Distributed Caching :


3 min read

In this blog, I will discuss what is distributed caching and why we need distributed caching. In a distributed environment it is always recommended to have a layer for cache. Later in this article, I will discuss the key points of using cache. But let’s first understand what is cache and why exactly we need a cache.

What is Cache: Introduction

The cache is typically stored in RAM which facilitates a much faster access of data, eventually increases throughput and reduces latency. The cache is like an in-memory data store. And as we all know, reads on the ram are much faster than sequential reads and writes to a spinning hard drive. Now that we have understood what is caching let's look at distributed caching.

Distributed Caching

A distributed cache can have its data spread in a cluster, spanned across several clusters, across data centers around the world.

This approach offers several benefits:

Improved Performance: Distributed caching can significantly improve the performance of applications by reducing the latency associated with retrieving data from a remote source or performing expensive computations. Cached data can be quickly accessed from nearby nodes, leading to faster response times.

Scalability: As the demand for cached data increases, distributed caching systems can scale horizontally by adding more nodes to the cluster. This allows the system to handle larger workloads without significant degradation in performance.

High Availability: Distributed caching systems often provide mechanisms for data replication and fault tolerance. If one node fails, the data can still be retrieved from other nodes in the cluster, ensuring high availability.

Reduced Load on Backends: By offloading requests from backend databases or services, distributed caching can help reduce the load on these resources, freeing them up to handle more complex tasks.

Data Consistency: Distributed caching systems may implement strategies for maintaining data consistency, ensuring that cached data remains up-to-date and accurate. Techniques like cache invalidation, time-based expiration, or event-based updates can be used to manage consistency.

Content Delivery: Distributed caching can be used for content delivery networks (CDNs) to efficiently distribute and deliver static content, such as images, videos, and web pages, to users around the world.

Popular distributed caching solutions include:

Memcached: An open-source, high-performance distributed memory caching system that is often used to accelerate dynamic web applications.

Redis: An open-source, in-memory data store that can be used as a distributed cache, among other use cases. It supports various data structures and provides advanced features like persistence and publish-subscribe messaging.

Hazelcast: An open-source in-memory data grid that provides distributed caching, computation, and clustering capabilities.

Conclusion :

Distributed caching requires careful consideration of data consistency, cache eviction policies, and cluster management. It is particularly valuable in scenarios where low-latency data access and high performance are critical, such as web applications, e-commerce platforms, real-time analytics, and gaming systems.