How ClickHouse Uses engineering

ClickStack and Hud now share trace IDs, pairing service-level observability with function-level runtime forensics so coding agents can assess risky changes before they ship, catch regressions right after deploy, and fix them with real production context.

I am excited to announce that I am joining ClickHouse, Inc. to establish and lead a new research team at ClickHouse Labs.

We ran ClickHouse Managed Postgres and PlanetScale on the same hardware. ClickHouse delivered up to 54% higher throughput with lower latency.

ClickHouse has achieved the AWS Small and Medium Business Competency, joining a select group of AWS Partners recognized for deep expertise in real-time analytics for small and medium businesses.

Search and uncover technology trends across 10 billion GitHub events in real time.

This post shows a lightweight metrics layer for ClickHouse using MooseStack, an open source developer agent harness for ClickHouse.

High-performance full-text search, even on object storage. This deep dive from the ClickHouse engineers explains the new text index design and how it keeps queries fast at scale.

How to remain competitive in the AI era of software engineering

A Solutions Architect’s experience building a retail analytics platform with AI agents, real-time dashboards, and full observability on the ClickHouse data platform

ClickHouse Cloud now shards indexes across replicas, turning a fixed per-node memory cost into a shared distributed resource. The result: lower memory usage, faster index analysis, and better performance for petabyte-scale 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 .

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.

ClickHouse Cloud takes on Snowflake, Databricks, BigQuery, and Redshift on TPC-H, ranking first on SF100 cost-performance and running SF10 for less than one cent.

Over two years of focused join engineering, ClickHouse became 26× faster on the TPC-H SF100 join-heavy workload. Here’s how parallel hash joins, runtime filters, lazy column replication, and smarter join planning got us there.

ClickHouse was released in open source on Jun 15 2016, ten years ago. Since then, it became the most popular open source analytical database with more than 2000 contributors.

On June 15, 2016, ClickHouse went open source under the Apache 2.0 license. Ten years later, it's one of the most widely deployed analytical databases in the world.

Real-time analytics is more than fast queries. CostBench compares Snowflake and ClickHouse Cloud across the full path: continuous ingest, query-ready data maintenance, freshness, query latency, and cost.

Benchmarks only matter when they can be inspected, challenged, and reproduced. Here’s what we found when we tested Databricks’ ClickHouse claim.

We've released @clickhouse/rowbinary a Node.js reader and writer for ClickHouse's RowBinary formats. You can import it and call its generic parser like any other library. But it also ships as an Agent Skill: point a coding agent at the bundled `SKILL.md`

A hands-on walkthrough of loading billions of Binance tick records into ClickHouse, showing how columnar storage and codecs compress trade and quote data 19x, and how standard SQL handles VWAP, OHLC candles, and as-of joins at millisecond granularity.

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