DatriseAI-first ETL

ClickHouse to PlanetScale Integration

AI-first ETL from ClickHouse into PlanetScale. Governed entities, incremental sync, typed landing tables.

How Datrise loads ClickHouse into PlanetScale

Datrise syncs ClickHouse's tables, rows, partitions, materialized views, table schemas, and merge parts into PlanetScale as a typed table per source entity. Flexible or custom fields land in JSON columns, and timestamps such as created, updated, and status changes are typed as DATETIME.

Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE, so re-runs update only what changed. Vitess sharding by tenant or entity key for very large tables. PlanetScale disallows foreign-key constraints by default, so Datrise models relationships by stable id columns rather than enforced FKs.

Ideal for horizontally scalable MySQL apps on Vitess.

Endpoints

ClickHouse: Column-oriented database for real-time analytics.

PlanetScale: Serverless MySQL platform with safe schema workflows.

How ClickHouse entities map to PlanetScale

ClickHouse entityPlanetScale objectNotes
tablesclickhouse_tablesid PK · custom fields → JSON columns
rowsclickhouse_rowsid PK · linked to clickhouse_tables
partitionsclickhouse_partitionsid PK · linked to clickhouse_tables
materialized viewsclickhouse_materialized_viewsid PK · linked to clickhouse_tables

FAQ

How does Datrise handle ClickHouse's custom fields in PlanetScale?

Flexible values are stored as JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native PlanetScale types.

How does the ClickHouse to PlanetScale sync stay up to date?

It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE.

Related pipelines

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