Postmark to ClickHouse Integration
AI-first ETL from Postmark into ClickHouse. Governed entities, incremental sync, typed landing tables.
How Datrise loads Postmark into ClickHouse
Datrise syncs Postmark's messages, servers, delivery events, bounces, opens, and message streams 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
Postmark: Transactional email delivery with detailed events.
ClickHouse: Columnar OLAP engine for fast aggregations.
How Postmark entities map to ClickHouse
| Postmark entity | ClickHouse object | Notes |
|---|---|---|
| messages | postmark_app_messages | id PK · custom fields → JSON or Map columns |
| servers | postmark_app_servers | id PK · linked to postmark_app_messages |
| delivery events | postmark_app_delivery_events | DateTime64 events |
| bounces | postmark_app_bounces | id PK · linked to postmark_app_messages |
FAQ
How does Datrise handle Postmark'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 Postmark 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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