Clockify to ClickHouse Integration
AI-first ETL from Clockify into ClickHouse. Governed entities, incremental sync, typed landing tables.
How Datrise loads Clockify into ClickHouse
Datrise syncs Clockify's time entries, projects, tasks, users, clients, and tags 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
Clockify: Time tracking across projects and teams.
ClickHouse: Columnar OLAP engine for fast aggregations.
How Clockify entities map to ClickHouse
| Clockify entity | ClickHouse object | Notes |
|---|---|---|
| time entries | clockify_time_entries | id PK · custom fields → JSON or Map columns |
| projects | clockify_projects | id PK · linked to clockify_time_entries |
| tasks | clockify_tasks | id PK · linked to clockify_time_entries |
| users | clockify_users | id PK · linked to clockify_time_entries |
FAQ
How does Datrise handle Clockify'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 Clockify 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.
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