ClickHouse TypeScript
ClickHouse with TypeScript
The annoying part of using ClickHouse from TypeScript is not writing SQL. It is keeping runtime types, query code, and API surfaces aligned as the codebase grows. hypequery gives you generated schema types and a reusable query layer so that work does not keep getting rebuilt by hand.
Core benefit
Generated schema types
Execution model
Local or HTTP
Best fit
Analytics-heavy TypeScript apps
ClickHouse types do not map cleanly to JavaScript
DateTime values come back as strings, UInt64 values often need to stay strings, and Nullable columns return null. If you hand-write TypeScript interfaces, TypeScript trusts the wrong types.
Raw SQL strings do not scale across a product codebase
The same analytics query ends up duplicated in API routes, background jobs, dashboards, and internal tools. Refactors become fragile because the query contract is spread across string literals.
Most teams end up rebuilding the same analytics layer
Once a ClickHouse project grows, teams want generated schema types, reusable query definitions, typed APIs, and a clean way to share analytics logic between backend and frontend.
How hypequery helps
A ClickHouse TypeScript workflow built for reusable analytics
Instead of hand-writing TypeScript interfaces and copying SQL into route handlers, jobs, and dashboards, you generate schema types from ClickHouse and define the query once. That definition can run locally or be served over HTTP without drifting from the source query.
- Generate TypeScript schema types from your live ClickHouse database
- Build type-safe ClickHouse queries without hand-maintained interfaces
- Reuse the same analytics definitions across APIs, jobs, and dashboards
- Expose typed HTTP endpoints with request and response schemas
- Keep ClickHouse runtime types aligned with your TypeScript codebase
Step 1
Generate schema types from ClickHouse
This is where the ClickHouse TypeScript workflow becomes reliable. Runtime types like `DateTime`, `UInt64`, `Nullable`, and `Decimal` get mapped correctly instead of being guessed.
Step 2
Define typed ClickHouse queries once
hypequery lets you model analytics queries in TypeScript and reuse them across product surfaces. That means the same query definition can power a dashboard, an API route, a job, or an internal tool without duplicating logic.
This is the difference between “ClickHouse client in TypeScript” and a real ClickHouse analytics backend in TypeScript. You are not just running queries. You are defining typed contracts that can be reused safely.
If you want the shortest path to a working setup, skip the comparison content and start with hypequery now. One real query against your own schema will tell you more than another hour of reading.
If your team is comparing options, read hypequery vs @clickhouse/client, hypequery vs Kysely, and Drizzle ORM for ClickHouse after this page.
Why teams search for this
Common ClickHouse TypeScript problems this page should solve
ClickHouse query builder for TypeScript
If you want a ClickHouse query builder with real TypeScript support, the key question is not just syntax. It is whether the builder understands ClickHouse runtime types and can be reused across your app.
ClickHouse types in TypeScript
DateTime, UInt64, Decimal, and Nullable columns are where most silent bugs start. If that is your main pain point, read the type problem guide.
Reusable analytics APIs in TypeScript
Teams often start with raw queries and then realize they need typed APIs, internal analytics services, or OpenAPI docs on top. hypequery is optimized for that step-up in complexity.
Alternatives to hand-written query types
Hand-maintained interfaces drift. Generated schema types plus typed query definitions remove that drift from the system instead of relying on discipline alone.
How do I use ClickHouse with TypeScript?
Start by introspecting the live schema. Generate TypeScript types from that schema. Build queries against those types. Then decide where the query needs to run: inline, behind an endpoint, or in a dashboard. That is the workflow hypequery makes easier.
Further reading
Compare approaches and go deeper
The ClickHouse TypeScript type problem
A deep dive into the runtime type mismatches that bite TypeScript teams on ClickHouse.
Open article
hypequery vs @clickhouse/client
What you actually gain when you move from raw queries to generated schema types and reusable query definitions.
Open article
hypequery vs Kysely
Where Kysely is excellent, where ClickHouse changes the tradeoffs, and when hypequery is the better fit.
Open article
Drizzle ORM for ClickHouse
For teams searching for Drizzle ORM support on ClickHouse and the closest TypeScript-native alternative.
Open article
ClickHouse Next.js
How to reuse typed analytics queries across App Router handlers and server components.
ClickHouse React
How to turn a typed analytics API into React hooks for interactive dashboards.
ClickHouse Analytics
Why schema-driven query definitions become an analytics layer, not just a query builder.
ClickHouse Multi-Tenant Analytics
How to apply tenant isolation when ClickHouse powers customer-facing analytics features.
Next step
Start with hypequery on one real ClickHouse query
Generate schema types, define your first typed query, and prove the workflow on your actual ClickHouse schema before expanding further.