Handling Concurrent API Calls in Spring Boot

When building Spring Boot applications, handling concurrent API calls efficiently is crucial to ensure optimal performance and scalability. Here are a few approaches to manage concurrent read and write operations:

Handling Concurrent Read API Calls

Asynchronous Methods

Using @Async at @Service annotation and enabling asynchronous processing can help handle multiple API calls concurrently.

@Async
public CompletableFuture<String> asyncMethod() {
    // Call external API
    return CompletableFuture.completedFuture("Result");
}

WebClient with Reactor

Spring WebFlux’s WebClient allows for reactive programming, making it easier to handle multiple API calls.

Mono<String> firstApiCall = webClient.get().uri("http://example.com/api1").retrieve().bodyToMono(String.class);
Mono<String> secondApiCall = webClient.get().uri("http://example.com/api2").retrieve().bodyToMono(String.class);

return Mono.zip(firstApiCall, secondApiCall)
        .map(tuple -> tuple.getT1() + " " + tuple.getT2());

ExecutorService

Using ExecutorService allows for creating a pool of threads to manage concurrent calls.

private final ExecutorService executorService = Executors.newFixedThreadPool(10);

public CompletableFuture<String> makeConcurrentCall() {
    return CompletableFuture.supplyAsync(() -> "Result", executorService);
}

Parallel Streams

Java 8’s parallel streams can perform API calls in parallel.

return Stream.of("http://example.com/api1", "http://example.com/api2")
    .parallel()
    .map(url -> "Result from " + url)
    .collect(Collectors.toList());

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Handling Concurrent Write API Calls

Handling concurrent write operations, such as updates, requires careful management to ensure data consistency. Here are a few strategies:

Synchronized Blocks

Using synchronized blocks ensures that only one thread can execute a block of code at a time.

public synchronized String updateData(String url, Object data) {
    return webClient.post().uri(url).bodyValue(data).retrieve().bodyToMono(String.class).block();
}

Optimistic Locking

Optimistic locking uses versioning to handle concurrent updates, ensuring data consistency.

class Item {
    // Other fields
    @Version
    private Long version;
}
public Item updateItem(Item newItem) {
    return repository.save(newItem);
}

@Transactional with Retry

The @Transactional annotation combined with retry mechanisms can handle transaction conflicts. This is also used in @Service layer.

@Transactional
@Retryable(value = {OptimisticLockingFailureException.class}, maxAttempts = 3, backoff = @Backoff(delay = 500))
public Item updateItem(Item newItem) {
    return repository.save(newItem);
}

Distributed Locks

Using distributed locks like Redis ensures that only one service instance can perform the update at a time.

RLock lock = redissonClient.getLock("updateLock");
lock.lock();
try {
    // Perform the write call
} finally {
    lock.unlock();
}

By employing these techniques, concurrent API calls within a Spring Boot application can be efficiently managed, thus ensuring optimal performance and maintaining data integrity.