Google Pagespeed Insights → ClickHouse

AI-first ETL from Google Pagespeed Insights into ClickHouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Google Pagespeed Insights into ClickHouse

Datrise syncs Google Pagespeed Insights'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

Google Pagespeed Insights: SaaS or API data source for analytics and warehouse sync.

ClickHouse: Columnar OLAP engine for fast aggregations.

How Google Pagespeed Insights entities map to ClickHouse

Google Pagespeed Insights entity	ClickHouse object	Notes
records	`google_pagespeed_insights_records`	id PK · custom fields → JSON or Map columns
events	`google_pagespeed_insights_events`	DateTime64 events
configuration objects	`google_pagespeed_insights_configuration_objects`	id PK · linked to google_pagespeed_insights_records

FAQ

How does Datrise handle Google Pagespeed Insights'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 Google Pagespeed Insights 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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