Building a stable and reliable caching system: Sharing experience in the design and implementation of Java caching mechanism

Building a reliable caching system: Sharing of design and practical experience of Java caching mechanism

Introduction:
In most applications, data caching is an improvement A common approach to system performance. Caching reduces access to the underlying data source, significantly improving application response time. In Java, we can implement the caching mechanism in a variety of ways. This article will introduce some common caching design patterns and practical experiences, and provide specific code examples.

1. Cache design pattern:

1. Memory-based cache
   Memory-based cache is the most common cache design pattern. It stores data in memory for quick retrieval when the application needs it, typically using a HashMap or ConcurrentHashMap. Here is a simple memory-based cache example:

import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.TimeUnit;

public class InMemoryCache<T> {
    private final Map<String, CacheEntry<T>> cache;
    private final long expirationTime;

    private static class CacheEntry<T> {
        private final T value;
        private final long createTime;

        CacheEntry(T value) {
            this.value = value;
            this.createTime = System.currentTimeMillis();
        }

        boolean isExpired(long expirationTime) {
            return System.currentTimeMillis() - createTime > expirationTime;
        }
    }

    public InMemoryCache(long expirationTime) {
        this.cache = new HashMap<>();
        this.expirationTime = expirationTime;
    }

    public void put(String key, T value) {
        cache.put(key, new CacheEntry<>(value));
    }

    public T get(String key) {
        CacheEntry<T> entry = cache.get(key);
        if (entry != null && !entry.isExpired(expirationTime)) {
            return entry.value;
        } else {
            cache.remove(key);
            return null;
        }
    }

    public static void main(String[] args) {
        InMemoryCache<String> cache = new InMemoryCache<>(TimeUnit.MINUTES.toMillis(30));
        cache.put("key1", "value1");
        String value = cache.get("key1");
        System.out.println(value);
    }
}

2. Disk-based cache
   Disk-based cache stores data in disk files so that it can be cached when the application needs it Read. This cache design pattern works well for larger data sets, but is slower to read than a memory-based cache. The following is a simple disk-based cache example:

import java.io.*;
import java.util.HashMap;
import java.util.Map;

public class DiskCache<T> {
    private final Map<String, File> cache;

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

    public void put(String key, T value) {
        try {
            File file = new File("cache/" + key + ".bin");
            ObjectOutputStream outputStream = new ObjectOutputStream(new FileOutputStream(file));
            outputStream.writeObject(value);
            outputStream.close();
            cache.put(key, file);
        } catch (IOException e) {
            e.printStackTrace();
        }
    }

    public T get(String key) {
        File file = cache.get(key);
        if (file != null && file.exists()) {
            try {
                ObjectInputStream inputStream = new ObjectInputStream(new FileInputStream(file));
                T value = (T) inputStream.readObject();
                inputStream.close();
                return value;
            } catch (IOException | ClassNotFoundException e) {
                e.printStackTrace();
            }
        }
        cache.remove(key);
        return null;
    }

    public static void main(String[] args) {
        DiskCache<String> cache = new DiskCache<>();
        cache.put("key1", "value1");
        String value = cache.get("key1");
        System.out.println(value);
    }
}

2. Caching practical experience:

1. Selection of caching strategy
   When selecting a caching strategy, you need to Consider the size of the cache, the lifecycle of the data, and the application's access patterns to the data. For frequently accessed and small-capacity data, you can choose memory-based caching; for larger-capacity data sets, you can use disk-based caching.
2. Cache cleaning and expiration processing
   In order to prevent cached data from expiring, cache cleaning and expiration processing need to be performed regularly. You can set an expiration time based on the size and capacity of the cache, or use an elimination strategy (such as least recently used) for data cleaning.
3. Distributed processing of cache
   In a distributed system, the consistency of cached data needs to be considered when multiple nodes share cached data. You can use a distributed cache system (such as Redis) to implement distributed processing of cache and ensure data consistency.

3. Conclusion:
By properly designing and using the caching mechanism, the performance and response speed of the application can be significantly improved. When building a reliable cache system, choose an appropriate cache strategy, perform cache cleaning and expiration regularly, and consider the consistency of distributed caches. This article provides specific code examples of memory- and disk-based caching design patterns, hoping to help readers build reliable caching systems.

References:

• Javatpoint. (2019). Java Cache. https://www.javatpoint.com/java-cache
• Baeldung. (2021) ). Spring Caching with Redis. https://www.baeldung.com/spring-data-redis-cache

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