Skip to content

ClickHouse v26.6 Release Webinar

Alexey Milovidov

The ClickHouse v26.6 release webinar covers the summer release and 10-year open-source anniversary edition. Alexey Milovidov takes you through a dense set of new features — some small and practical, a few genuinely surprising — plus a live demo session with schema visualization, PNG rendering in the terminal, and an interactive help system that searches docs without leaving clickhouse-client.

  • Streaming queries — new experimental SELECT ... FROM table STREAM syntax for real-time data monitoring, with aggregation windows on the roadmap
  • Multi-stage distributed execution — hash joins, sorting, and group-by now support full distributed shuffle in both open source and ClickHouse Cloud
  • Memory reservations — resource workloads let you assign memory budgets and priorities across query classes
  • Hypothetical indexes — evaluate index effectiveness via EXPLAIN before committing to creation
  • Embeddings + quantization — generate embeddings inside the DB with any model, then compress with Lloyd-Max quantization down to 1-bit
  • ~50 performance optimizations including 2x faster ClickHouse Keeper, 64x faster complex queries with large column counts, and new sorting algorithms from recent academic work

Distributed joins and smarter workloads in ClickHouse Cloud

Multi-stage distributed execution for joins and sorting, memory reservations for workloads and a 2x faster ClickHouse Keeper: each release adds work like this, and ClickHouse Cloud rolls it out to your service without you managing a server. Get started in minutes and receive $300 in free credits.

Sign up

Recent videos

YouTube Video: P1IDAvsi7p8

Releases

ClickHouse v26.5 Release Webinar

Alexey Milovidov

YouTube Video: mKBNLaFOVDA

Releases

ClickHouse v26.7 Release Webinar

Alexey Milovidov

YouTube Video: 5iVfjvhbilY

Webinars

Instant compute for your most intensive workloads

Join Melvyn Peignon, Director of Product Management at ClickHouse, for a technical walkthrough and live demo. See how to use On-Demand Compute, where it fits alongside autoscaling and compute-compute separation, and why data lake queries are an especially strong fit.

Follow us

XBlueskySlackGithubTelegramMeetupRSS