ClickHouse + TypeScript: Building Scalable OLAP Dashboards with Fully Typed Queries

Hook — Why TypeScript + ClickHouse matters for analytics teams in 2026

If you've inherited dashboards that break when a new column appears, or your analytics queries turn into runtime debugging sessions, you're not alone. As ClickHouse moves from niche OLAP engine to mainstream analytics platform — helped by major funding and rapid cloud adoption — teams need patterns to build scalable, predictable analytics apps in TypeScript. This article shows how to combine ClickHouse's raw performance with TypeScript's type system to ship dashboards that are maintainable, strongly typed, and production-ready in 2026.

State of the ecosystem (2026 snapshot)

ClickHouse continued accelerating through 2025 and into 2026 — raising large funding rounds and expanding enterprise features. Bloomberg reported a $400M round in late 2025 that pushed ClickHouse's valuation significantly higher, signaling heavier adoption across observability, marketing analytics, and product telemetry teams. That shift matters to TypeScript developers because OLAP workloads are now integrated directly into application stacks: web dashboards, embedded analytics, and real-time monitoring.

With increased adoption comes a need for great developer experience. Teams expect typed client libraries, typed query builders, and predictable result shapes. Below you'll find practical patterns to build typed ClickHouse integrations using TypeScript — including typed SQL tag functions, runtime mappers, schema introspection scripts, and performance best practices for large-scale OLAP usage.

Goals and constraints: what "typed queries" should deliver

• Compile-time safety: catch mismatched projections, missing fields, and bad parameters before shipping.
• Runtime validation: protect against schema drift when data evolves or ingest pipelines change.
• Performance: keep memory usage low when scanning large tables and ensure queries leverage ClickHouse features.
• Developer ergonomics: small friction for writing queries, good autocompletion, and easy migration paths.

High-level architecture: typed client, typed query builder, and result mapper

The architecture we recommend has three layers:

1. Typed client wrapper — a small wrapper over the ClickHouse Node client (official or community) that exposes typed methods for queries, streaming, and inserts.
2. Typed query builders / SQL tags — either a TypeScript-friendly builder (Kysely-style) or a simple typed SQL tag that binds parameters and declares expected return types with generics.
3. Result mappers / runtime validators — use Zod/TypeBox/io-ts to validate rows and convert ClickHouse native types (DateTime64, UInt64) into safe JS types.

Why not rely only on ORMs?

ClickHouse is often queried with wide, custom aggregations that don't fit row-based ORMs. Instead, prefer typed SQL or query builders that preserve SQL expressiveness while giving you type safety for parameters and results.

Practical pattern: typed SQL tag + runtime validation

This pattern is small, dependable, and integrates well with existing analytics dashboards. We’ll show:

1. A minimal typed SQL template tag that carries parameter and result types.
2. Runtime validation with Zod to protect against schema drift.

1) Minimal typed SQL tag

The tag only needs to give you typed parameters and a generic typed return row. Use generics for the expected result shape so editors can infer autocomplete when you consume results.

import { ClickHouse } from '@clickhouse/client'

const client = new ClickHouse({ url: process.env.CLICKHOUSE_URL! })

// typed SQL tag: Q
export function Q, TRow = Record>(strings: TemplateStringsArray, ...values: any[]) {
  // This function is a friendly compile-time surface — runtime just builds text and params
  const text = strings.reduce((acc, s, i) => acc + s + (i < values.length ? `\$${i + 1}` : ''), '')
  const params = values
  return { text, params } as const
}

// usage
interface PageView { ts: string; page: string; views: number }

const q = Q, PageView>`
  SELECT toStartOfInterval(ts, INTERVAL 1 hour) AS ts, page, count() AS views
  FROM page_views
  WHERE ts BETWEEN ${'from'} AND ${'to'}
  GROUP BY ts, page
  ORDER BY ts
`

const res = await client.query<{ data: PageView[] }>(q.text, { params: q.params })

Note: this example shows the shape. Real client APIs vary; official ClickHouse Node client supports parameter binding and typed returns. The key idea is to keep the TypeScript signature as the single source of truth for the expected row type.

2) Runtime validation and mapping

TypeScript gives compile-time guarantees but can't validate the runtime payload when schema drift happens. Use Zod (or io-ts) as a safe guard and to map ClickHouse types (e.g., DateTime64 -> Date, UInt64 -> string or BigInt).

import { z } from 'zod'

const PageViewSchema = z.object({
  ts: z.string().transform((s) => new Date(s)), // ClickHouse returns ISO by default for DateTime
  page: z.string(),
  views: z.number(),
})

type PageView = z.infer

function mapRows(rows: unknown[], schema: z.ZodType): T[] {
  return rows.map((r) => schema.parse(r))
}

// consume
const raw = await client.query({ query: q.text, params: q.params })
const rows = await raw.json() // depends on client
const typedRows = mapRows(rows.data, PageViewSchema)

Generating TypeScript types from ClickHouse schema (automation)

For medium-to-large analytics teams, manually maintaining row interfaces quickly becomes brittle. Automate type generation by querying ClickHouse's system tables and mapping ClickHouse column types to TypeScript.

