When system stores the data in a RAM, it is called in-memory caching. It is the simplest cache as compared to the other cache forms. It sits between applications and databases to deliver responses at high speeds by storing data from earlier requests or copied directly from databases. With in-memory caching, the time required to access the memory for the In/Outbound and CPU-bound request of the application or the computer reduces drastically..
The biggest advantage of an in-memory cache is that it removes performance delays when an application built on a disk-based database must retrieve data from a disk before processing it.
In-memory caching avoids latency and improves online application performance as RAM has an extremely faster speed of retrieval as compared to hard disks. Under this caching approach, cached data is stored based on a key-value database.
Key-value database saves the data as a set of key-value pairs, and The key is represented by a unique value, while the value is by the cached data. Each data piece of data is identified by a unique value which makes the process faster and more efficient.
Caching is used to improve the performance and scalability of a system by reducing the work required to generate or present data/content on the app or a computer. The system stores multiple copies of data or files in the temporary storage, location or cache units so it can be accessed faster on recurring data requests.
Cache units can store data for software applications, servers, and web browsers, which ensures users do not need to download information every time they access a website or application to speed up site loading. Cache files include multimedia such as images, files, and data scripts, which are automatically stored on a device the first time a user visits a website or opens an application.
It simply makes a copy of data that can be retrieved much faster than the actual source, as the crawler has to crawl the data source and takes a few nanoseconds to get the data as compared to cache files which are much faster.
Let's understand what cache means. A cache is a software or a hardware element that stores the data so that future requests for the same data can be served faster and more efficiently. Whenever the system stores/saves the data into the system or in any component, such memory is called cache.
Distributed cache is widely used in cloud computing systems and virtualized environments because it provides scalability and minimizes faults and delays. It can grow and multiply nodes or servers, which allows it to enhance its capacity. Distributed caching is more suitable for workloads that do more reading than writing data, such as product catalogues or set images that do not change frequently or with heavy traffic. In a distributed system, application servers are generally spread across multiple Virtual Machines (VMs) and sometimes across multiple physical locations. Distributed system access and route the system requests and retrieve the information from distributed caches (servers) on a temporary basis. In simple words, distributed caching works like cloud computing, as it is the process of creating a large pool of cache.
In a disk cache, a portion of RAM is used to store and speed up access to data on a disk. The RAM that is used for the internal system could be part of the disk drive itself or it could be general-purpose RAM in the computer, where a unit is reserved for disk cache. All modern devices and systems generally have a unit for cache memory for data storage. Although, hard disk caches are more expensive compared to other cache storage, a soft cache stores the most recently accessed data in the RAM cache for the system to access the data more quickly. When a program needs to access new data, the operating system first checks to see if the data is in the cache before accessing it from the disk. Because machines can access data from RAM much faster than from a disk. But at the same time, disk caching can significantly increase the performance of the system.
The functionality and effectiveness of any application/system design rely on the data access strategies/ cache strategies used. Caching strategies define the relationship between the data source and the caching memory/system. Hence, it is of utmost importance to choose the right caching strategy. So, before opting for a strategy, the system designer must analyze the access pattern of the data & try to fit your application's suitability.
1. Cache Aside
Cache aside stores and update the cache through the application asynchronously. And the system directly corresponds with both the cache and the database. Whenever a user tries to access data on an application/website, the system first checks for the data in the cache. In case of a cache miss, the system automatically routes the request to the database. Once the request is served from the database, the data set is later saved in the cache for subsequent requests.
2. Write-through cache
The write-through cache is a strategy used to write caches and not read or retrieve. Under this strategy, the data is first written to the cache and then transferred to the database. Write-through cache only deals with write issues. System designers need to combine it with a read-through cache for better functionality, as a write-through cache can only write/store data sets and not read.
3. Read-through cache
The read-through cache sits in line with the database just like the write-through cache. For the read-through cache, the data will be read through the cache every time. If a data request is raised it first goes to the cache and sends the response. If the data is not present in the cache, the system will pull that data from the database and route it again through the cache. Read-through cache has the same problem as cache aside; if the datastore gets updated through many sources, the cache gets obsolete.
4. Write-behind cache
The write-behind cache is a subtype of a write-through cache. Here, the system doesn't acknowledge the datastore before saving or storing data. The write-behind cache asynchronously writes/stores data in the datastore depending on the load. And similar to the write-through cache strategy, this strategy alone won't be sufficient for the effective functioning of the system and needs to be paired with the read-through cache strategy.
5. Refresh ahead cache
Under this strategy, the system keeps refreshing the cache/data before it expires. The refresh ahead cache strategy is used for live data and data that keeps on changing very frequently. This kind of cache is used heavily on real-time websites, such as stock markets, financial dashboards railway, or airline ticketing sites.
- Cache memory stores the instructions which may be required by the processor for subsequent uses; as it helps in recovering and reading the information faster as compared to Random Access Memory (RAM).
- Cache takes less access time as compared to main memory, and increases the effectiveness of the system.
- It stores all the data and instructions frequently used by the CPU; it retrieves the data from the data store, increasing the performance of the CPU.
- Cache memory is more expensive as compared to primary memory and secondary memory.
- When the system is turned off, the data stored in the cache is not saved and gets destroyed.
- It is a volatile memory and stores the data on a temporary basis.