ClickHouse Blog

Learn how to use ClickHouse's SAMPLE BY clause to run fast, approximate queries on large datasets with minimal accuracy trade-off.

How we integrated the Rust Delta Kernel to replace our native Delta Lake implementation, reducing maintenance overhead while unlocking writes, schema evolution, time travel, and partition pruning.

Welcome to the May 2026 ClickHouse newsletter, which will round up what’s happened in real-time data warehouses over the last month.

We're releasing clickhousectl v0.2.0. This release adds Postgres, ClickPipes, SQL over HTTP, a few agent-friendly output tweaks, and a standalone Rust client library for the ClickHouse Cloud API.

Welcome to the April edition of What's New in ClickStack. April was a release focused on tightening the core experience across querying, alerting, and dashboards. SQL-powered alerts landed this month, rounding out the SQL-native observability workflow we started building last month. You can now move from writing queries and building dashboards directly into alerting, without switching query langu…

In this blog, we explore why high cardinality behaves fundamentally differently in column-oriented databases like ClickHouse, and why the costs appear in very different places than in traditional series-based systems like Prometheus.

How ChatFeatured cut analytics query times from 2.5 minutes to under a second by migrating from PlanetScale Postgres to Postgres managed by ClickHouse — in just 30 minutes.

How the D. E. Shaw group replaced its previous observability platform with ClickHouse to handle high-cardinality metrics at scale, achieving 7x better query performance and enabling multi-year capacity planning across millions of compute workloads.

A deep dive into how pg_clickhouse's Foreign Data Wrapper decides what SQL to push down to ClickHouse versus execute locally in Postgres .

Why does high cardinality break Prometheus but not ClickHouse? In Part 1, we explore the architectural tradeoffs of Prometheus and other series-based systems, showing how cardinality impacts memory, ingestion, querying, and operational stability at scale.

Learn how ClickStack’s new SQL-powered charting and alerting unlock anomaly detection, rolling baselines, and advanced observability workflows directly on top of ClickHouse, without relying on external tooling.

Learn how ClickHouse Cloud's Join table engine enables fast, updatable in-memory lookups for dimensional modeling — with automatic upserts, deduplication, and data compaction powered by ReplacingMergeTree under the hood.

Avride replaced Apache Iceberg with ClickHouse Cloud, cutting index lookup latency from 20 seconds to under 100ms and ingestion from hours to seconds.

ClickHouse 26.4 is here! In this release, more features become SQL compatible, COUNT DISTINCT gets faster, EXPLAIN gets even prettier, and more

Query Insights is now in preview for ClickHouse Cloud Managed Postgres: every query pattern your database runs, ranked by impact, with the diagnostic picture of why each one is slow.

How ClickStack and Odigos eliminate observability gaps with zero-code eBPF instrumentation and full-fidelity distributed tracing at scale.

Agentic analytics makes query-readiness a write-side cost problem. This post compares Snowflake and ClickHouse under continuous ingest, showing how ClickHouse obtains query-ready data at 22× lower cost and delivers 31× better write-side cost-performance.

We use clickhousectl to spin up multiple ClickHouse versions side by side and benchmark two recent performance improvements.

Learn how Gala migrated to the ClickHouse Cloud data platform on AWS to improve analytics performance and cut costs

How Qonto uses ClickHouse Cloud to power observability at scale — replacing sampling and hour-capped queries with two-week query windows, 99.84% compression on high-cardinality data, and an AI incident companion built on the ClickHouse MCP server.