1. Caching strategies
Welcome back! Now, we'll cover caching strategies.
2. What is caching?
Caching is like keeping our frequently used cooking ingredients on the kitchen counter instead of in the cupboard - it's faster to access, but we have limited counter space.
In Java applications, we commonly cache database query results, API responses, expensive calculations, and other resource-intensive operations. By implementing caching appropriately, we can dramatically improve our application's performance.
3. In-memory caching in Java
Let's start with an in-memory caching implementation in Java. Here, we've created a generic cache class using a `HashMap` as the underlying data structure. This implementation provides two methods: `get()` and `put()`. The `get()` method retrieves a value from the cache using a key, while the `put()` method adds or updates an entry in the cache. This is the leanest form of caching, but it has limitations - it has no size limits and no expiration policy, meaning our cache could grow too large or contain stale data.
4. Cache eviction policies
As our applications run, caches can grow and consume substantial memory. This is why cache eviction policies are crucial. These policies determine which items to remove when the cache reaches capacity limits. Common eviction strategies include: LRU, which removes the least recently used items first; LFU, which removes the least frequently used items; FIFO, which removes the oldest items first, time-based expiration, where items expire after a set time, and more!
5. Redis for distributed caching
For applications that run on multiple servers, we often need distributed caching solutions. Redis is a popular in-memory data store that excels as a distributed cache. It supports various data structures like strings, lists, and sets, making it versatile for different caching needs. To use Redis with Java, we can leverage client libraries such as Jedis.
6. Using Redis with Jedis
The example shows Jedis usage, which requires adding the Jedis dependency to our project and importing the Jedis class. We're connecting to a Redis server, storing a value with a key, and then retrieving that value. Redis provides additional features like automatic expiration of cached items and cluster support for high availability.
7. Implementing a time-based cache with Redis
Let's look at implementing a time-based cache using Redis with the Jedis client. Redis already has built-in support for key expiration, making it easy to implement time-based caching. Our `RedisTimedCache` class connects to a Redis server and provides basic `get()` and `put()` methods for string values. When putting a value in the cache, we use the `setex` command, which sets both the value and its expiration time in seconds. Redis automatically removes expired keys, so our get method simply retrieves the value if it still exists.
8. Summary
Let's summarize what we've learned about caching strategies. Caching stores computed results to avoid recalculation, which is particularly effective for expensive operations and frequently accessed data. To manage memory efficiently, we need to implement proper eviction policies. For applications running on multiple servers, distributed caching solutions like Redis (using the Jedis library) provide shared cache access.
9. Let's practice!
Now, let's practice!