In today’s always-connected landscape, speed is key both when it comes to website loading and response times. A few minutes too late and a customer could be lost for good. This is the reason businesses ensure that their websites and eCommerce portals are always optimized and ready for ever-changing customer demand and sentiment.
Caching is one way companies cope with the high demands of big data, keeping websites responsive and customers happy.
In the world of computing, caching is also a vital process that helps in fast retrieval of frequently used data. It’s a high-speed data layer that stores a subset of data so that subsequent requests for that data become faster. By using main memory (RAM), data access becomes faster because data movement to and from disk and within the network is reduced. In this process, the RAM used for temporary data storage is referred to as the cache.
Caching is an efficient approach for applications that follow a common pattern that repeatedly accesses data. It can also store data calculations so there’s no need to repeat the process in case it’s needed again. This saves a lot of time, especially when the calculations are time-consuming to perform.
In-memory caches present a feasible solution for applications because it uses RAM to speed up data processing and minimize constant access to dick. It also reduces data movement by allowing the application and its data to collocate in the same memory space. An IMDG helps ensure that you provide service without delay by maximizing throughput and keeping latency as low as possible.
Scaling Applications Through Distributed Caching
The current demands of server applications, high-transaction web apps, and other in-memory computing solutions have more or less made the old ways of storing data obsolete. Conventional data processing solutions simply can’t keep up with the continuously growing amounts of data and the increasing complexity of storage and management.
One of the main hindrances with “old-school” data systems is that it isn’t possible to scale out by adding more servers to the network; the more modern application architectures, on the other hand, are easily scalable.
A distributed cache takes caching further by pooling together the RAM of multiple computers within a network into a single in-memory data store that is used as a data cache. This distributed architecture allows the cache to go beyond the limits of a single computer’s memory by combining the memory and computing power of multiple computers.
A distributed cache is useful in applications that deal with high volume and load because it’s designed to do away with data storage bottlenecks. It also ensures data synchronicity across servers and allows for continuous scaling out and scaling back as necessary.
A distributed cache is commonly used through the following methods:
In this method, the cache doesn’t interact with the database but is kept aside as a faster in-memory data store. Before reading anything from the database, the application checks the cache so that it can be updated as necessary. This ensures that the cache and the database are always kept synchronized.
This method treats the cache as the main data store, reading data from and writing data to it. It relieves the pressure from the application by doing these tasks while keeping cache items in the cache while it fetches the latest version from the database. As such, it’s ideal for applications that read individual rows from the database or data that can be mapped to a cache-item.
The Benefits of Distributed Caching
As the business landscape becomes more reliant on data, speed has become a major consideration when it comes to improving application performance. There are also a host of other benefits that make a distributed cache architecture an attractive proposition for both application developers and small businesses.
- Reduced database costs.
A single cache instance can replace multiple database instances because it can provide thousands of input/output operations per second (IOPS). This consequently lowers the overall cost, especially if the primary database charges per throughput.
- Reduced load on the backend.
A distributed cache redirects the read load from the backend to the in-memory data layer, reducing the overall load on the database. This protects the system from slowdowns under load and from crashes during spikes.
- Increased read throughput.
Due to the high IOPS, a distributed cache also has the ability to serve thousands of requests per second if an instance is used as a distributed side-cache.
- Reliable performance.
Using a high-throughput in-memory cache addresses the challenges of slowdowns and unpredictable behavior during spikes in application usage. It helps mitigate high latencies caused by increased load on the database so performance is not compromised.
Say Goodbye to Bottlenecks
To achieve maximum speed and performance of applications, the key is in keeping the relationship between the data source and the caching system optimized and intact. By ensuring that it takes less time to retrieve resources from the cache than the origin server, efficiency is almost guaranteed. Distributed caching isn’t a one-size-fits-all solution for all computing challenges, however, and tailored solutions are still the best option to ensure business growth.
Ultimately, knowing what your business needs and what it wants to achieve will help you decide what the best next step is.