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Blog — PlanetScale

Keeping a Postgres queue healthy — PlanetScale Patterns for Postgres Traffic Control — PlanetScale Graceful degradation in Postgres — PlanetScale High memory usage in Postgres is good, actually — PlanetScale Stripe Projects partnership: Provision PlanetScale Postgres and MySQL databases from the Stripe CLI — PlanetScale Enhanced tagging in Postgres Query Insights — PlanetScale Behind the scenes: How Database Traffic Control works — PlanetScale Introducing Database Traffic Control — PlanetScale Scaling Postgres connections with PgBouncer — PlanetScale Drizzle joins PlanetScale — PlanetScale Video Conferencing with Postgres — PlanetScale Faster PlanetScale Postgres connections with Cloudflare Hyperdrive — PlanetScale Introducing the PlanetScale MCP server — PlanetScale Database Transactions — PlanetScale Automating our changelog with Cursor commands — PlanetScale Postgres 18 is now available — PlanetScale Using MotherDuck with PlanetScale — PlanetScale $50 PlanetScale Metal is GA for Postgres — PlanetScale AI-Powered Postgres index suggestions — PlanetScale $5 PlanetScale is live — PlanetScale Announcing Vitess 23 — PlanetScale $50 PlanetScale Metal — PlanetScale Report on our investigation of the 2025-10-20 incident in AWS us-east-1 — PlanetScale $5 PlanetScale — PlanetScale Benchmarking Postgres 17 vs 18 — PlanetScale Larger than RAM Vector Indexes for Relational Databases — PlanetScale Partnering with Cloudflare to bring you the fastest globally distributed applications — PlanetScale Processes and Threads — PlanetScale PlanetScale for Postgres is now GA — PlanetScale Postgres High Availability with CDC — PlanetScale Announcing Neki — PlanetScale Caching — PlanetScale The principles of extreme fault tolerance — PlanetScale Announcing PlanetScale for Postgres — PlanetScale Benchmarking Postgres — PlanetScale Announcing Vitess 22 — PlanetScale The Real Failure Rate of EBS — PlanetScale IO devices and latency — PlanetScale Announcing PlanetScale Metal — PlanetScale PlanetScale Metal: There’s no replacement for displacement — PlanetScale Upgrading Query Insights to Metal — PlanetScale Automating cherry-picks between OSS and private forks — PlanetScale Database Sharding — PlanetScale Anatomy of a Throttler, part 3 — PlanetScale Introducing sharding on PlanetScale with workflows — PlanetScale Announcing Vitess 21 — PlanetScale Announcing the PlanetScale vectors public beta — PlanetScale Anatomy of a Throttler, part 2 — PlanetScale Instant deploy requests — PlanetScale Anatomy of a Throttler, part 1 — PlanetScale Increase IOPS and throughput with sharding — PlanetScale Tracking index usage with Insights — PlanetScale Faster backups with sharding — PlanetScale Building data pipelines with Vitess — PlanetScale The State of Online Schema Migrations in MySQL — PlanetScale Optimizing aggregation in the Vitess query planner — PlanetScale Dealing with large tables — PlanetScale Announcing Vitess 20 — PlanetScale Self-managed Vitess vs Managed Vitess with PlanetScale — PlanetScale Achieving data consistency with the consistent lookup Vindex — PlanetScale The MySQL adaptive hash index — PlanetScale Introducing global replica credentials — PlanetScale Profiling memory usage in MySQL — PlanetScale Summer 2023: Fuzzing Vitess at PlanetScale — PlanetScale How PlanetScale makes schema changes — PlanetScale Identifying and profiling problematic MySQL queries — PlanetScale The Problem with Using a UUID Primary Key in MySQL — PlanetScale Announcing Vitess 19 — PlanetScale PlanetScale forever — PlanetScale Introducing schema recommendations — PlanetScale Amazon Aurora Pricing: The many surprising costs of running an Aurora database — PlanetScale Three common MySQL database design mistakes — PlanetScale OAuth applications are now available to everyone — PlanetScale Deprecating the Scaler plan — PlanetScale PlanetScale branching vs. Amazon Aurora blue/green deployments — PlanetScale Databases at scale — PlanetScale Considerations for building a database disaster recovery plan — PlanetScale Working with Geospatial Features in MySQL — PlanetScale PlanetScale vs Amazon Aurora replication — PlanetScale Introducing the Vantage and PlanetScale integration — PlanetScale MySQL isolation levels and how they work — PlanetScale Introducing the schemadiff command line tool — PlanetScale $ pscale ping — PlanetScale Announcing foreign key constraints support — PlanetScale The challenges of supporting foreign key constraints — PlanetScale What is HTAP? 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How our Rails test suite runs in 1 minute on Buildkite — PlanetScale
Mike Coutermarsh · 2022-01-18 · via Blog — PlanetScale

Mike Coutermarsh |

At PlanetScale, our backend API is built with Ruby on Rails. It’s a pretty standard Rails application. We use minitest for our test suite and FactoryBot for creating test data.

Everyone on our team has worked in the past on Rails applications with slow test suites and knew how much it hurts team productivity. As our app has grown, we have continually invested time into keeping our test suite fast. We know how much a quick feedback cycle pays off for our team and a little extra work on it makes every feature we build easier.

Local development

We never run all of our application’s tests in local development. It’s not a good use of time and will never be as fast as running them on CI. When working locally, we’ll run the tests for the single file we modified, or just a single test at a time. Then we push the commit and get feedback for the whole test suite quickly.

Our whole test suite locally takes around 12 minutes running serially on a MacBook Pro. We haven’t put much effort here because it’s not something our engineers ever run.

Parallel Tests on CI

Rails now can run tests in parallel with minitest. If you’re using another test framework, various gems enable this as well.

This had the biggest impact and is also the easiest step to improve your test suite speed. When we initially started this, we began by running our tests in parallel on 2 workers. You’re limited by the number of cores the machine you’re running on has.

This gave us some speed gains, but we wanted it really fast. Our infrastructure team set us up with some 64 core machines on Buildkite.

# Only run in parallel on CI
if ENV["CI"]
  parallelize(workers: 64)
end

After this change, our test suite ran in around 3-4 minutes. We clearly still had some issues to figure out. The next step was improving the tests themselves.

Auditing FactoryBot

After a bit of digging, we noticed most of our test time was spent setting up test data. We use FactoryBot for this.

We began investigating this by putting a debugger in our tests to stop execution right after the test setup. We used pry here to look around at all the objects created and see if they matched our expectations. We found a few surprising places where we were creating up to 8× as many objects as we thought we were.

This is a common mistake in FactoryBot. The library makes it so easy to set up relationships between data that it’s possible to trigger the creation of more associated objects than you expect.

Fixing our Factories

Solving this was more straightforward once we knew the problem. We set up tests with our expectations for the amount of data our factories should create.

test "factory doesn’t create tons of databases" do
  create(:database)
  assert_equal 1, Database.count
end

These tests failed at first, but we worked through the factories and eventually got them down to creating the correct number of objects.

This gave us another huge gain, and after a few of these changes, the test run time dropped to ~1 minute.

We keep these tests in our models, protecting us from any regressions when making changes to our factories.

Read more