How ClickHouse Uses SQL

ClickHouse 26. 1 is a major release featuring 25 new features, 43 performance optimizations, and 176 bug fixes.

The article discusses the development of chDB, a Python library that integrates ClickHouse with Pandas DataFrames for high-performance SQL querying.

The article reviews the significant developments and features introduced in ClickStack over its first seven months since launch, highlighting advancements such as JSON support, integration with Cli...

The article discusses the need for improved observability in AI Site Reliability Engineering (SRE) rather than relying solely on larger models.

At ClickHouse, we don't like the word "impossible." We believe that with the right tools, everything is a data problem. To prove it, we decided to complete the 2025 Advent of Code unconventionally: using pure ClickHouse SQL.

The article reviews significant demo developments at ClickHouse in 2025, highlighting various applications that showcase its performance and capabilities.

The article discusses key benchmarks conducted over the past year that reveal insights into performance optimization in ClickHouse.

The article discusses ClickHouse's journey of integrating Rust into its predominantly C++ codebase without undertaking a complete rewrite.

The article highlights Alexey's favorite features introduced in ClickHouse throughout 2025, including lightweight updates, data lake support, and advancements in text and vector indexing.

The November 2025 edition of What's New in ClickStack highlights several new features and improvements in the open-source observability stack built for ClickHouse.

This article provides a detailed analysis of how the five major cloud data warehouses—Snowflake, Databricks, ClickHouse Cloud, Google BigQuery, and Amazon Redshift Serverless—calculate compute cost...

The article details the journey of upgrading the chDB kernel from ClickHouse v25. 5 to v25. 8. 2.

The article discusses the development of StockHouse, a real-time market analytics application that leverages ClickHouse, Massive, and Perspective to handle high-frequency financial data.

This article details the transformation of ClickHouse's internal data warehouse from a traditional BI-first approach to an AI-first model, significantly enhancing user accessibility to analytics.

The article discusses the creation of a website for tracking team activity across GitHub repositories, initially intended as a single report but evolved into a comprehensive tool for comparing vari...

The October 2025 edition of What's New in ClickStack highlights significant updates to the open-source observability stack for ClickHouse, including the introduction of alerting features, customiza...

ClickHouse version 25. 10 introduces significant enhancements, including 20 new features, 30 performance optimizations, and 103 bug fixes.

This article discusses how to enhance log compression through log clustering techniques in ClickHouse, focusing on transforming unstructured logs into structured data for efficient storage.

This article explores the process of tracing OpenAI agents using ClickStack, demonstrating how to build an OpenAI agent that interacts with ClickHouse and visualizes the decision-making process.

The article discusses the potential of lakehouses using open table formats like Apache Iceberg and Delta Lake for observability, highlighting their advantages in scalability, cost-effectiveness, an...

The article discusses the rising costs associated with observability in software engineering and proposes a shift towards open, cost-efficient architectures.

This article serves as a practical guide for building ClickHouse-powered APIs in web applications, specifically using React and MooseStack.

ClickHouse version 25. 8 introduces 45 new features, 47 performance optimizations, and 119 bug fixes, enhancing its capabilities as a high-performance analytical database.

This article discusses how to instrument a Next. js application using OpenTelemetry and ClickStack, focusing on the integration of observability and analytics through ClickHouse.

The article discusses how ClickHouse Cloud has achieved the capability to scale complex GROUP BY queries across thousands of cores, processing over 100 billion rows in under a second.

This article discusses the implementation of Change Data Capture (CDC) from Delta Lake to ClickHouse, detailing the architecture, components, and a reference implementation in Python.

The article explores the potential need for Object-Relational Mappers (ORMs) in Online Analytical Processing (OLAP) environments, particularly focusing on ClickHouse.

This article compares the UPDATE performance of ClickHouse and PostgreSQL, highlighting ClickHouse's significant speed advantages in bulk updates while also acknowledging PostgreSQL's strengths in ...

ClickHouse Release 25.

This article discusses the significant performance improvements achieved in ClickHouse's SQL UPDATE operations, demonstrating how they can be up to 1,000× faster than traditional mutation methods t...

This article discusses the innovative approach ClickHouse has taken to implement fast SQL-style UPDATEs in its column store architecture.

This article discusses how ClickHouse addresses the challenges of row-level updates in column stores by treating updates as inserts.

The article discusses the importance of LLM observability using ClickStack, OpenTelemetry, and MCP, highlighting how to instrument LibreChat for enhanced insights into AI-driven applications.

This article explores a bug encountered by the ClickPipes team related to logical replication slot creation in PostgreSQL read replicas, where a query could not be terminated by standard methods.

The article discusses the latest updates in ClickStack, an open-source observability stack built on ClickHouse, highlighting the introduction of native JSON support for improved query performance a...

The article explores the analysis of Wimbledon tennis data using ClickHouse, detailing the unique scoring system of tennis and how to implement a function to compute points needed to win a game.

The article discusses the transition of ClickHouse Cloud to a fully stateless compute architecture, enabled by the introduction of a Shared Catalog.

ClickHouse version 25.

This article provides a detailed guide on building a real-time market data application using ClickHouse and Massive.

This article discusses the performance improvements achieved by replacing traditional joins with in-memory dictionaries in ClickHouse Cloud, resulting in significantly faster query times and reduce...

This article benchmarks join-heavy SQL queries across ClickHouse, Databricks, and Snowflake, demonstrating that ClickHouse outperforms both competitors in speed and cost across various data scales.

The article discusses the evolution of ClickHouse's observability platform, LogHouse, as it scales beyond 100 petabytes of data.

This article discusses how to build an agentic application using ClickHouse MCP Server and CopilotKit, focusing on creating a customizable analytics dashboard for the UK real estate market.

The article discusses the integration of ClickHouse with the Model Context Protocol (MCP), highlighting its benefits for connecting third-party services to large language models (LLMs).

The article discusses the use of the eBird dataset, which contains 1. 5 billion bird observations, and how to analyze it using ClickHouse.

This article explores the implementation of Change Data Capture (CDC) for MySQL databases using ClickPipes, ClickHouse's native data integration solution.

The article discusses how ClickHouse efficiently queries Parquet files, a key storage format for Lakehouse architectures, without requiring data ingestion.

The article introduces AgentHouse, an interactive demo environment that integrates ClickHouse's real-time analytics with Anthropic's large language model, Claude Sonnet.

The article discusses the introduction of lazy materialization in ClickHouse, a powerful analytical database, which optimizes query performance by delaying the reading of column data until it is ac...

The article announces the launch of Ruby gem analytics powered by ClickHouse, enabling Ruby developers to analyze gem download data since 2017.