K6 Cloud → ClickHouse
AI-first ETL from K6 Cloud into ClickHouse. Governed entities, incremental sync, typed landing tables.
How Datrise loads K6 Cloud into ClickHouse
Datrise syncs K6 Cloud's records, events, and configuration objects into ClickHouse as a MergeTree table per source entity. Flexible or custom fields land in JSON or Map columns, and timestamps such as created, updated, and status changes are typed as DateTime64.
Sync is incremental: Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge, so re-runs update only what changed. Partition by month and order by (entity id, updated-at) for fast range scans. ClickHouse deduplicates asynchronously on merge, so Datrise uses ReplacingMergeTree and FINAL-safe queries rather than assuming immediate upserts.
Ideal for high-volume event analytics that need sub-second aggregation.
Endpoints
K6 Cloud: SaaS or API data source for analytics and warehouse sync.
ClickHouse: Columnar OLAP engine for fast aggregations.
How K6 Cloud entities map to ClickHouse
| K6 Cloud entity | ClickHouse object | Notes |
|---|---|---|
| records | k6_cloud_records | id PK · custom fields → JSON or Map columns |
| events | k6_cloud_events | DateTime64 events |
| configuration objects | k6_cloud_configuration_objects | id PK · linked to k6_cloud_records |
FAQ
How does Datrise handle K6 Cloud's custom fields in ClickHouse?
Flexible values are stored as JSON or Map columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native ClickHouse types.
How does the K6 Cloud to ClickHouse sync stay up to date?
It runs incrementally — Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge.
Related pipelines
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