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Caching Solutions: Distributed vs. Non-Distributed

In a business environment where millions of transactions are processed daily, there is a need to complete each process within milliseconds. This extremely high throughput level cannot be achieved with traditional databases. A data processing system that is handling too much information records decreased response time due to input/output challenges.

In such a scenario, caches become one of the solution options for organizations. It offers a temporary storage solution for frequently accessed data. Data stored in the cache memory is closer to the processor and thus accessed faster compared to data stored in traditional storage.

How caching work

Data in a computer is either stored in hard disk, RAM, or cache memory. RAM and cache memory are high-speed storage layers that store recently used data nearer to the processor, which makes it easier to access it. The cache memory is closer to the processor than RAM and thus provides a faster speed. In-memory caching is a strategy where data is stored in RAM for fast retrieval.

It is a layer found between the computer database and apps for faster delivery of requests. It stores recently accessed requests temporarily. The main aim of cache is to reduce the total time spent retrieving data by incredibly increasing speed. The cache memory is much smaller and can only store a limited amount of information.

Why is caching important?

Caching is highly important because it allows users to achieve higher data performance. No user would appreciate applications or documents that take too long to open and process requests. Both developers and users want applications that perform superbly.

The high-performance solutions offered by caching is not a new concept in computer technology. More organizations are using the concept as a viable strategy for increasing e-commerce sales and revenue. It provides a better way to compete with millions of applications in the market, providing similar solutions to organizations. Caching provides multiple solutions to a company.

  • Eliminates the need to keep making new requests all the time
  • It readily avails recently requested data
  • It reduces CPU usage and network overhead
  • Helps prolong CPU and server lifespan due to limited usage
  • Reduces infrastructure cost due to reduced overall requests required
  • Offers rapid request performance

Distributed versus non-distributed cache

Distributed caching

Distributed cache is cache data that is spread across multiple clusters. The clusters can be found across multiple databases remotely located in different places around the world. Having cache data distributed in multiple locations gives organizations the advantage of horizontal scalability. It is a preferred caching strategy adopted by organizations that seek highly available data systems that meet their demand.

Distributed caching offers high scalability, availability, and excellent data tolerance, which are crucial to fast-growing online enterprises today. The companies overcome the challenge of redundancy and ensure they stay online all the time. Distributed cache aligns well with cloud computing due to its scalability and availability abilities.

Non-distributed caching

The non-distributed cache is a cache located in a single data memory within the company network. All users access it from a single source which provides several advantages to them. Since the database is only one, multiple users get a complete view of the data.

They don’t face a lot of data management challenges like in distributed cache. It is easier to create backups and update them as needs arise. One key shortcoming of a non-distributed cache is reduced productivity since multiple users are accessing it all the time.

How distributed and non-distributed data caching differs

To understand the differences better, it is important to know the main difference. A non-distributed cache is a centralized cache which means it is located in a single place. Distributed cache is located in multiple places, which can be two or more cache locations. A cache store can either be RAM or cache memory located near the CPU.

Data accessibility

Cache data stored in centralized location records reduces speed because it is accessed by multiple users. Distributed cache records super-fast access speed because each user accesses it from the location nearest to them. Distributed caching key benefits include high availability, scalability, and fault tolerance.

Data consistency

Cache data in a centralized location tends to have greater consistency because it is being retrieved from the same source. Users achieve a total view of the data. Data from a distributed system often records multiple inconsistencies because there can be chances of data replication. It requires cleaning to remove duplicates across the system, which must involve additional technologies.

Data maintenance

Data requires consistent management strategies to keep it up to date and to create backups. It is easier to perform these tasks in a non-distributed cache system since all information is accessed from one place. Distributed cache takes time to perform synchronization before updating and creating backups.

Managing downtimes and failures

There are probabilities that the system may record data failure or downtime once in a while. Data failure in a non-distributed cache is a serious issue because users cannot access it. This is different in a distributed cache because if one cluster fails, the other clusters will still be functional.

Main benefits of data caching

The key benefits of data caching are that it allows quick access and reduces loading requirements on the main databases. It enhances performance and makes data readily available and scalable. Cache data is applied to multiple use scenarios, which can include data stores, website apps, data delivery systems, and operating systems. Data silos often provide a challenge to organizations when making requests due to read/write latency.

Caching allows organizations to have more centralized data access by breaking silos into smaller clusters. The outcome is more quality data that is cheaper to maintain and analyze for effective decision-making processes. Caching is useful in many compute-intensive workloads due to its advantage of using a memory data layer and thus providing access to large data sets in real time.


Distributed cache has a wide range of advantages over non-distributed cache. The concept is significantly gaining popularity in recent times due to its effectiveness in data management processes. In a microservice applications environment, it addresses limitations experienced in use cases such as maintenance, performance, and reducing operational costs. It automatically syncs with hybrid cloud systems to always avail the latest data to users.

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