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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? — PlanetScale Introducing Insights Anomalies — PlanetScale Webhook security: a hands-on guide — PlanetScale Announcing the Fivetran integration — PlanetScale Introducing webhooks — PlanetScale What is MySQL replication and when should you use it? — PlanetScale Sync user data between Clerk and a PlanetScale MySQL database — PlanetScale Introducing database reports — PlanetScale Distributed caching systems and MySQL — PlanetScale What is MySQL partitioning? — PlanetScale MySQL High Availability: Connection handling and concurrency — PlanetScale Personalizing your onboarding with Markdoc — PlanetScale PlanetScale vs. Amazon Aurora — PlanetScale PlanetScale vs. Amazon RDS — PlanetScale PlanetScale is bringing vector search and storage to MySQL — PlanetScale
PlanetScale migrates open source Vitess test suite from Python to Go — PlanetScale
Deepthi Sigireddi · 2020-03-20 · via Blog — PlanetScale

Deepthi Sigireddi |

Over the last three quarters, the team at PlanetScale has focused on the dual goals of making open source Vitess easy to use and easy to contribute to. A part of this effort was a migration of all the integration tests written in Python to Go.

There were several reasons for this project:

  • The Python tests were very time-consuming to develop and debug.
  • The Python tests added additional install dependencies for anyone getting started as a contributor.
  • Support for the Python version being used (2.7) ended on January 1, 2020.

This was a fairly massive project that required several people working on it for almost four months. The project was started around November 1, 2019 and completed on February 25, 2020. There were 197 separate integration tests in 39 files that had to be migrated. In terms of LOC, it was over 24,000 lines of Python code.

In order to accomplish the migration, we first built a test framework in Go (using the command and testing packages) that allowed us to start a Vitess cluster and interact with it programmatically. The framework had to support running multiple tests in parallel without port conflicts; create non-conflicting working directories for all the relevant processes; log sufficient information to enable failure diagnosis, etc. Once that was done, it was a matter of translating Python tests into the equivalent Go code.

Along the way, we were also able to improve the CI pipeline for Vitess. While Travis CI has served us well over the years, we saw an opportunity to switch to GitHub actions. The advantages?

  • Larger compute+memory instance types. While Travis CI (and Circle CI for that matter) will provide you with larger instances on paid plans, we really wanted to stay within the free tier so that contributors could run with the same technologies and experience as the core project. Larger sizes are important for Vitess, since the test suite can launch 6 or more instances of mysqld.
  • No limit of 5 concurrent jobs. We were using Travis matrix builds for a purpose they weren’t designed for — to split 2 hrs and 30 minutes of testing into 5 “shards” of 30 minutes. That meant that we could only effectively have one concurrent job, and during peak periods there could be a delay of an hour or more to have test suite results. Our new GitHub actions configuration still uses shards, but now with over 14 of them. We are also no longer blocked by other developers running CI tasks at the same time.

The end result of the project is that it is now much easier and faster to develop new integration tests. It is also easier for someone new to the project to get started. The CI changes give us quicker feedback on pull requests and increase throughput on pull requests.

To learn more, join the Vitess Slack channel and attend the next monthly open meeting on Thursday, March 19 to hear Arindam Nayak talk about this project in detail. The meeting details can be found in the Vitess Slack channel.