Types of Data Caching for Different Business Use Cases
Caching is a cost-effective solution that ensures fast response times for most businesses, but it isn't a one-size-fits-all solution.
- By Edward Huskin
- February 16, 2021
Depending on what type of business you're in, you might have different computing requirements. There is, however, one aspect that all businesses should factor into their data strategy and systems -- speed. Intelligent business analytics and self-service data preparation can leverage processing power to provide this and help maximize system and application performance.
Today's always-online world has allowed consumers to get the products or services they want faster and in the comfort of their own homes. A delay of a few seconds can cause customers to look elsewhere, leading to revenue loss that increases exponentially if the cause of delay isn't addressed immediately. Caching is a cost-effective method of maximizing system performance without driving up overall cost. Different types of caching are available for a number of use cases, but it's vital that IT teams know how caching works before going into the types and how beneficial these types are for their use case.
What is Caching?
Caching is a method of using main memory or RAM to efficiently manage frequently accessed data. Aside from being stored in RAM, data in the cache can also be used in correlation with a software component. The main purpose of a cache is increasing performance in data retrieval by minimizing the need to constantly access the slower disk storage layer. Compared to traditional databases, caching focuses more on speed than capacity. It stores a subset of the data transiently, instead of having data that's complete and durable. Caching, however, has a track record of success, helping companies big or small in the improvement of application performance.
How Caching Can Improve Data Governance
The main benefit of an in-memory cache is that it facilitates fast access without putting a significantly increased load on the main data stores. It improves performance and improves both the availability and scalability of applications. A cache can be applied to different use cases, including web applications, operating systems, content delivery networks (CDNs), DNS, and even databases. By improving data governance, caching helps break down an organization's data silos, providing a more centralized data architecture. This results in improved data quality, lower data management costs, and actionable insights from data that help in business decision making.
Integrating caching into these systems reduces latency and vastly improves input/output operations per second (IOPS) for read-heavy applications, including social network and media sharing systems. A cache is also valuable to recommendation engines, high-performance computing simulations, and other compute-intensive workloads because it uses an in-memory data layer to manage real-time access to large data sets across computer clusters. If this data is manipulated on disk, it is limited to the speed of the underlying hardware and can therefore cause bottlenecks and increase data movement within the network as well as to and from disk.
Depending on what a business wants to achieve, it can use different types of caching. Here are the most common use cases of in-memory caching for business.
Application Programming Interface (API)
The web applications of today are mostly built on API's, web services that can be accessed over HTTP that also expose resources that allow users to interact with the application. Having a cached result of the API will deliver optimal response because it doesn't require the API to make backend requests to a database on every request. This is especially useful if you can match the cached API response to the rate of change in the underlying data. Caching API responses lightens the workload on application servers and databases, helping you deliver faster response times and overall high performance.
An example use case for this is for COVID-19 APIs. This is a globally popular and relevant topic. The issue is that the read frequency is higher than the write frequency, which can result in stale data and updating discrepancies. By caching the response in memory, updated information can be served fast and the database can be updated asynchronously once new data is available.
The main purpose of an integrated cache is to assist the underlying database in serving responses to the inbound database request. It uses an in-memory data layer to automatically cache frequently accessed data from the origin database. One of the main characteristics of an integrated cache is its ability to keep cached data consistent with data stored on disk. Assisting the underlying database also helps dramatically increase database performance through reduced request latency and CPU and memory usage on the database engine.
An integrated cache is beneficial to websites that handle a large number of HTTP and SQL requests because it eliminates the need to make round trips to an origin server. It compares requests with already-stored policies and only forwards the request to the origin server if the response isn't found in the cache. Ideally, the majority of web content requests should be handled by the cache without needing to call on the origin server.
For websites with users in different geographical locations, replicating their entire infrastructure for each location can be wasteful, expensive, and time-consuming. The solution to the challenge of geo-dispersed web traffic is a content delivery network (CDN). With a CDN's global network of edge locations, it can facilitate the delivery of a cached copy of web content to users around the globe. It reduces response times by using the edge location closest to the originating request location, and because web assets are derived from cache, throughput is increased dramatically.
For websites that store meta information, web caching can be used so that log-in information need not be requested repeatedly. Server session information is a common type of meta information, and it's information that can be stored in the cache so it can be served faster. If the information is updated, however, the cache should also be updated.
Generally, database architectures can provide relatively good performance; however, today's modern applications often demand more. A database cache significantly improves the performance of applications by acting as a data access layer adjacent to the database. This data access layer can then be used by applications to lower data-retrieval latency while increasing throughput. Lazy loading and write-through methods are the most common ways used to load data into a database cache.
A common use case of database caching is speeding up the storage and retrieval of user profiles from a database, which usually involves data movement from server to server. With database caching, this process is only done once when data is retrieved for the first time. Succeeding requests are quicker because the profiles are stored to a server closer to the user. This significantly reduces the time required to read the profile when it is needed again.
The Right Cache for Your Business
Caching is a cost-effective solution that ensures fast response times for most businesses, but it isn't a one-size-fits-all solution. Each use case will be different and will have different data and compute requirements, so understanding the different types of caching and when and where to use each is vital. By applying the right approach according to your current and long-term business needs, you can rest assured that your applications will be efficient, light, and responsive. Caching is a prudent and practical investment, but you must also create caching policies that will help you reach your goals and push your business forward.