Example script (simplified):

/** generate-types.ts */
import { ClickHouse } from '@clickhouse/client'
import fs from 'fs'

const client = new ClickHouse({ url: process.env.CLICKHOUSE_URL! })

async function toTsType(chType: string) {
  if (/String|LowCardinality\(String\)/i.test(chType)) return 'string'
  if (/Date(Time)?(64)?/i.test(chType)) return 'string' // or 'Date' if you parse it
  if (/UInt(8|16|32)/i.test(chType) || /Int(8|16|32)/i.test(chType)) return 'number'
  if (/UInt(64)|Int64/i.test(chType)) return 'string' // preserve precision
  if (/Float(32|64)/i.test(chType)) return 'number'
  if (/Array\(/i.test(chType)) return 'any[]'
  return 'any'
}

async function generate(table: string, database = 'default') {
  const sql = `SELECT name, type FROM system.columns WHERE table = '${table}' AND database = '${database}' ORDER BY position`
  const res = await client.query({ query: sql })
  const rows = await res.json()
  const tsLines = ['export interface ' + pascalCase(table) + ' {']
  for (const r of rows.data) {
    tsLines.push(`  ${r.name}: ${await toTsType(r.type)};`)
  }
  tsLines.push('}')
  fs.writeFileSync(`${table}.d.ts`, tsLines.join('\n'))
}

function pascalCase(s: string){return s.replace(/(^|_)(\w)/g, (_, __, c) => c.toUpperCase())}

generate('page_views').catch(console.error)

Run this in your CI pipeline and commit generated types to a mono-repo or publish them as a dev-only package so front-end and back-end agree on result shapes.

Type mapping caveats for ClickHouse

• 64-bit integers: ClickHouse has 64-bit ints; JavaScript number cannot represent UInt64 precisely. Use string or BigInt based on your safety requirements and client support. Many teams standardize on strings for IDs in analytics.
• Date/time types: Date, DateTime, and DateTime64 may be returned as strings. Decide on a canonical representation (ISO strings or Date objects) and centralize mapping.
• LowCardinality: LowCardinality(String) typically maps to string. Beware of NULLable columns and union types.

Typed query builders: use or build?

There are two viable approaches:

1. Adopt a TypeScript-first query builder (Kysely-style). It provides compile-time checks for selected columns and safer refactors. In 2026 you’ll find several community dialects adding ClickHouse support; consider them when you want schema-aware queries.
2. Write a thin SQL tag wrapper (shown above) and pair it with generated types and runtime validators. This keeps full expressiveness of ClickHouse SQL (arrayJoin, window functions, lambda expressions) which many analytics queries need.

For dashboards with many ad-hoc or analytics-specific queries, the SQL tag + codegen approach gives flexibility and good type ergonomics. For apps that treat ClickHouse as a relational store with CRUD-ish patterns, a query builder provides more safety.

Best practices for large-scale OLAP integration

The following practices are battle-tested for ClickHouse deployments powering dashboards and analytics in 2026.

1) Use materialized views, projections, and pre-aggregations

Precompute heavy aggregations as materialized views or ClickHouse projections to avoid scanning billions of rows per dashboard request. Materialized views reduce latency for common queries (e.g., daily aggregates), while projections can speed up grouped queries by maintaining physical sort orders.

2) Partition and ORDER BY strategically

ClickHouse performance depends on how data is sorted and partitioned. Partition by date/month and ORDER BY by timestamp or a commonly-filtered composite key. Tests show orders of magnitude speedups when partitioning fits your query patterns.

3) Sampling and approximate algorithms

For interactive dashboards, provide an approximate mode using SAMPLE or aggregated summaries. Offer a quality slider: approximate for fast responses, exact for scheduled reports.

4) Stream results and paginate

Don't buffer huge resultsets in memory. Use the Node client's streaming cursor or async iterator to process rows progressively, and design the UI to paginate or progressively load data (time buckets first, then series details).

5) Cache at the edge

Put a caching layer (edge or CDN) in front of common queries. Use cache keys including query fingerprint, time-range, and parameter set. For dashboards, cache raw JSON responses for 30s–5m depending on freshness needs.

6) Validate and monitor schema drift

Run nightly checks that reconcile generated TypeScript types with system.columns. Fail CI when column types change unexpectedly. Combine with runtime Zod validation that logs and alerts on unexpected rows.

7) Optimize ingress: compressed batches and native formats

For heavy inbound telemetry, ingest in batches using the native protocol or insert formats like Parquet or Native. Avoid individual row inserts. In 2026 many teams also use object-store-backed ingestion (S3) and serverless functions to move data into ClickHouse efficiently.

