Strategies to Scale Database Reads

In the previous article, we understood a fundamental truth:

Fast reads require data to be highly organized.

But in real systems, read load keeps increasing:

• More users
• More dashboards
• More APIs
• More analytics
• More features querying the same data

Very quickly, the database becomes read-bound.

The CPU is not the problem.
The disk is not the problem.
The problem is that too many queries are asking the database to find and organize data again and again.

The Core Idea Behind Scaling Reads

A read becomes slow when the database has to:

• Search large data
• Join multiple tables
• Compute aggregates
• Reconstruct data repeatedly

So the idea behind scaling reads is simple:

Don’t make the database do the same work repeatedly.

Either:

• Store data in a way that is already easy to read
• Or avoid going to the database at all

Every technique below follows one of these two ideas.

1. Read Replicas

One of the simplest ways to scale reads is to duplicate the database.

A primary database handles writes.
Multiple replicas handle reads.

Now instead of one machine serving all read queries, many machines share the load.

Why this works (theoretical view):

Reads are independent operations. They can be parallelized easily.

If one database can serve R reads/sec, then N replicas can serve roughly N × R reads/sec.

You are scaling reads horizontally without changing storage design.

2. Caching (Redis / Memcached)

Many queries ask for the same data repeatedly:

• User profile
• Product details
• Configuration data

Instead of hitting the database each time, store the result in a fast in-memory cache.

Why this works:

Memory access is thousands of times faster than disk access.

You are not optimizing the read.
You are avoiding the read.

3. CDN for Static or Semi-Static Data

Some data rarely changes:

• Images
• Static JSON responses
• Public content

Serving this from a CDN removes the database from the path entirely.

Why this works:

The best way to scale a read is to make sure the database is never involved.

4. Materialized Views

Some queries are expensive because they:

• Join many tables
• Compute aggregates
• Filter large datasets

A materialized view stores the result of that query as a table.

Now the database does not recompute the result every time.

Why this works:

You convert:

Expensive read computation → Cheap lookup

The work is shifted to writes or periodic refresh.

5. Denormalization

Normalized schemas are good for writes and consistency.

But reads often need data from multiple tables.

Denormalization stores related data together so reads don’t need joins.

Why this works:

Joins are expensive because the database must reconstruct relationships at read time.

Denormalization does that work earlier.

6. Covering Indexes

A covering index contains all the columns required by a query.

This allows the database to answer the query from the index itself without touching the main table.

Why this works:

Indexes are smaller and already sorted.

The database avoids extra disk access.

7. In-Memory Hot Data

Frequently accessed data (hot data) can be stored entirely in memory.

This can be done using in-memory databases or memory tables.

Why this works:

The database avoids disk I/O for the most common reads.

The Pattern Behind All These Techniques

All these strategies follow a common principle:

Make reads cheaper by doing the work earlier, elsewhere, or only once.

You either:

• Duplicate data
• Store precomputed results
• Keep data in memory
• Avoid the database entirely

Final Thought

Scaling reads is not about making queries faster.

It is about making sure the database does not have to work hard for every query.

You achieve this by:

• Replicating data
• Caching results
• Precomputing answers
• Storing data in read-friendly forms