Database Caching Techniques and Why it Matters

One of the main challenges when businesses are handling a lot of data is speed. The problem is experienced more in business environments where data is stored in multiple datastores. Usually, when computer users open files, applications, websites, and servers, the most recently accessed data is stored in a memory cache.

Cache data ensures users do not need to download afresh or retrieve the files again from the datastores. Data experts use the same concept to store data in memory in a technique known as caching. It helps users re-access data from memory and thus bypass retrieval or download time. Different techniques are used in database caching.

Cache-Aside technique

Cache-Aside is a data caching technique that emulates the functionality of read-through caching. The method loads data on demand from the data store into the cache. It gives an advantage to the business by enhancing performance. It helps create consistency between cache data and data in the datastore.

When applications, web browsers, or files are opened, the data is stored temporarily in the cache. This makes repeatedly accessed data to be retrieved with ease. The problem that arises is data consistency. The data in the cache might not always be consistent with data in the data store.

Another strategy is required to ensure the data in the cache and that in the datastore is always consistent. Most business owners implement read-through or write-through techniques to solve this problem.

These techniques reference the cache when retrieving data. If the data in question is not in the cache, it is retrieved from the storage and saved in the cache. Anytime the data is modified, it is updated in the data store too.

Sometimes the cache does not provide this feature. In this case, the applications are used for maintaining cache data. The application used for this functionality works the same way read-through caching would work, but, in this case, it is called cache-aside. It is an on-demand functionality that loads data when it is required.

Read through, write-through caching technique

Software developers have recently created new ways to free business people to handle complex applications and hardware management issues. They’ve started offering SaaS products where business people access applications online as a service. Some of the SaaS products offered are customer support SaaS tools, communication, and sales & marketing SaaS tools.

These are high transaction applications that are accessed multiple times every minute. When an application is processing a lot of data every minute, the business data storage is not able to keep up with the demand.

The time for storing each data generated and retrieving it must be accelerated many times more for the data store to keep up with the speed of access. In this scenario, memory storage provides the solution experienced with data bottlenecks.

In-memory joins several servers to pool their RAM in parallel. One of the ways this is done is through read-through/write-through. In this technique, cache memory is treated as the datastore. The application reads from the cache and writes data into it.

Instead of the application reading from the datastore and writing it into the cache, the cache takes that responsibility. It reads from the datastore and writes into it. The business benefits from this in different ways.

It helps simplify apps code: When an application is used to read data from a database and write it in the cache, its code is directly dependent on the database. The cache removes this dependency when it reads directly from the database and writes into it.

Improved performance and scalability: The taken by an application to read and write data is eliminated. The cache keeps updating data stored in it and data in the database to ensure it is always consistent. This helps to make the data highly scalable.

Lazy loading caching technique

As the name suggests, lazy loading is a lazy way of caching data. It helps increase speed but uses a longer process to read from the data store and to write in the cache. In this technique, the cache doesn’t read directly but an application does the reading.

Whenever there is a need to access data from the data store, the application checks if it’s available in the cache, if it is available, it retrieves it from there. The data could be available in cache but it’s expired. In this case, it reads from the data store and updates it on the cache, and then retrieves it to display on the screen.

Its main advantages are as follows-

  • The technique caches only the requested data
  • It minimizes load failures significantly

Write-Around data caching

Write-Around data caching is used in scenarios where data is rarely retrieved. It can be retrieved once and take another long time before retrieving it again. The technique works almost like a write-through cache. However, when writing data, the cache is bypassed and it’s written directly into the datastore.

This process helps reduce loading too much data into the cache, which can make it slower. The only disadvantage with this technique when retrieving data is that the application will experience a cache miss forcing it to read from the data store. The main advantage is that data that is rarely read will not occupy space in the cache. This helps the cache to have a consistent speed.

Write back caching technique

Write back technique is a process where data is written in cache and then written back into the datastore. The time taken to write back can be 10 seconds, half an hour, one day, or even several weeks.

For this technique to work, every data that is read from the datastore and written in the cache is queued immediately for write back. The data is queued following the same reading/writing sequence. The data that was written into the cache first is written back into the datastore first.

The technique requires the application to be reconfigured so that it reads and writes back. The main advantage of this technique is that updates of the datastore occur outside the cache. This ensures no conflicting updates are occurring in the datastore. It helps to improve performance, reduce database load, and eliminate data failures.