What do you get: It offers NVMe-backed Postgres + built-in CDC into ClickHouse + pg_clickhouse so you can keep your app Postgres-first while running analytics in ClickHouse.

Try it (Sign up for private preview): https://clickhouse.com/cloud/postgres

Live demo: https://www.youtube.com/watch?v=rpBA13nQxAk&t=4s

Detailed technical blog: https://clickhouse.com/blog/postgres-managed-by-clickhouse

Problem - Scaling limits of Postgres for OLTP and OLAP

As companies grow on Postgres, they often start to run into performance limits, both for transactions and analytics. On the OLTP side, things like slow updates and upserts, painful vacuums, and WAL spikes from long-running transactions are common, usually driven by disk I/O bottlenecks. With fewer I/O constraints, Postgres could handle a lot more.

On the analytics side, many limitations come from the fact that Postgres was designed for OLTP and lacks features common in analytical databases, such as vectorized execution and broad ingest format support. As a result, many companies (e.g., GitLab, Ramp, Cloudflare) complement Postgres with ClickHouse to offload analytics, adopting two purpose-built open-source databases.

However, adding ClickHouse to a Postgres-based application isn’t straightforward—you typically need to build CDC pipelines, manage backfills and schema changes, and update application code to handle a second database.

Solution - NVMe-backed Postgres natively integrated with ClickHouse

On the OLTP side, NVMe-based Postgres can dramatically improve performance. By colocating storage with compute, NVMe enables much lower latency and higher IOPS than network-attached storage, speeding up disk-bound operations (up to 10x), including updates, upserts, vacuums, and checkpointing. We’re working on a detailed blog exploring how NVMe reduces WAL fsync, buffer read, and checkpoint overhead, stay tuned!

On the OLAP side, the Postgres service offers native CDC to ClickHouse and unified querying via pg_clickhouse. CDC is currently powered by ClickPipes/PeerDB using logical replication, and we’re working to make it faster and simpler with logical replication v2, a new decoding plugin, and a path toward sub-second replication.

Every Postgres comes with the pg_clickhouse (https://github.com/ClickHouse/pg_clickhouse) extension, which simplifies adding ClickHouse-powered analytics to a Postgres application. It lets you query ClickHouse directly from Postgres, enabling a single interface for transactions and analytics. pg_clickhouse supports broad analytics query pushdown, with plans to expand this further over time.

Who is it for?

1. Companies who are looking for a faster and more scalable Postgres. 2. Companies already using ClickHouse that want a tightly integrated Postgres to offload analytics to ClickHouse, without the operational burden of managing the integration. 3. Companies that are growing rapidly (or expect to) and want a simple yet powerful stack with Postgres on NVMe + ClickHouse.

CTA

1. If the above value and vision resonates with you and you want to try it for a use-case, please sign up here and we will give you access within a day or two! https://clickhouse.com/cloud/postgres 2. We’d love to hear your feedback and anything else that comes to mind, it would be super helpful to us as we build this out!