CSV File to ClickHouse Integration
AI-first ETL from CSV File into ClickHouse. Governed entities, incremental sync, typed landing tables.
How Datrise loads CSV File into ClickHouse
Datrise syncs CSV File's rows, columns, headers, delimiters, file batches, and encodings 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
CSV File: Delimited flat files from exports and drops.
ClickHouse: Columnar OLAP engine for fast aggregations.
How CSV File entities map to ClickHouse
| CSV File entity | ClickHouse object | Notes |
|---|---|---|
| rows | csv_file_rows | id PK · custom fields → JSON or Map columns |
| columns | csv_file_columns | id PK · linked to csv_file_rows |
| headers | csv_file_headers | id PK · linked to csv_file_rows |
| delimiters | csv_file_delimiters | id PK · linked to csv_file_rows |
FAQ
How does Datrise handle CSV File'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 CSV File 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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