Caching Patterns

Caching is a crucial technique for optimizing system performance and conserving resources. It involves temporarily storing and sharing data in a high-speed memory area. This approach offers two main benefits:

• It avoids the need to retrieve data from slower physical storage.
• It allows the reuse of results from computationally intensive queries.

Shared Cache

A common caching pattern involves sharing a cache among multiple servers.

Consider a web service that needs to cache a piece of data temporarily:

• Storing it locally on a single instance might make the service stateful. Other instances might not be aware of the cached data, leading to inconsistencies.

• To address this, cached data can be moved to a dedicated shared store. All instances will consistently serve the same data by accessing this central cache.

Distributed Cache

Cached data is often self-contained, which enables a distributed cache to shard data across multiple servers.

Cache Compression

Cache components can be expensive due to their reliance on large amounts of fast memory. Compression is an important, though often overlooked, way to reduce operating costs.

Since cache components are frequently busy serving many clients, it’s generally better to handle compression and decompression on the client side.

Cache Eviction

Due to the high cost of high-speed memory, it’s crucial to cache only necessary data. This requires a cache eviction policy to remove older data and free space for new entries.

Least Recently Used (LRU)

The Least Recently Used (LRU) strategy is the most common approach. When the cache reaches capacity, the least recently accessed data is discarded.

This method is straightforward and widely used. It is most effective when recent access patterns are a reliable indicator of future access.

Least Frequently Used (LFU)

Least Frequently Used (LFU) is used when access frequency is a better predictor of future access. In this case, the data with the lowest number of accesses is evicted.

From a programming standpoint, LFU is more challenging to implement and requires more resources to operate. The choice between LRU and LFU should depend on the specific data access pattern.

Next, we will explore common patterns for effectively maintaining caches.

Cache-aside (Lazy Loading)

Adapted from the lazy loading pattern, this strategy caches data only after it has been recently read. In other words, the data must first be retrieved from the primary store, and the result is then reused for subsequent requests.

For example, if a service attempts to load data from the Cache Store and doesn’t find it (a cache miss), it then retrieves the data from the primary store and caches it for future use.

This strategy is widely used due to its simplicity and versatility. However, the main drawback of Cache-aside is potential inconsistency. Cached data is typically evicted (deleted) after a certain period. While the data is cached, the source data in the primary store might be updated, leading to a mismatch between the cached version and the source.

Furthermore, if the system handles many complex and resource-intensive queries, the penalty for a cache miss (when requested data is not found in the cache) can be significant. The system might experience numerous concurrent cache misses, leading to performance degradation.

Write-through Cache

A more complex caching strategy is Write-through. This approach proactively caches data beforehand. When data is updated in the primary store, it is also simultaneously updated in the cache store.

Developing and managing a write-through cache is considerably more challenging. Imagine caching the result of a complex query involving several data entities. Any change to these entities would alter the query result, requiring a cache refresh.

Moreover, proactively caching data can be resource-intensive if that data is never used.

The write-through cache is particularly useful for:

• Ensuring consistency for critical data, as the cache is continuously synchronized with the data source.
• Minimizing cache misses, especially when cache misses are computationally expensive, because the cache is precomputed.

Refresh-ahead Cache

Another common strategy is Refresh-ahead, which involves precomputing and caching data periodically.

For instance, in a game’s ranking system, directly sorting and paginating results from the primary user store for every query would be extremely costly. Instead, the leaderboard can be computed and cached, say, every hour.

The primary issue with this strategy is staleness; the data might be outdated between refreshes. Despite this, Refresh-ahead caching is well suited to data that is computationally expensive to generate, such as ranking systems, recommendation engines, and analytics dashboards.

Write-Behind (Write-Back) Cache

Unlike the previous strategies, which focus on read operations, the Write-Behind strategy is designed to improve write performance.

Instead of immediately writing updates to the physical store, changes are batched and written asynchronously. The cache component temporarily holds these in-flight updates and flushes them to reliable storage after a certain threshold is met (e.g., a specific number of updates or a time interval).

A significant risk with this approach is potential data loss. If the cache system fails before flushing the data, any unwritten updates will be lost permanently. Write-behind caching is particularly useful for write-heavy applications, such as:

• Non-critical systems where some data loss is tolerable (e.g., metrics collection, user activity tracking).
• Systems where data loss is recoverable (e.g., transferring data from various sources to a data lake).

Client-Side Caching

In many scenarios, caching can be implemented on the client side. This helps reduce resource consumption on the server, particularly network bandwidth.

However, this approach should be used cautiously. Client devices often have limited resources, and implementing caching or performing heavy computations on them could significantly degrade the user experience.