Implementing a cache with distributed query routing using Netflix Eureka and HashMap in Java

23 Oct 2023

references

In distributed systems, caching is a crucial component to improve performance and reduce network overhead. By caching frequently accessed data closer to the client, we can minimize the number of requests sent to the backend services. In this blog post, we will explore how to implement a cache with distributed query routing using Netflix Eureka and HashMap in Java.

Overview
Netflix Eureka
Cache Implementation
- Using HashMap
- Distributed Query Routing
Conclusion

Overview

In a distributed system, multiple instances of a service may be running concurrently. To efficiently cache data, we need to ensure that each instance has access to the same cache. Netflix Eureka, a service registry, can help us achieve this by providing a centralized registry of all available services in the system.

We will leverage Netflix Eureka to implement a distributed cache where each service instance can query and update the cache. The cache itself will be implemented using a HashMap, a key-value store data structure.

Netflix Eureka

Netflix Eureka is a REST-based service that allows services to register themselves and discover other services. It helps with dynamic load balancing, fault tolerance, and service discovery in a distributed system. To use Eureka, we need to set up a Eureka Server to which all services will register.

To integrate Eureka in our Java project, we can make use of the spring-cloud-starter-netflix-eureka-client dependency. This will provide us with the necessary annotations and classes to interact with Eureka.

Cache Implementation

Using HashMap

To implement the cache, we will use a HashMap data structure, which provides constant-time access and retrieval of data based on a key. We can create a wrapper class that encapsulates the HashMap and provides methods to read and update the cache.

public class Cache {
    private Map<String, Object> cache;

    public Cache() {
        this.cache = new HashMap<>();
    }

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

    public void put(String key, Object value) {
        cache.put(key, value);
    }

    // other cache operations...
}

Distributed Query Routing

To achieve distributed query routing, we will leverage the Eureka service registry. Each service instance will register itself with Eureka and obtain a list of all available instances of the same service. When a service wants to access the cache, it will use Eureka to discover all instances and query each instance for the desired data.

@Service
public class CacheService {
    private Cache cache;

    @Autowired
    private DiscoveryClient discoveryClient;

    public CacheService() {
        this.cache = new Cache();
    }

    public Object get(String key) {
        List<ServiceInstance> instances = discoveryClient.getInstances("my-service-name");
        Object value = null;

        for (ServiceInstance instance : instances) {
            value = queryCache(instance, key);
            if (value != null) {
                break;
            }
        }

        return value;
    }

    private Object queryCache(ServiceInstance instance, String key) {
        // Make a REST API call to the service instance to fetch the value from its cache
        // Return the value if found, otherwise return null
        // You can use any HTTP library (e.g., Apache HttpClient or Spring RestTemplate) to make the API call
        // Example implementation:
        // ResponseEntity<Object> response = restTemplate.getForEntity(instance.getUri() + "/cache?key=" + key, Object.class);
        // if (response.getStatusCode() == HttpStatus.OK) {
        //    return response.getBody();
        // } else {
        //    return null;
        // }
    }
}

In the CacheService class, we retrieve a list of all instances of the service using the DiscoveryClient provided by Eureka. We then iterate over each instance and query its cache using the queryCache method. If we find the desired data in any instance, we return it. Otherwise, we return null.

Conclusion

Implementing a cache with distributed query routing can greatly improve the performance and scalability of distributed systems. By leveraging Netflix Eureka for service registry and a HashMap for caching, we can achieve efficient data access and distributed cache synchronization across all service instances.

By following the steps outlined in this blog post, you can implement a cache with distributed query routing using Netflix Eureka and HashMap in Java. This will not only speed up your system but also improve overall reliability and scalability.

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