Building a Real-Time ClickHouse Dashboard with React and hypequery
A full walkthrough — schema generation, typed query endpoints, React hooks, and a polling dashboard that stays live without WebSockets.
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Deep dives, architecture notes, and product guidance from the hypequery team.
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Steal the exact query patterns, cache plays, and architecture shortcuts behind sub-second analytics. 5 minute read, every Friday.
A full walkthrough — schema generation, typed query endpoints, React hooks, and a polling dashboard that stays live without WebSockets.
ClickHouse has no native RLS like Postgres. Here are the three patterns for tenant isolation — and why context injection with hypequery is the right default for TypeScript teams.
Three strategies for testing ClickHouse queries in TypeScript — mocking the client, using a local Docker instance, and letting the type system catch errors before tests run.
How to use toStartOfInterval with GROUP BY and column aliases in ClickHouse. TypeScript examples using hypequery and raw SQL — DateTime bucketing for time-series analytics.
A safer ClickHouse MCP pattern: expose a small set of named analytics queries as tools instead of letting the model write arbitrary SQL.
A practical comparison of the real TypeScript options for ClickHouse: raw client, Kysely, Cube, and hypequery, with the tradeoffs each one leaves you holding.
Generate typed React hooks from the same backend query definitions that power your ClickHouse API, so components stop owning fetch glue and response types.
hypequery 0.2.0 adds a query() API for the common problem where one ClickHouse query ends up reimplemented for scripts, HTTP endpoints, and frontend consumers.
ClickHouse and TypeScript often disagree about what types come back at runtime. Learn the real mappings for DateTime, UInt64, Nullable, Decimal, and more.
ClickHouse solved fast analytics reads. It did not solve how the rest of your product should call those queries safely. This post argues for a typed analytics layer between raw SQL and every consumer.