Caching plays a crucial role in optimizing system performance, reducing latency, improving scalability, and enhancing the user experience by providing faster access to frequently accessed data while reducing the load on the primary data source. But unfortunately, there is no universal way to build a caching system that works equally well for different types of applications. Therefore, there are different caching strategies, each of which has its own characteristics and is better suited for a particular case. In this topic, let's talk about three of these caching strategies and discuss their advantages and limitations.
Cache-aside pattern
First, let's take a look at the cache-aside strategy, perhaps the most common approach. The cache-aside pattern is a caching strategy used for read-heavy applications to reduce the load on the primary data store and improve overall application performance. The main idea of this strategy is to "aside" the cache as a separate layer between the application and the data store.
When an application needs to read data, it first attempts to retrieve the data from the cache. And if the data is already presented in the cache (a cache hit), it's returned immediately. Otherwise, (a cache miss), the application retrieves the data from the main database, puts it into the cache, and then returns it. When your application needs to write data, it writes directly to the primary data store and invalidates the corresponding entry in the cache.
The cache-aside strategy performs very well in highly loaded systems where the priority operation reads from the memory. Because of this, using this caching strategy can significantly help to reduce the workload on the primary data store and improve the application's performance. In addition, the performance also gets better because writing to the cache occurs only when necessary, in the case of a cache miss. This makes applications with cache-aside quite cache failures resistant and makes them more reliable to use.
However, the cache-aside strategy could be a little complex to implement in the application, in case the code needs to handle cache management, such as checking the cache, populating the cache on misses, invalidating the cache on writes, etc. And in addition, it has some risk of serving old data if the data in the primary store changes and the cache isn't invalidated properly yet.
So, the cache-aside strategy is useful when read operations are more frequent than write operations, and the cost of a cache miss (i.e., the time taken to fetch data from the data store) is acceptable. You can use the cache-aside pattern in web applications, where certain data like user profiles, product details, or articles are read more frequently than they are updated. Using this caching pattern can help improve the performance and user experience of these applications by reducing the load on the database and serving the frequently accessed data faster.
Write-through caching
Another caching strategy is a write-through. In a write-through cache, data is simultaneously written into the cache and the underlying data store. When the cache receives a data-write operation, it updates its own data and then forwards that write operation to the data store.
This caching strategy provides high data consistency since every write operation updates the cache and the data store; the two are always in sync. Also, the write-through approach is relatively simple to implement: there's no need to manage complex synchronization processes between the cache and the data store.
Despite that, write operations have higher latency because each write needs to update the cache and the data store. This can slow down the application's write performance. In addition, if many write operations are updating data that are rarely read, those write operations can consume resources without providing much benefit.
So, you can safely use this strategy when you need strong consistency between the cache and the data store, and when write operations are not too frequent. For example, for online shops or user profiles on some social media.
Write-behind caching
Similar to the previous caching method is a write-behind. The difference is that write operations to the cache and to the database are not simultaneous. In a write-behind cache, write operations are first written to the cache and then written to the data store after a certain delay or under certain conditions. The cache will return success to the write operation as soon as the data is updated in the cache, without waiting for the data to be written to the data store.
This feature makes it a good choice in some cases because write operations return as soon as the cache is updated, without waiting for the data store. Also, it can group multiple write operations together and update the database in a single batch operation. This also helps in reducing the load on the data store.
But there are some drawbacks to this approach too. Firstly, using the write-behind caching, you can face data inconsistency: there's a time window in which the cache and the data store are not in sync, which can lead to data inconsistency if the data is read from the data store directly. Also, write-behind caching is more complex to implement than write-through caching. It requires a mechanism to track and manage the delayed write operations.
In total, this caching strategy can be a good solution for write-heavy applications, as it allows writing operations to return quickly without waiting for the data store. Especially when you can tolerate some degree of data loss or inconsistency, as there's a risk of data loss if the cache fails before it has a chance to update the data store.
Cache invalidation
Cache invalidation is a critical aspect of cache management. It refers to the process of removing data from the cache so that subsequent requests for that data will retrieve the updated data from the underlying data store, ensuring that stale or outdated data isn't served from the cache.
However, cache invalidation can be challenging for several reasons. For instance, when data changes, it's not always straightforward to know which cache entries are affected and must be invalidated. This is especially challenging in complex systems where one piece of data may be associated with multiple cache entries because cache invalidation can be a resource-intensive operation.
Despite these challenges, there are various strategies for handling cache invalidation effectively. For example, in the cache-aside pattern, when a write operation occurs, the corresponding cache entry is invalidated. The next read operation will then fetch the updated data from the data store and refresh the cache. Also, as mentioned before, in a write-through cache, every write operation updates both the cache and the data store. This ensures that the cache always has the most up-to-date data, eliminating the need for explicit cache invalidation.
Comparing the strategies
In this section, let's highlight the main pros and cons of every caching strategy, so you can easily decide which one to use in your next project.
| Cache-aside | Write-through | Write-behind |
|---|---|---|
| Significantly reduces the workload on the main storage | Provides high data consistency | Write operations are quite fast |
| Perfectly suitable for read-heavy applications | Simple to implement | Can group the operations and execute them in a single batch |
| Can be a little complex to implement in the application | Low risk of the stale data serving | Can be complex to perform |
| Has a risk of serving old data | Write operations may have high latency | Has a risk of data inconsistency |
Conclusion
In this topic, you looked at three caching strategies: cache-aside, write-through, and write-behind caching. Also, you considered its advantages and drawbacks. Remember, the best strategy can vary depending on the specifics of your application. It's often a good idea to test different strategies under realistic conditions to see which one works best for your use case.