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How to Use in Memory Cache

Retrieving data to and from the main storage can be slow and expensive. To overcome this challenge, a high-speed memory is required to boost the speed and sync data. Although it can be costlier compared to RAM or disk memory, the cache memory provides the fastest memory that creates a buffer between the main memory and the CPU. 

The cache stores often used data and makes it readily available whenever it’s needed. As a result, it significantly reduces the time taken to retrieve data and move it back to the main memory. The memory is smaller compared to other memories but it’s many times faster than them. One CPU might contain several independent caches holding various data and instructions. 

In-memory caching – What is it?

In memory cache is mostly called caching and is a technique that is used by computers to store data in their main memory temporarily. It is a portion in RAM that stores most recently or often accessed data for fast retrieval. If often accessed data is stored in permanent storage such as hard disks, it takes time to retrieve it and store it back. RAM is much faster than hard disks and caching helps save time. 

Caching becomes more useful when an application or data is accessed repeatedly. Instead of retrieving it often from the main storage, the cache provides a quicker way to access it. It can also be useful in situations where certain complex data calculations are often performed. Instead of performing the calculations repeatedly, cache saves time by retrieving the previous calculations. 

How does in-memory caching work?

In-memory caching can be used to improve data center efficiency but it requires some technical knowledge. First, set aside some part of the RAM to be used for caching. Before an app reads data from the main storage, it first reads from the cache to confirm if it exists there. If it finds it there, it avoids reading from the main storage and reads from the cache. 

If the app fails to find the targeted data from the cache, it reads from its source, retrieves it, and writes it on the cache memory. Once the data is written in the cache memory, it becomes readily available the next time it is required. 

The cache memory is different from the RAM because data available in RAM gets lost once the computer is switched off unless an application to make it a storage device has been installed. Although the data stored in cache is also temporary, it doesn’t get lost once the computer is switched off. 

On the other hand, cache memory is small and cannot hold much data. As more data is written on it, it will eventually get full. The system automatically removes the old data to give space to newer data. The data available on cache memory is mostly the current data because historical data is removed. 

For example, a certain data was first accessed three months ago and is retrieved daily and another set of data was first accessed a week ago and hasn’t been accessed again. If the cache memory is full, it will remove the latter data, although it was first accessed more recently. But since the former file is accessed often, it is categorized as recent data and thus, it shall not be removed.  

Other data might not have been accessed for a week but had been retrieved multiple times before. The cache memory will classify it as data that is frequently used and it shall not remove it. 

Cache memory limitations

One of the greatest cache limitations is minimizing cache misses. These are the number of times cache tries to read data that is not stored in it. If it misses reading too many times, it becomes less efficient. If you have an app that reads or writes new data only, it cannot be stored in the cache and it will affect its productivity because it has to find and read it from the source all the time. 

Solving cache memory limitations

The solution to look for is to minimize the number of cache misses and improve its storage space to improve its efficiency. One of the best ways is to leverage on bigger caches. A single RAM might not solve these challenges because its storage is limited. 

To allow more storage and speed apps performance, users need to distribute caches to enable them to read and access bigger sets of data. The way distributed caches work is by pooling together multiple RAMs. This way, the user can create a bigger cache that they can continually grow by pooling together more RAMs into the network. 

In-memory data grids are important when clustering multiple RAMs into a pool to let them share their caches and thus run bigger data on them. This process accelerates speed many times faster than the way a single cache memory would work. 

Why do companies use cache memory?

According to data, a large number of visitors abandon their searches if the website they are searching for proves to be too slow. Businesses increase their cache storage to improve retrieval speed and thus maintain or increase traffic to their website. 

Another importance is when businesses are looking for intelligence. Some searches in the cache that are accessed most can be used by businesses to improve service delivery. The cache is also used to reduce database costs. Instead of reading from the database thousands of times, the data in the cache can be read ten thousand times which reduces cost and saves a lot of money. 

Cache memory helps enhance apps productivity. When reading from a storage disk, the app takes longer to read but when reading from the cache, it takes milliseconds to read, which significantly improves performance. 

Predictable performance – there are seasons when some apps are accessed millions of times per minute. An example is during national elections, world cup, athletics, or during the Christmas season. During these seasons, applications such as social media, news media, and betting apps get accessed millions of times every minute. 

If they are reading from storage, it’s impossible to predict if the application will fail due to overloading from the back-end. When it’s reading from the cache memory, it’s possible to predict its performance, especially when it’s pooled with multiple RAMs. 

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Written by Marcus Richards

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