When working with JSON columns, one of the most important considerations is access patterns. If our posts table contains millions of rows, certain search queries—especially those not using an index—can become performance bottlenecks.

Although SQLite does not support generic indexing of entire JSON columns, we can create specific indexes for subsets of JSON data that match our expected access patterns. Let’s explore how to do this.

Using Virtual Columns to Index JSON Data

Jumping back into our playground, we still have our posts table with a metadata JSON column. However, this time, I’ve added a virtual column for word_count.

• This word_count column extracts the word_count value from the JSON content.
• It is cast as an integer for proper indexing.
• Defining it as a stored column allows us to create an index on it.

Why Use a Stored Virtual Column?

By using stored virtual columns, we can extract and index specific values inside JSON payloads, making queries much faster. Let’s examine the impact of indexing on query performance.

Comparing Query Performance: JSON Search vs. Indexed Virtual Column

1. Searching JSON Data Without an Index

If we run a query to find posts where word_count is greater than 1,000 directly in the JSON column:

Post.where("metadata ->> '$.content.word_count' > ?", 1000).explain

This means SQLite scans the entire table, which is extremely inefficient for large datasets. If we have millions of rows, this approach can become a serious performance bottleneck.

2. Searching Using an Indexed Virtual Column

Now, let’s run the same query using our stored virtual column:

Post.where("word_count > ?", 1000).explain

Since we created an index on word_count, SQLite uses the index instead of scanning the entire table, significantly improving query speed and efficiency.

Balancing JSON Storage and Indexed Reads

JSON columns are great for storing flexible data structures without needing multiple tables. Virtual columns allow us to extract specific values from JSON data and index them. Using indexes on frequently queried JSON values improves performance, reducing the need for full-table scans.

However, JSON columns should only be used for data that is written once and read many times. If frequent updates are required, a separate table is often the better choice, as SQLite is optimized for structured relational data.

In our final video in this module, we’ll explore how to use SQLite’s aggregate table functions with JSON to perform advanced queries efficiently.