ClickhouseAnnouncement
A new getting started experience for ClickHouse Managed Postgres
Summary
ClickHouse Managed Postgres introduces a four-step onboarding experience designed to move users from provisioning a service to working with operational data and ClickHouse analytics. Existing onboarding placed new users on an empty Overview page, while the documentation quickstart required users to track progress outside the product. The new flow guides users through creating a service, migrating or ingesting data, running a query in the SQL Console, and configuring Postgres CDC to ClickHouse. Data can arrive through a managed Postgres-to-Postgres ClickPipe, a language client, a sample dataset, or a table created from scratch; pg_clickhouse provides a PostgreSQL query layer over ClickHouse foreign tables. The article reports less than a minute of replication lag and benchmarks showing five aggregations falling from 555 ms to 164 ms and a JOIN with aggregations from 1,246 ms to 170 ms on one million-row data set.
Context
After creating a Postgres service, users landed on an Overview page designed for active databases, but new services had no activity or data to explore. The existing CLI quickstart described the full Postgres-to-ClickHouse journey, yet users had to read the documentation in one tab and work in another while the product remained unaware of their progress.
Approach / What changed
The onboarding flow keeps four outcome-oriented steps visible: spin up a Postgres service, migrate or ingest data, run a first query, and run analytics with ClickHouse. It supports migration through a fully managed Postgres-to-Postgres ClickPipe, as well as language clients, sample datasets, and table creation. It then guides users to Postgres CDC ClickPipes and surfaces pg_clickhouse for querying ClickHouse through PostgreSQL syntax.
Takeaways
The onboarding journey treats loading data as the central activation step, offering Postgres migration, language clients, sample datasets, and table creation as entry points.
A Postgres CDC ClickPipe copies existing data into ClickHouse and continuously replicates changes, with the reported lag kept below one minute.
Using pg_clickhouse, five aggregations on a one-million-row dataset dropped from 555 ms to 164 ms, while a JOIN with aggregations dropped from 1,246 ms to 170 ms.
Source and freshness
- Source
- clickhouse.com
- Published
- Added to Yomu
- Last updated
Summaries are generated from the original by a language model. Check the original before relying on it.
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