Apify to ClickHouse Integration
AI-first ETL from Apify into ClickHouse. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apify into ClickHouse
Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs 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
Apify: Web scraping and browser automation platform.
ClickHouse: Columnar OLAP engine for fast aggregations.
How Apify entities map to ClickHouse
| Apify entity | ClickHouse object | Notes |
|---|---|---|
| actors | apify_actors | id PK · custom fields → JSON or Map columns |
| runs | apify_runs | id PK · linked to apify_actors |
| datasets | apify_datasets | id PK · linked to apify_actors |
| key-value stores | apify_key_value_stores | id PK · linked to apify_actors |
FAQ
How does Datrise handle Apify'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 Apify 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
More destinations for Apify
More sources for ClickHouse
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