Read-Through Caching Pattern: An Example with Caffeine

Read-Through Caching Pattern

The read-through cache pattern is a caching strategy that enhances performance by maintaining a cache between the application and the data store. Unlike the cache-aside pattern, where the application is responsible for reading from and writing to the cache, in the read-through pattern, the cache itself manages the interaction with the data store.

The read-through cache pattern operates as follows:

Read Operations: When an application needs to read data, it makes a request to the cache. If the data is found in the cache (a cache hit), it’s returned immediately. If the data is not found in the cache (a cache miss), the cache retrieves the data from the data store, stores it in the cache for future use, and then returns it to the application.
Write Operations: When an application needs to write data, it writes directly to the cache. The cache then writes the data to the data store either immediately or at a later time based on the cache’s write policy.

This pattern is called “read-through” because all read requests are made to the cache, which reads through to the underlying data store if necessary.

Sequence Diagram

The Caffeine Caching Library

Caffeine is a robust, high-performance caching library for Java 8, designed as an advanced alternative to Google’s Guava cache. It provides an efficient in-memory cache that scales well on multi-core systems. Caffeine uses an intelligent eviction policy to manage memory usage, ensuring valuable entries remain in the cache while less valuable ones are removed.

It offers advanced features such as automatic cache loading and asynchronous computation of values, which can significantly improve application performance. Additionally, it supports various eviction policies, providing fine-grained control over cache behavior. Overall, Caffeine is an excellent tool for enhancing application performance through efficient and scalable caching.

Kotlin Example with Caffeine Library

Here’s a simple Kotlin code example that demonstrates the read-through cache pattern using the Caffeine library:

import com.github.benmanes.caffeine.cache.CacheLoader
import com.github.benmanes.caffeine.cache.Caffeine
import java.util.concurrent.TimeUnit

class ReadThroughCacheExample {
    private val cache = Caffeine.newBuilder()
        .expireAfterAccess(5, TimeUnit.MINUTES)
        .maximumSize(100)
        .build(CacheLoader<String, Data> { key -> fetchDataFromStore(key) })

    fun readData(key: String): Data? {
        // The cache will automatically load the data from the data store in case of a cache miss
        return cache.get(key)
    }

    fun writeData(key: String, data: Data) {
        // Write data to the cache
        cache.put(key, data)

        // The cache will write the data to the data store based on its write policy
    }

    // Fetch data from the data store (e.g., a database)
    private fun fetchDataFromStore(key: String): Data {
        // Implementation depends on the specific data store
    }
}

data class Data(val content: String)

Kotlin

In this example, readData and writeData methods implement the read-through cache pattern. When reading data, the application requests data from the cache. If the data is not found in the cache, the cache fetches the data from the data store, stores it in the cache, and then returns it. When writing data, the application writes directly to the cache, and the cache writes the data to the data store based on its write policy.

In the provided Kotlin code example, the cache is loading the data using a CacheLoader. A CacheLoader is a functional interface provided by the Caffeine library that defines how to compute or retrieve a value corresponding to a key.

Here’s the relevant part of the code again :

private val cache = Caffeine.newBuilder()
    .expireAfterAccess(5, TimeUnit.MINUTES)
    .maximumSize(100)
    .build(CacheLoader<String, Data> { key -> fetchDataFromStore(key) })

Kotlin

In this code, CacheLoader<String, Data> { key -> fetchDataFromStore(key) } is a lambda function that defines how to load data for a given key. This function is passed to the build method of the Caffeine builder.

When the application calls cache.get(key), the cache checks if it contains a value for the given key. If it does (a cache hit), it returns the value. If it doesn’t (a cache miss), it uses the CacheLoader to load the data. Specifically, it calls the fetchDataFromStore(key) method, which is supposed to fetch the data from the data store (e.g., a database). The fetched data is then stored in the cache and returned to the application.

This way, the cache automatically loads the data from the data store in case of a cache miss, which is a key characteristic of the read-through cache pattern.

When to use the Read-Through Caching Pattern

Pros
- Automatic Loading: The cache automatically loads data from the data store in case of a cache miss, which can simplify the application code.
- Consistency: The cache is always updated immediately after a write operation, which can help ensure data consistency.
Cons:
- Complexity: The read-through pattern can be more complex to implement, as it requires a loader function or callback for the cache to use when loading data.
- Potential Waste: If the cache preloads data that is never requested by the application, it can lead to wasted memory.

In general, the choice between cache-aside and read-through depends on the specific requirements of your application. If you need fine-grained control over cache operations and want to keep things simple, cache-aside might be the way to go. On the other hand, if you want to ensure data consistency and simplify your application code, you might prefer the read-through pattern.

Read-Through Caching Pattern: An Example with Caffeine