5 Strategies for Read-Heavy Systems

Build a high-performance system

Saurabh Dashora

Apr 30, 2024

There’s a higher probability of you working on a read-heavy system.

Why?

Because read-heavy systems are far more common than write-heavy systems. Even as a user, you see them all the time.

Customers view dozens of products on websites like Amazon before actually buying one and triggering the write requests.
Most users scroll through hundreds of social media posts every day and post only one or two. Some don’t post at all.

In this post, I’ll discuss 5 strategies to build high-performance read-heavy systems:

1 - Perform Database Indexing

Indexing your database tables is the absolute must-do activity for optimizing reads.

With proper indexes that are tailored to the common query patterns of your application, your queries will be a lot more efficient and fast.

Moreover, indexing can reduce the need for other strategies for a long time.

You can play around with the diagram on Eraser.io

2 - Database Replication with Read Replicas

Database replication is a technique in which you replicate data from the main database to other secondary databases.

These replicas are copies of the primary database that can handle read requests.

With more replicas, you can distribute read requests to improve the performance of the reads. It also increases the durability of the data.

However, you need to deal with scenarios related to replication lag and eventual consistency in the replicas.

3 - Caching

Caching is a great choice for read-heavy systems where the data doesn’t change much after it’s created.

With a good caching strategy, you can significantly reduce the load on your database.

4 - Content Delivery Networks

While caching is good, you can also take it to the next level by investing in a Content Delivery Network or CDN.

With a CDN, you can cache and serve static content closer to the end users and reduce read latency. You can also support the geographic distribution of data with a CDN.

5 - Load Balancing

While it’s possible to spin up multiple replicas, cache instances, or servers to handle the reads, you also need to ensure fair distribution of traffic to these instances.

Using a load balancer is the key to implementing an efficient distribution of workload.

Plus, it also increases the availability of your system with health checks that ensure that traffic is routed only to healthy instances.

So - which other strategies do you think can help with read-heavy systems?

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