Handling Concurrent Data Structures in Java: Safely Adding Elements to ConcurrentLinkedDeque

In multi-threaded Java applications, handling shared data structures safely is crucial to maintaining data integrity and performance. One such use case involves maintaining a ConcurrentMap<String, ConcurrentLinkedDeque> structure within a singleton bean and adding elements to its queues without breaking concurrency. This article explores an efficient and thread-safe way to handle this scenario using Java’s concurrent utilities.

Understanding the Data Structure

Why Use ConcurrentMap and ConcurrentLinkedDeque?

• ConcurrentMap<String, ConcurrentLinkedDeque> is a thread-safe combination where multiple threads can concurrently access, update, and modify elements without explicit synchronization.
• ConcurrentLinkedDeque is a non-blocking, lock-free, thread-safe double-ended queue, making it ideal for high-performance applications that require frequent insertions and removals.

Implementing a Singleton Bean to Hold the Map

In a Spring-based application, a singleton bean ensures that only one instance of the data structure exists across the application. Below is an implementation using Spring’s @Component annotation.

import org.springframework.context.annotation.Scope;
import org.springframework.stereotype.Component;

import javax.annotation.PostConstruct;
import com.google.gson.JsonObject;
import java.util.concurrent.ConcurrentLinkedDeque;
import java.util.concurrent.ConcurrentMap;
import java.util.concurrent.ConcurrentHashMap;

@Component
@Scope("singleton")
public class MySingletonBean {

    private ConcurrentMap<String, ConcurrentLinkedDeque<JsonObject>> map;

    @PostConstruct
    public void init() {
        map = new ConcurrentHashMap<>();
    }

    public ConcurrentMap<String, ConcurrentLinkedDeque<JsonObject>> getMap() {
        return map;
    }
}

Safely Adding Elements to the Queue Without Breaking Concurrency

To safely insert elements into one of the ConcurrentLinkedDeque instances inside the map, we leverage the computeIfAbsent method, which ensures atomic operations on the map.

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import com.google.gson.JsonObject;
import java.util.concurrent.ConcurrentLinkedDeque;
import java.util.concurrent.ConcurrentMap;

@Service
public class MyService {

    private final MySingletonBean mySingletonBean;

    @Autowired
    public MyService(MySingletonBean mySingletonBean) {
        this.mySingletonBean = mySingletonBean;
    }

    public void addToQueue(String key, JsonObject jsonObject) {
        ConcurrentMap<String, ConcurrentLinkedDeque<JsonObject>> map = mySingletonBean.getMap();

        // Retrieve or create the queue for the given key atomically
        ConcurrentLinkedDeque<JsonObject> queue = map.computeIfAbsent(key, k -> new ConcurrentLinkedDeque<>());
        
        // Add the object to the queue (thread-safe operation)
        queue.add(jsonObject);
    }
}

Why Use computeIfAbsent?

• Ensures that only one thread initializes the queue for a given key if it does not already exist.
• Avoids explicit synchronization or checking for null manually, reducing the risk of race conditions.
• Provides a lock-free, efficient mechanism to insert or retrieve values in a multi-threaded environment.

Performance Considerations

Using ConcurrentLinkedDeque within a ConcurrentMap provides significant advantages:

1. High Concurrency: ConcurrentLinkedDeque allows multiple threads to insert elements safely.
2. Non-Blocking: Unlike traditional synchronized collections, ConcurrentLinkedDeque and ConcurrentHashMap provide efficient, lock-free operations.
3. Atomic Updates: computeIfAbsent ensures thread-safe initialization without the need for additional synchronization mechanisms.

Use Cases for This Approach

This pattern is particularly useful in:

• Event-driven architectures, where events are queued and processed concurrently.
• Message buffering, where different message types are stored and processed in a thread-safe manner.
• Data pipelines, where multiple threads write and read large amounts of structured data concurrently.

Conclusion

Handling concurrency in Java can be complex, but by leveraging ConcurrentMap and ConcurrentLinkedDeque, we can ensure thread-safe operations with minimal performance overhead. The use of computeIfAbsent simplifies queue initialization and eliminates race conditions, making it a highly efficient approach for managing concurrent data structures in a singleton Spring bean.

Implementing this strategy allows Java applications to scale effectively, ensuring safe concurrent modifications while maintaining optimal performance. If your application deals with multi-threaded data processing, adopting this approach will help maintain data integrity and consistency.