Effective Data Caching Patterns
Applications should deliver responses quickly to be considered high-performance. No one likes a lagging application or website, as it affects user experience. Data caching is one of the techniques used to improve application performance. For instance, when performing data integration, an effective data caching mechanism is crucial to ensure fast data retrieval from different sources. It reduces latency in retrieving data from a slow or remote data source.
Caching comes from the term cache, a component that stores data elements that would otherwise take longer to process or calculate. Also, caches may originate from other underlying backend systems. Caching helps prevent more requests for round trips for regularly used data. But what are some commonly used data caching patterns? Let’s find out.
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Common Terminologies Used in Data Caching
Before diving into the frequently used data caching patterns, let’s introduce some terminologies used in data caching.
- Cache miss. This term is used when the data request is not found in the cache, meaning it has to be calculated or processed by backend systems.
- Cache hit. Unlike a cache miss, a cache hit means the data request is available on the cache. This means data will be served from the cache rather than from backend systems.
Top 5 Data Caching Patterns
Here are some effective data caching patterns used to improve application performance:
1. Cache-Aside Pattern
Cache-aside or lazy loading is the most common data caching strategy. This caching pattern works based on the following data retrieval logic:
- If an app needs to read data from your database, it scans the cache first to check whether the requested data is available on the cache.
- Suppose it’s a cache hit or the data is available. In that case, the cached data is returned, issuing the response to the caller.
- However, if the data is not available in the cache (cache miss), a query is sent to the database. The cache is populated with the data retrieved from your database before returning it to the caller.
Cache-aside data caching pattern has several benefits, including the following:
- Implementing cache-aside is straightforward. Also, it generates immediate performance improvements, whether you use custom application logic or an application framework that encapsulates cache-aside.
- The cache only contains data that your application requests. This helps keep your cache size cost-effective.
On the downside, this data caching pattern only loads data into the cache after a cache miss. This adds some overhead to the primary response time since additional roundtrips to the database and cache are necessary.
2. Read-Through and Write-Through Pattern
These data caching patterns are unique from the rest. All data access is acted upon by the cache. In this case, the cache acts as a transparent layer between your application and the data source.
For read-through, when your application requests data, the cache checks whether it is present locally. If the data is available, the cache returns it to the caller. Otherwise, the cache will do the work of fetching the data from the data source and storing it locally before returning it to the caller.
Similarly, the write-through data caching pattern works the same. When your application writes data, the cache will update itself and the data source. This simplifies data caching for applications, as it doesn’t need to be aware of the caching layer.
These patterns facilitate faster retrieval of data. Also, they ensure data is not lost by writing the data immediately to the backing store. However, writing data may experience latency, as it involves writing two places (cache and backing store) every time.
3. Write-Back Caching
This pattern is also known as write-behind. It defers the write operation to the underlying data source and instantly updates the cache. The write operation is performed asynchronously. This improves writing performance. Write-back caching is useful when write operations are frequent and must be fast while maintaining data consistency.
This data caching pattern is beneficial in write-intensive applications. It ensures high throughput and low latency. However, it poses a data availability risk. The write operation to the primary data source is done asynchronously. This means if the cache fails before writing data to the primary storage, it can lose your data.
4. Cache Invalidation Pattern
This pattern involves updating and removing cache entries when the corresponding data source is changed. This ensures outdated data is not served from the cache. Several cache invalidation strategies are used, including event-based, time-based, and manual invalidation. The choice of strategy depends on your application’s requirements and use cases.
5. Refresh Ahead Caching
This caching technique involves refreshing cached data before it expires. It essentially refreshes your cache at a predefined interval immediately before the next potential cache access. It often takes some time because of network latency. Meanwhile, hundreds or thousands of read operations might have occurred in a read-heavy system in a few milliseconds.
- This data caching pattern reduces latency more than other techniques.
- It is useful when many users are using the same cache key or in read-heavy environments. Since data is updated frequently and periodically, data staleness isn’t a permanent problem.
- This data caching model is challenging to implement because the cache service takes additional pressure to update or refresh all the keys when and as they are accessed.
How to Choose the Right Caching Pattern
We have seen every data caching has its benefits and challenges. Therefore, selecting the right data caching pattern is crucial to maximize performance and efficiency. Some patterns are more efficient for write-intensive applications, while others are more effective in read-heavy environments. Here are some factors to consider when selecting a caching pattern:
- Cache consistency. Ask yourself how consistently you’ll be using the cache. This will guide help you narrow down your options.
- Cache size. Determine the amount of cache you need to optimize your application’s performance.
- Eviction policies and invalidation strategies. Determine whether you need to evict or automatically invalidate data with time.
Data caching helps improve the performance of your applications and maximize their efficiency. Several caching patterns exist, including cache-aside, write-back, write-through, and read-through. The choice of caching pattern depends on the characteristics of your application and specific requirements. So, you should properly understand your application’s purpose and requirements to select the right data caching mechanism.