What Is Caching?

Caching, often referred to as memory caching, is a technique that allows computer applications to temporarily store data in a computer's memory for quick retrieval. The temporary storage area for this data is known as the cache. Caching frequently accessed data makes applications more efficient. Memory retrieval is much faster than accessing solid-state drives (SSDs), hard disk drives (HDDs), or network resources.

Caching is especially efficient when the application exhibits a common pattern of repeatedly accessing data that was previously accessed. It's also useful for storing data calculations that are otherwise time-consuming. By storing the calculations in a cache, the system saves time by avoiding the repetition of the calculation.

Memory caching works by first setting aside a portion of RAM to be used as the cache. As an application tries to read data, typically from a data storage system like a database, it checks to see if the desired record already exists in the cache. If it does, the application will read the data from the cache, thus eliminating the slower access to the database. If the desired record is not in the cache, the application reads the record from the source instead. It retrieves data and stores it in the cache, allowing the application to quickly access it in the future.

Cache size is limited, so some existing data must be removed to make room for new data the application has recently accessed. This requires the cache system to have a strategy for selecting which records to remove. The strategy depends on data access patterns and focuses on removing rarely accessed records.

Caching Strategies

For example, a least-recently-used (LRU) strategy removes the record that has not been accessed for the longest time compared to others in the cache. The underlying assumption is that if a record hasn’t been accessed in a while, it is less likely to be accessed again soon. In other words, recent records will likely be used again soon.

On the other hand, a least-frequently-used (LFU) strategy involves tracking how many times each record in the cache has been accessed. Under this strategy, the record with the fewest accesses is removed. The assumption is that a record used infrequently will unlikely be accessed again soon.

Read more about caching strategies »

Dealing With Cache Misses

The challenge with caches is minimizing “cache misses,” i.e., attempted reads by the application for records that are not in the cache. If you have too many misses, the efficiency of your cache decreases. An application that only reads new data does not benefit from a cache. It may perform worse because it has to check the cache and often won't find the desired record. One way this challenge can be mitigated is by leveraging larger caches. Using a single computer is often impractical for handling larger data sets, so developers prefer distributed caches to speed up applications.

A distributed cache pools together the RAM of multiple computers connected in a cluster, allowing you to create a bigger cache that can continue to grow by adding more computers to the cluster. Technologies like Hazelcast Platform can be used as a distributed cluster to accelerate large-scale applications.

Handling Stale Data

Another challenge of caches is the risk of reading “stale” data, in which the data in the cache does not reflect the latest data in the underlying source. Often, this risk is an acceptable trade-off for the sake of application performance. In cases where it is not, it is up to the application to update the underlying data source to update the record in question in the cache.

CPU Cache

A small amount of memory is built into a computer’s central processing unit (CPU) that stores frequently accessed data and instructions. The CPU cache makes it faster for the CPU to get to data and instructions, so it doesn’t have to go to the slower main memory or storage devices as often.

Memory Cache

Memory caching is a small portion of main memory (RAM) set aside as a temporary storage area for frequently accessed data. It helps to improve application performance by reducing the time it takes to access data from slower storage media like hard disk drives or networks.

Disk Cache

This is a portion of main memory (RAM) used to store data that has recently been read from or written to a disk, such as a hard disk drive or solid-state drive. Disk caching helps reduce the number of read and write operations on the disk, improving the overall system performance.

Browser Cache

A web browser's cache is a temporary storage area for web content, such as HTML pages, images, and other media. When a user visits a webpage, their browser stores a copy of its content in the cache. When the user revisits the same webpage, the browser can load the content from the cache rather than downloading it again, improving the page’s loading time.

Distributed Cache

A distributed cache is a shared resource used by multiple computers within a network, designed to store frequently accessed data across various servers. By reducing the need to retrieve data from multiple servers, distributed caching enhances the performance of distributed systems. Additionally, it improves an application's scalability, as data can be cached in different locations. This setup allows more concurrent users to access the data with fewer requests.

Caching contributes significantly to a computer system’s overall performance by reducing the time it takes to access data and instructions. When a computer program needs to access data, it first checks the cache to see if the data is already there. If it is, the program can access the data from the cache much faster than it can access data from slower storage media, such as hard disk drives or networks. This speeds up the program’s execution and improves the system’s overall performance.

Overall system performance can be improved by reducing the workload on slower storage devices and networks and improving individual program performance. The system caches frequently accessed data to minimize read and write operations, allowing devices to manage other tasks more efficiently. This may result in better system performance and responsiveness.

Caching in Distributed Systems

Caching can greatly influence the performance of distributed systems using cache architectures, such as distributed hash tables (DHTs). By minimizing the time required to access data stored across multiple servers, caching improves the overall efficiency of these systems.

Caching is an essential technique for boosting computer system performance. It decreases the time needed to access data and instructions while reducing the load on slower storage devices and networks.

Caching can be leveraged in a variety of specific use cases such as:

• Database acceleration
• Query acceleration
• Web/mobile application acceleration
• Cache-as-a-service
• Web caching
• Content delivery network (CDN) caching
• Session management
• Microservices caching

View Hazelcast caching use cases >

Limitations of Data Caching for Contextual Insight

There is a data visibility limitation regarding caching, meaning it does not create any context for your data. This is because caches are essentially storage units from which you can retrieve data, so they are perfect for spitting out the raw data that you stored within them but not for giving you any background for that data.

The Challenge of Distributed Data Systems

Your data is probably scattered across multiple backend systems. You need a solution that can provide context for the different data types. This will help you understand it better, especially when you need more information beyond what is already in the cache.

The Need for Real-Time Insights Beyond Caching

While caches provide quick access to data, they do not allow for real-time insights as events occur. This is where stream processing comes into play. As time passes, the value of your data decreases because you spend time collecting, combining, and analyzing it—often using various applications beyond the cache—instead of using that time more productively.

Limitations in Data Processing and Analysis with Caches

Unfortunately, caches can only retrieve stored information and cannot manipulate, organize, calculate, or analyze data. For your company, this means missed revenue opportunities that can easily slip away within your technology infrastructure. The delay caused by retrieving data from the cache and processing it through multiple backend systems results in a significant loss of actionable information.

The Issue of Disconnected Data Across Systems

Relying solely on simple caches creates fragmented and inconsistent data presentation for the company. Retrieving data from diverse backend systems in various formats and languages poses a challenge. Caches offer no standardization when delivering data to end users and applications.

Connecting Siloed Data for Advanced Analysis

When your company possesses many unique sources and databases of data, establishing connections between siloed data sets is crucial for advanced information analysis.