Typing third-party ClickHouse libraries and contributing types

By 2026 the ecosystem has better TypeScript support, but you still encounter untyped or partially-typed libraries. Here are practical steps:

• Prefer libraries that include first-class types (official clients often do).
• If a package lacks types, add a small local declaration file (e.g., declarations/clickhouse.d.ts) with the minimal surface you use.
• Contribute typings to DefinitelyTyped if the package is community-maintained. Open-source contributions help the whole ecosystem.
• Use dts-gen for bootstrapping placeholder types and then refine them.

Performance checklist for TypeScript + ClickHouse apps

• Enable strict TypeScript mode and isolate types for queries — it reduces runtime surprises.
• Stream large result sets and avoid full-materialization unless necessary.
• Use Zod or similar for runtime validation on critical query paths.
• Automate type generation from system.columns and gate PRs when generated types change.
• Cache query results intelligently; use hashed query fingerprints for stable cache keys.
• Use partitions, ORDER BY, materialized views, and projections to push compute to ClickHouse.
• Prefer string/BigInt for 64-bit values and centralize conversion helpers.

Advanced strategies and 2026 trends

Looking ahead, expect tighter integration between ClickHouse and ML/feature-store pipelines. Teams will increasingly run feature extraction queries directly in ClickHouse and stream summarized features into model serving systems. For TypeScript apps, this means stricter typing contracts between analytics queries and ML consumers. Consider the following advanced strategies:

• Schema-first analytics contracts: define the output schema for analytical queries (as TypeScript/Zod) and require contract checks during CI to ensure dashboards and ML pipelines agree.
• Edge pre-aggregation: push light-weight aggregations to edge collectors (Web SDKs or proxies) to reduce cardinality before ClickHouse ingestion.
• Vectorized features: ClickHouse continues optimizations for vectorized operations; prefer analytics expressions that leverage these (array functions, window functions) for large batch computations.

Case study — scalable dashboards at a mid-size SaaS (summary)

One mid-size SaaS migrated their product analytics from PostgreSQL to ClickHouse in 2025. They implemented:

• Generated TypeScript interfaces from ClickHouse system tables and published them as a private npm package to frontend teams.
• Wrapped ClickHouse queries with a typed SQL tag and Zod validators for each dashboard query.
• Introduced materialized views for hourly/day aggregates and implemented a two-tier cache: 1) edge cache for <5m responses, and 2) application cache for 1h scheduled reports.
• Switched ingestion to compressed Parquet batches and used the native protocol for inserts to reduce ingress overhead.

Results: median dashboard latency dropped from 4.2s to 250ms for common queries; incidents caused by schema drift dropped to near zero after runtime validators and nightly schema checks were introduced.

Actionable checklist — get started this week

1. Enable strict TypeScript and add a small typed wrapper around your ClickHouse client.
2. Pick a runtime validator (Zod recommended) and add schemas for the top 10 dashboard queries.
3. Write a simple codegen script that queries system.columns and generates TypeScript interfaces for the tables you query most.
4. Review your ingestion pipeline: batch inserts, compressed formats, and partitioning strategy.
5. Introduce caching for dashboard endpoints and a materialized view for one heavy aggregation.

Common pitfalls and how to avoid them

• Trusting only TypeScript: always pair compile-time types with runtime validators for analytics workloads, where schema drift is common.
• Using number for 64-bit IDs: use string or BigInt to avoid subtle precision bugs when dealing with UInt64 / Int64.
• Not instrumenting query costs: track query duration and bytes read — ClickHouse bills or scales often depend on those metrics.
• Over-aggregating client-side: prefer pre-aggregations in ClickHouse rather than pulling raw data and post-processing in Node/React.

Further reading and tools

• Official ClickHouse Node client docs (check the client package for up-to-date typing support).
• Zod (https://github.com/colinhacks/zod) — runtime validators that pair well with TypeScript.
• Query builder projects (Kysely and community dialects) — explore if you want a schema-aware query DSL.
• System tables in ClickHouse (system.columns, system.tables) for schema introspection and generation.

"ClickHouse's growth to mainstream OLAP in 2025–26 makes typed query contracts a practical necessity, not a nicety." — analysis based on ecosystem trends and adoption signals (Bloomberg, 2025)

Conclusion — Why this approach wins in 2026

The combination of ClickHouse's OLAP performance and TypeScript's reliability allows teams to build analytics dashboards that are fast, safe, and maintainable. As ClickHouse adoption grows across enterprises in 2026, the differentiator will be how well engineering teams manage schema contracts, validation, and deployment patterns. Typed queries plus runtime validation and codegen is a pragmatic, incremental approach that delivers immediate developer productivity and long-term stability.

Call to action

Ready to try a working template? Clone our starter repo for ClickHouse + TypeScript dashboards (includes typed SQL tags, codegen script, and Zod validators) and run it against a dev ClickHouse instance. If you want the repo link, sign up for our weekly TypeScript.page newsletter or follow the ClickHouse community channels — and share questions or example schemas you want typed and validated. Ship safer analytics.

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