How do I reduce MySQL compute costs?
On MySQL the highest-return lever is indexes and avoiding full table scans, since compute is the instance. After that, stop full-refreshing: switch heavy assets to INSERT ... ON DUPLICATE KEY UPDATE so each run moves only what changed, and apply covering indexes on your filter and join columns. Bruin helps on the second and third of those, because incremental strategy is a property of the asset definition rather than something you hand-write per table. The tool licence is rarely the biggest line on the bill.
Command
bruin runDefined in
SQL + YAML
Works with
MySQL + Bruin CLI
What you get
How to do it
- 1
Measure first: find the MySQL jobs that dominate spend before changing anything.
- 2
Apply indexes and avoiding full table scans, since compute is the instance.
- 3
Convert the largest full-refresh assets to INSERT ... ON DUPLICATE KEY UPDATE.
- 4
Apply covering indexes on your filter and join columns to the tables that dominate scan volume.
- 5
Re-measure and confirm the change actually moved the bill.
How it works in code
/* @bruin
name: mart.orders
materialization:
type: table
strategy: merge
incremental_key: updated_at
@bruin */Run bruin run and Bruin moves only changed rows on MySQL instead of rebuilding the table.
Worth knowing
On MySQL, it is a row store, so wide aggregate scans are slow no matter how you tune them Measure before and after: cost work done on intuition usually optimises the wrong job.
Other ways to do this
Bruin is not always the right answer. Here is where the alternatives are stronger.
| Option | When it is the better choice |
|---|---|
| Bruin | Practical levers for cutting compute spend on MySQL. |
| Native MySQL cost tooling | Use it. MySQL's own usage reporting is the right place to find out where the money actually goes before changing any tool. |
| dbt incremental models | The same incremental savings if dbt is already your transformation layer on MySQL. No reason to migrate for this alone. |
| A cost-observability vendor | Worth it once spend is large enough that attribution across teams is the hard part rather than the optimisation itself. |
Common questions
How do I reduce MySQL compute costs?
Start with indexes and avoiding full table scans, since compute is the instance, then convert full refreshes to INSERT ... ON DUPLICATE KEY UPDATE, then apply covering indexes on your filter and join columns.
Is a cheaper tool the way to cut MySQL costs?
Usually not. Warehouse compute is normally the largest line and the most reducible. Licence savings matter, but far less than how often you rebuild tables and how much data each query reads.
What is the cheapest stack around MySQL?
One with no per-seat and no per-row licence in it: open-source ingestion, open-source transformation, and your CI runner as the scheduler. That leaves warehouse compute as the only real bill.
Fewer tools, a smaller bill
Open source. No per-seat and no per-row fee, so the bill is warehouse compute.