Caching is a high-speed data storage layer that stores a subset of data, typically transient in nature, to serve future requests for that data more quickly than accessing the primary storage location. This allows for efficient reuse of previously retrieved or computed data, significantly enhancing data retrieval performance.

How Does Caching Work?

Data in a cache is generally stored in fast-access hardware such as Random-access memory (RAM) and may also work in conjunction with software components. The primary purpose of caching is to increase data retrieval performance by reducing the need to access the slower underlying storage layer.

  • Speed vs. Capacity: Caches trade off capacity for speed, storing a smaller subset of data temporarily, unlike databases that store complete and durable data sets.

Caching Overview

RAM and In-Memory Engines

Caches utilize RAM and in-memory engines due to their high Input/Output operations per second (IOPS). This results in improved data retrieval performance and cost savings at scale compared to traditional disk-based storage. Supporting the same scale with disk-based systems would require more resources and still fail to achieve the low latency provided by in-memory caches.

Applications of Caching

Caching can be applied across various layers of technology, including:

  • Operating Systems: Enhancing system performance by storing frequently accessed data.
  • Networking Layers: Including Content Delivery Networks (CDNs) and DNS for faster domain resolution.
  • Web Applications: Reducing latency by caching web artifacts such as HTML, JavaScript, and images.
  • Databases: Improving query performance by caching frequently accessed data.

Design Patterns in Caching

In distributed computing environments, a dedicated caching layer allows systems and applications to operate independently from the cache, with their own lifecycles, without risking cache integrity. This central cache layer can be accessed by disparate systems, providing a consistent data source even when application nodes scale dynamically.

Caching Best Practices

  • Data Validity: Ensure the validity of the data being cached to achieve a high cache hit rate.
  • TTL (Time to Live): Implement TTL controls to expire data accordingly.
  • High Availability: Use highly available in-memory engines like Redis to ensure continuous data availability.
  • RTO and RPO: Define appropriate Recovery Time Objective (RTO) and Recovery Point Objective (RPO) for data stored in the cache.

Benefits of Caching

Improve Application Performance

Memory access is much faster than disk access. Reading data from an in-memory cache is sub-millisecond, significantly improving application performance.

Reduce Database Cost

A single cache instance can handle hundreds of thousands of IOPS, potentially reducing the need for multiple database instances and lowering overall costs.

Reduce Backend Load

Caching reduces the load on backend databases by redirecting read requests to the in-memory layer, preventing performance degradation or crashes during traffic spikes.

Predictable Performance

By handling traffic spikes efficiently, caching ensures consistent application performance even during high-demand periods like Black Friday or major events.

Eliminate Database Hotspots

Caching frequently accessed data prevents database hotspots and avoids overprovisioning of database resources, maintaining fast and predictable performance.

Increase Read Throughput

In-memory systems offer much higher request rates compared to disk-based databases, supporting hundreds of thousands of requests per second.

Use Cases & Industries

Database Caching

Database caching dramatically increases throughput and lowers data retrieval latency, improving overall application performance. Techniques like lazy loading and write-through methods are commonly used.

Content Delivery Networks (CDN)

CDNs utilize a global network of edge locations to deliver cached web content, reducing response times and increasing throughput. CDNs like Amazon CloudFront integrate with other services to accelerate content delivery.

DNS Caching

DNS caching stores domain request data closer to the client, improving query resolution times and reducing load on upstream servers.

Session Management

Centralized session management data stores ensure consistent user experiences across web servers, better session durability, and higher availability.

Application Programming Interfaces (APIs)

Caching API responses can optimize performance by reducing backend load and improving response times, especially for APIs serving static or infrequently changing data.

Caching for Hybrid Environments

Caching on-premises data in the cloud can optimize data retrieval performance in hybrid cloud environments, reducing latency and improving efficiency.

Web Caching

Web caching reduces latency and server load by caching web artifacts. Server-side caching typically involves web proxies, while client-side caching can include browser-based caching.

General Cache

Using an in-memory key-value store as a standalone database can build highly performant applications, benefiting from high throughput and cost-effectiveness.

Integrated Cache

Integrated caches automatically cache frequently accessed data from the origin database, enhancing performance by reducing latency and resource utilization.

By implementing effective caching strategies, you can significantly enhance application performance, reduce costs, and ensure reliable and scalable systems.

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Last updated 17 Aug 2024, 12:31 +0200 . history