In this blog post, we will explore how to implement a cache with distributed monitoring using Micrometer and a HashMap in Java. Caching is a common technique used to improve the performance of read-heavy applications by storing frequently accessed data in memory.

Table of Contents

• Introduction
• Implementing the Cache
• Enabling Distributed Monitoring with Micrometer
• Conclusion

Introduction

Caching provides a way to store the results of expensive computations or database queries in memory, allowing subsequent requests for the same data to be served faster. However, it is crucial to monitor the cache to ensure its performance and identify potential issues.

Micrometer is a popular metrics collection library in the Java ecosystem. It provides a unified API for collecting metrics and integrating with various monitoring systems. By integrating Micrometer into our cache implementation, we can easily monitor cache statistics like hit rate, eviction rate, and request latency.

Implementing the Cache

To implement the cache, we will use a HashMap as the underlying data structure. The keys will be the cache keys, and the values will be the cached values.

First, let’s define the Cache interface:

public interface Cache<K, V> {
    V get(K key);
    void put(K key, V value);
    void evict(K key);
    void clear();
    long size();
}

Next, we will create an implementation of the Cache interface using a HashMap:

public class HashMapCache<K, V> implements Cache<K, V> {
    private final Map<K, V> cache;

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

    @Override
    public V get(K key) {
        return cache.get(key);
    }

    @Override
    public void put(K key, V value) {
        cache.put(key, value);
    }

    @Override
    public void evict(K key) {
        cache.remove(key);
    }

    @Override
    public void clear() {
        cache.clear();
    }

    @Override
    public long size() {
        return cache.size();
    }
}

The HashMapCache class simply delegates the cache operations to the underlying HashMap.

Enabling Distributed Monitoring with Micrometer

To enable distributed monitoring with Micrometer, we will instrument our cache with relevant Micrometer metrics. Let’s add the necessary dependencies to our project:

<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-core</artifactId>
    <version>latest-version</version>
</dependency>
<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-registry-prometheus</artifactId>
    <version>latest-version</version>
</dependency>

We will use the Prometheus registry for monitoring our cache metrics.

Now, let’s modify our HashMapCache class to include Micrometer instrumentation:

import io.micrometer.core.instrument.Counter;
import io.micrometer.core.instrument.Metrics;
import io.micrometer.core.instrument.Timer;

public class HashMapCache<K, V> implements Cache<K, V> {
    private final Map<K, V> cache;
    private final Counter hits;
    private final Counter misses;
    private final Timer requestTimer;

    public HashMapCache() {
        this.cache = new HashMap<>();
        this.hits = Metrics.counter("cache.hits");
        this.misses = Metrics.counter("cache.misses");
        this.requestTimer = Metrics.timer("cache.request");
    }

    @Override
    public V get(K key) {
        Timer.Sample sample = Timer.start();
        V value = cache.get(key);
        sample.stop(requestTimer);
        if (value != null) {
            hits.increment();
        } else {
            misses.increment();
        }
        return value;
    }

    @Override
    public void put(K key, V value) {
        cache.put(key, value);
    }

    @Override
    public void evict(K key) {
        cache.remove(key);
    }

    @Override
    public void clear() {
        cache.clear();
    }

    @Override
    public long size() {
        return cache.size();
    }
}

In the modified HashMapCache class:

• We create three Micrometer counters: hits, misses, and requestTimer.
• In the get() method, we start a timer before accessing the cache and stop it afterwards. We increment the respective counter based on the cache hit or miss.

Conclusion

In this blog post, we learned how to implement a cache with distributed monitoring using Micrometer and a HashMap in Java. Micrometer provides a convenient way to collect metrics and integrate with monitoring systems. By monitoring cache statistics, we can identify performance bottlenecks and fine-tune our caching strategy.

By implementing this cache with distributed monitoring, you can gain insights into your cache’s performance and make informed decisions to optimize your application.

References

• Micrometer Documentation
• Prometheus