Cache is a critical component in modern applications, as it helps improve performance by storing frequently accessed data closer to the application. However, there are different types of cache, each with its own purpose and characteristics. In this article, we will explore a specific type of cache where the application treats the cache as the main data store.
What is Cache?
Before diving into the details of cache types, let’s first understand what cache is. In simple terms, cache is a temporary storage location that holds a copy of frequently accessed data. It helps reduce the load on the primary data source and provides faster access to data for subsequent requests.
Main Data Store vs. Cache
In most applications, the primary data store refers to a database or file system where all the data resides permanently. The application interacts with this primary data store to read or write data. On the other hand, caches are typically used as an intermediate layer between the application and the primary data store.
However, there are scenarios where an application treats cache as the main data store. This means that instead of directly accessing a database or file system, it primarily relies on cached data for read and write operations.
Advantages of Treating Cache as Main Data Store
There are several advantages to treating cache as the main data store:
- Improved Performance: Since cached data is stored in memory rather than on disk or in a remote database, accessing it is significantly faster.
- Reduced Latency: By eliminating network round-trips to fetch data from a remote source, latency can be drastically reduced.
- Better Scalability: Caches can be easily distributed across multiple nodes, allowing for horizontal scaling without impacting primary data stores.
- Less Load on Primary Data Store: By serving most of the read requests from cache, the load on the primary data store is reduced, resulting in improved overall performance.
The decision to treat cache as the main data store depends on the specific use case and requirements of an application. Here are a few scenarios where it might be suitable:
- Real-time Data Processing: Applications dealing with real-time data, such as financial systems or IoT platforms, often require low-latency access to frequently changing data. Caching this data allows for faster processing and real-time analytics.
- Content Delivery Networks (CDNs): CDNs serve static content like images, videos, and web pages from edge locations closer to end-users.
These edge locations act as caches that store frequently accessed content, reducing latency and improving content delivery speed.
- Caching Layers in Microservices: In microservices architectures, individual services can have their own caching layers. By treating these caches as the main data store for specific service-related data, performance can be optimized within each service boundary.
We have explored the concept of treating cache as the main data store in applications. While it offers several benefits such as improved performance and reduced latency, it is important to carefully consider the specific use case before implementing this approach. By leveraging cache effectively, applications can achieve faster response times and better overall scalability.