Caching refers to a buffering technique that typically stores frequently-queried data in the short-term memory. That means that the data is readily available for a short period. The more programs you run on your computer, the more temporary data the computer has to store.
The database is one of the most common storage technologies used to store data. Database caching is one of the most effective strategies that can be used to improve the application’s overall performance and cut off unnecessary expenses on the database.
Is caching restricted to a specific database
Database caching is applied to all databases, including the NoSQL databases and MongoDB. The application performance regarding both scale and speed of the database is greatly improved because it’s minimally invasive to implement caching.
Benefits of using caches
Using caches improves the performance by making it easier to access data and reduces database workloads. The cache can still provide continuous service if the backend service is unavailable, making the system more tolerant of failures. Using caches is ideal for your financial planning goals. Due to the distribution of the workload to the cache system, the operation costs are lowered.
Database challenges to be aware of
When building a distributed application, specific applications require low latency and scalability. As a result, disk-based databases can pose various challenges during the caching process.
Cost to scale
Whether the data is distributed in a disk-based NoSQL database or scaled up in a vertical relational database, scaling for high reads is usually very expensive. You will require several database read-replicas to match with what is delivered by a single in-memory cache node per second.
Slow processing queries
Despite having several query optimization techniques and other schema designs to boost the query performance, data retrieval speed from the hard disk, together with the added processing time, delays your query response.
Need to simplify data access
Most relational databases aren’t optimal for data access though they provide excellent means to the data model relationships. There will be instances where your applications want to access the data in a specific structure to simplify the data retrieval process and improve the application performance.
Top caching strategies
The different strategies used for cache implementation usually have disparate impacts on your system, depending on its design. That’s why it’s vital to go through your data needs before designing your structure. Here are the top caching strategies.
Cache aside
In this strategy, the cache sits aside from the database. Your application will request data from the cache, and if the data exists, that’s a Cache Hit. Then, the application will retrieve the data directly. If the data doesn’t exist, called Cache Miss, the application will then request data from the database and write it to the cache.
Write through
The cache sits between the application and the database. Each of the writes from the application goes through the cache to the database.
Write back
Its setup is just similar to the write through. Your application will still write data to the cache. This process is delayed because the cache only flushes all the updated data to databases once in a while.
Read through
Here, the cache sits between the application and the database. If the application misses its data request, it retrieves data from the database, then updates itself, and the data is then returned to the application.
Pros and cons of each caching strategy
Cache aside
Cache aside is resilient to failure when the database goes down and is simple to implement. Also, its data model is usually different between database and cache, and that’s why it’s best for heavy workloads. On the other hand, there is the possibility of data inconsistency between cache and database, which may always lead to Cache Miss.
Write through
For this strategy, data is always available in the cache, and it’s very hard to result in Cache Miss. If paired with “write through,” there is guaranteed data consistency. However, this strategy increases the write latency for the caching process.
Write back
Write back is suitable for heavy workloads and is resilient to many moderate data failures. It reduces the writes to the database, which lowers the cost of the operation. Sometimes, this process permanently leads to the loss of data when the cache fails.
Read through
Here, a lot of code is readable as the application is unaware of the database, which is why it’s best for heavy workloads when the data sets are being repeated. However, you will need to write a more complicated plugin for the cache to be able to fetch data from the database.
The three types of database caches
Database caches supplement the primary database by removing all the unwanted pressure on it. There are three common types of database caches.
Local cache
A local cache is used to store data that’s used often within your application. A significant disadvantage of the local cache is that each node has its resident cache working disconnected.
That means that the information stored within an individual cache node cannot be shared with the other local caches. As a result, there are a lot of challenges, especially in a distributed environment where sharing of information is very critical in supporting scalable, dynamic environments.
Database integrated caches
Integrated caches are managed within the database engine and have in-built write-through capabilities. With the database integrated cache, the databases update when particular changes are made to the table.
Nothing within the application tier is needed to leverage database integrated caches. That means they cannot be leveraged for purposes like sharing data. They fall short due to their limited size and capabilities.
Remote caches
These caches are stored in dedicated servers and are particularly built upon value NoSQL stores. They are relatively fast because they can provide hundreds of thousands to a million requests per second.
On the other hand, the average latency request to the remote cache is fulfilled in sub-millisecond latency. That’s typically faster than a disk-based database. Remote caches work as a connected cluster that all your disparate systems can leverage.