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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 product design works at PlanetScale — PlanetScale
Jason Long · 2022-08-22 · via Blog — PlanetScale

Jason Long |

Our product design process is more lightweight and collaborative than many companies. We don’t have a rigid set of rules we follow. We don’t have product managers. We do just enough design exploration work to feel confident in the direction. Perhaps most interestingly, we don’t do design “handoffs” — we code right alongside the engineers on our teams.

Keeping planning light

Our company roadmap is determined by our leadership team working in tandem with engineering and product design. This is a mix of work that furthers the company’s vision, exposes more of the power of Vitess, and addresses customer feedback. These projects are prioritized and product design begins exploring possible solutions. For larger or less-defined features, sketches on paper or iPad are usually the most efficient way to start focusing in on the preferred direction.

Low-fidelity sketches of deploy requests and schema reverts

Low-fidelity sketches of deploy requests and schema reverts

Starting with a prototype

Once a direction starts to become clear, we move into high-fidelity mockups in Figma. Sometimes it’s helpful to experiment with a specific element in Codepen. For more intricate user flows, we’ve found it useful to build prototypes in Figma to share with team members. With Figma’s prototyping features, it’s easy to create a realistic UX complete with long-running processes, state changes, and UI transitions. Designers can even observe others while they navigate through a prototype, often revealing friction points needing smoothed out.

Early prototype of the schema revert feature

Getting to code quickly

We don’t spend time mocking up every possible UI state. If everyone involved is feeling comfortable with the core parts of the design, we move into code and continue refining the UI there. From here on out, the process is a close collaboration with the engineering team. Our product designers are able to write HTML/JSX and CSS and we can help build out basic components in our Next.js/Typescript application. We try to address all of the structural and styling pieces, leaving the engineers with something as close to fill-in-the-blanks as possible.

return (
  <>
    {true /* TODO: if anything in deploy queue */ && (
      <div className='rounded-b border-t px-3 py-2'>
        <p>
          {true /* TODO: if queue length = 1 */ && <>There is a deployment queued to deploy</>}
          {false /* TODO: if queue length > 1 */ && (
            <>There are {/* TODO: queue length */} deployments queued to deploy</>
          )}
          ({/* TODO: loop over queue, comma-separate links */}
          <Link href={/* TODO: DR link */}>
            <a>#{/* TODO: DR number */}</a>
          </Link>)
        </p>
      </div>
    )}
  </>
)

Annotating TODOs in a React component

We will often kick off feature development by adding the necessary feature flags and checks to the API and front-end. These flags allow us to enable new features for specific people and teams. Later, the entire company and early-access customers can be included before shipping to everyone.

Our employees have a high level of trust with each other and the autonomy to decide how best to approach a problem, implement a solution, and ship it. Because our product designers can code, we can avoid the standard handoff process. In our experience, this results in less friction between teams and a better product for our customers.