Simulating Materialized Views in SQLite with Triggers

Materialized views are a powerful feature in databases like PostgreSQL and Oracle. They allow you to "cache" the result of a complex query into a physical table, making subsequent reads lightning fast.

But here's the catch: SQLite doesn't strictly support CREATE MATERIALIZED VIEW.

Standard views in SQLite are "virtual tables" — every time you query them, the database re-runs the underlying query. If that query involves expensive joins or aggregations over millions of rows, your specific dashboard or report will be slow.

In this guide, we'll learn how to simulate materialized views in SQLite using Tables and Triggers. We'll build a system where a summary table updates automatically whenever the raw data changes.

The Problem: Expensive Aggregations

Imagine you run an e-commerce store. You want to show the total sales per category on your homepage.

The live query might look like this:

SELECT category, SUM(amount) as total_sales
FROM orders
GROUP BY category;

As your orders table grows to millions of rows, this query gets slower and slower. You don't want to calculate this sum every time a user visits your homepage.

The Solution: A "Materialized" Table

Instead of calculating the sum on read, we can:

1. Create a physical table orders_summary to store the results.
2. Use Triggers to keep it in sync with the orders table.

This moves the "cost" of calculation from the SELECT (read) to the INSERT/UPDATE/DELETE (write). Since reads usually outnumber writes by a huge margin, this is a massive performance win.

Interactive: Building the Triggers

Let's build this system. We need three triggers to handle:

1. New orders (INSERT) -> Add to the summary.
2. Cancelled/Changed orders (UPDATE) -> Adjust the summary.
3. Deleted orders (DELETE) -> Subtract from the summary.

How the Triggers Work

1. AFTER INSERT: We use standard INSERT ... ON CONFLICT (UPSERT) syntax. If the category doesn't exist, we create it. If it does, we just add the new amount.
2. AFTER DELETE: We simply find the matching category row and subtract the deleted amount.
3. AFTER UPDATE: This is the trickiest. To be safe, we subtract the old value from the old category and add the new value to the new category.

Pros and Cons

Feature	Standard View	Simulated Materialized View
Read Speed	Slow (Recalculates every time)	Instant (Direct table read)
Write Speed	Fast	Slower (Triggers must run)
Data Freshness	Always Real-time	Always Real-time (Transactional)
Complexity	Low	High (Need to maintain triggers)

Best Practices

1. Use Transactions: When initializing your materialized view for the first time (populating it from existing data), usage a transaction to ensure consistency.
2. Clean Up Zeroes: If a category's total sales drops to 0, you might want a trigger to delete that row to keep the summary table small.
3. Don't Over-Optimize: Only "materialize" queries that are actually causing performance bottlenecks. For small datasets (under 10k rows), SQLite is fast enough with standard standard views.

Conclusion

While SQLite lacks the CREATE MATERIALIZED VIEW command, the combination of specific Tables and Triggers gives you the exact same functionality.

By shifting the workload from read-time to write-time, you can make your analytics queries near-instant, regardless of how large your raw dataset grows.