues with HashMap

1.2 ConcurrentHashMap’s Solution: Segment Locking (Before Java 8)

1.3 ConcurrentHashMap Improvement: CAS-Based (Java 8+)

1.4 Practical Usage Examples

2. CopyOnWriteArrayList

2.1 Concept: Copy on Write

2.2 Use Cases

2.3 Pros and Cons

3. BlockingQueue

3.1 Concept: Producer-Consumer Pattern

3.2 BlockingQueue Implementations

3.2.1 ArrayBlockingQueue

3.2.2 LinkedBlockingQueue

3.2.3 PriorityBlockingQueue

3.2.4 SynchronousQueue

3.3 Practical Example: Asynchronous Log Processing

4. Advanced Lock Mechanisms

4.1 ReentrantLock

4.2 ReadWriteLock

4.3 StampedLock (Java 8+)

5. Atomic Classes

5.1 AtomicInteger / AtomicLong

5.2 LongAdder (Java 8+)

5.3 AtomicReference

6. Concurrency Utilities

6.1 CountDownLatch

6.2 CyclicBarrier

6.3 Semaphore

Summary

References

Official Documentation

Theory and Concepts

Technical Resources

Performance and Internals

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