I was building a system that processes customer orders when I hit a wall. My application ran perfectly on my laptop—a single instance handling everything. Then we deployed it to the cloud, with multiple containers running to handle the load. Suddenly, the same $100 discount was being applied twice to an order, and our inventory count went negative. The problem wasn’t the code; it was the environment. In a world of many application instances, how do you ensure something only happens once? This question led me straight to the need for distributed locks. If you’re moving from a monolith to microservices or scaling horizontally, understanding this tool is essential.
Think about a hot product drop. Thousands of users click “Buy Now” at the same millisecond. Your load balancer sends these requests to different application servers. Without coordination, each server checks the database, sees one item left, and allows the purchase. You’ve just sold one physical item a hundred times. A distributed lock is the coordinator that prevents this chaos. It creates a global agreement, across all your servers, about who gets to perform a critical task.
This is where Redisson shines. It’s a Java library that uses Redis, a lightning-fast in-memory store, to provide these coordination primitives. Why Redis? It’s single-threaded for command execution, which makes it a perfect, impartial judge for our lock disputes. Redisson wraps this power into a simple API that feels familiar if you’ve used standard Java locks.
Let’s get our hands dirty with code. First, you’ll need to add Redisson to your Spring Boot project. I prefer using their Spring Boot starter for a smooth setup.
<dependency>
<groupId>org.redisson</groupId>
<artifactId>redisson-spring-boot-starter</artifactId>
<version>3.25.2</version>
</dependency>Next, a minimal configuration in your application.yml points to your Redis instance.
spring:
redis:
redisson:
config: |
singleServerConfig:
address: "redis://localhost:6379"
lockWatchdogTimeout: 30000That lockWatchdogTimeout is a safety net. It’s a thread that automatically renews your lock if your operation is taking longer than expected, preventing accidental expiration mid-task.
Now, for the fun part. Imagine a service that allocates a unique promotional code from a limited pool. Here’s how you’d protect it with a Redisson lock.
@Service
public class CodeService {
@Autowired
private RedissonClient redissonClient;
public String assignPromoCode(String userId) {
String lockKey = "lock:promo_codes";
RLock lock = redissonClient.getLock(lockKey);
try {
// Try to get the lock, wait up to 5 seconds, and hold it for 30 sec
boolean isLocked = lock.tryLock(5, 30, TimeUnit.SECONDS);
if (!isLocked) {
throw new RuntimeException("Could not acquire lock. Try again.");
}
// Critical section: Only one thread in the cluster executes this
String code = getNextAvailableCodeFromDatabase();
associateCodeWithUser(userId, code);
return code;
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
throw new RuntimeException("Lock acquisition interrupted", e);
} finally {
if (lock.isHeldByCurrentThread()) {
lock.unlock();
}
}
}
}See the pattern? You get a lock object using a unique key. You try to acquire it, do your sensitive work, and then always—always—release it in a finally block. Forgetting that final step is a recipe for a system-wide deadlock. Have you ever had a task hang forever, only to find a forgotten lock was the culprit?
But what if many threads need to read data and only a few need to update it? Using a basic exclusive lock would create a bottleneck. This is where Redisson’s read-write lock comes in. It allows multiple “read locks” to be held simultaneously but requires exclusive access for a “write lock.”
public void updateProductInventory(String productId, int quantityChange) {
String rwLockKey = "rwlock:product:" + productId;
RReadWriteLock rwLock = redissonClient.getReadWriteLock(rwLockKey);
RLock writeLock = rwLock.writeLock();
try {
if (writeLock.tryLock(2, 10, TimeUnit.SECONDS)) {
// Read current inventory
int currentStock = productRepository.getStock(productId);
// Update it
productRepository.updateStock(productId, currentStock + quantityChange);
}
} finally {
writeLock.unlock();
}
}This is far more efficient. Multiple services can check the inventory (using a readLock) at the same time without blocking each other. The update is still safe and exclusive. It mirrors how we think about data access in our heads, doesn’t it?
Distributed locks are powerful, but they are not a golden hammer. They add latency (every lock check is a network call to Redis) and complexity. Before using one, ask yourself: Can I solve this with a database transaction using a SELECT FOR UPDATE? Can I design my system to be idempotent, so duplicate processing doesn’t cause harm? Often, a simpler solution exists. Use locks when you absolutely need strong, cross-server coordination for a critical resource.
My journey with these tools taught me that the hardest bugs are often about timing—things that work 99 times out of 100. Distributed locks provide the certainty needed for those last, critical moments. They are a fundamental piece for building robust systems that scale.
I hope walking through my experience and these examples helps you tackle your own concurrency challenges. What’s the trickiest race condition you’ve faced in a distributed system? Share your stories in the comments below—I learn just as much from your experiences. If this guide was useful, please like and share it with another developer who might be wrestling with these same concepts.
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