# clickhouse Published articles for clickhouse. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## September 2026 newsletter DevFeed: [September 2026 newsletter]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-17T13:42:49Z Content type: news Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [releases](), [Replication](), [Grafana Cloud Metrics]() Tags: [2026](), [caching](), [clickhouse](), [events](), [news](), [newsletter](), [real-time](), [release](), [replication](), [september-2026]() ### AI overview The September 2026 ClickHouse newsletter covers the 26.8 release, including custom HTTP handlers, pipelined SQL, dynamic query filtering, tokenizer support, and other features. It also summarizes replication-queue diagnostics, CostBench results, preview releases, community contributions, and upcoming events. ### Source excerpt Welcome to the September 2026 ClickHouse newsletter, featuring ClickHouse 26.8, PromQL, On-Demand Compute, CostBench results, and the latest community news and events. ## ClickHouse is now available on the dbt platform DevFeed: [ClickHouse is now available on the dbt platform]() Original publisher: [Read original article]() Author: Aditya Chidurala; José Muñoz; Alex Francoeur Published: 2026-09-16T19:38:40Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Cloud](), [Rust](), [Open Source]() Tags: [adapter](), [announcements](), [beta](), [clickhouse](), [cloud](), [dbt](), [open-source](), [platform](), [release](), [rust]() ### AI overview ClickHouse announces a dbt v2 adapter powered by dbt's Rust engine, available in public beta. ClickHouse is also available as a natively supported data warehouse on dbt Platform in private beta, with support for open-source ClickHouse and ClickHouse Cloud. ### Source excerpt The ClickHouse adapter for dbt v2 is in public beta, powered by dbt's Rust engine. ClickHouse also joins the dbt platform in private beta, supporting open-source ClickHouse and ClickHouse Cloud. ## ClickHouse Cloud Announces Private Preview of PromQL Support and Time-Series Table Engine DevFeed: [ClickHouse Cloud Announces Private Preview of PromQL Support and Time-Series Table Engine]() Original publisher: [Read original article]() Author: James Cunningham Published: 2026-09-15T14:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Prometheus](), [Time Series](), [Cloud](), [Grafana](), [telemetry](), [Traces]() Tags: [clickhouse](), [cloud](), [grafana](), [logs](), [metrics](), [prometheus](), [time-series](), [traces]() ### AI overview ClickHouse announces a private preview of PromQL support and a time-series table engine in ClickHouse Cloud, allowing Prometheus metrics to be stored in ClickHouse and queried with existing PromQL. ### Source excerpt ClickHouse PromQL support lets you store Prometheus metrics in ClickHouse Cloud, query them using familiar PromQL, and bring metrics together with your logs and traces without rewriting queries in SQL. ## Replica-aware routing public beta DevFeed: [Replica-aware routing public beta]() Original publisher: [Read original article]() Author: Amy Chen; Jan Mensch Published: 2026-09-15T13:15:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Routing (disambiguation)](), [Replication](), [Caching]() Tags: [cache](), [clickhouse](), [consistency](), [replication](), [routing]() ### AI overview ClickHouse introduces replica-aware routing in Public Beta for Enterprise customers. The feature routes requests to the same replica, allowing continued access to temporary tables and named sessions and supporting read-after-write consistency over HTTP or the native protocol. ### Source excerpt Temporary tables and named sessions live on a single ClickHouse replica, so a follow-up query routed elsewhere can't see them. Replica-aware routing pins your requests to the same replica over HTTP or the native protocol -- and here's how we built it. ## AI Functions in ClickHouse: Upgrade your SQL to the AI age DevFeed: [AI Functions in ClickHouse: Upgrade your SQL to the AI age]() Original publisher: [Read original article]() Author: Andriy Yakovlev; George Larionov Published: 2026-09-11T12:49:32Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Large Language Model](), [AI search](), [Retrieval Augmented Generation (RAG)](), [Databases]() Tags: [ai](), [classification](), [clickhouse](), [embeddings](), [llm](), [rag](), [sql]() ### AI overview ClickHouse introduces beta AI Functions that invoke LLM and embedding providers directly from SQL for tasks including classification, extraction, generation, translation, filtering, redaction, embeddings, and semantic similarity. ### Source excerpt Explore ClickHouse AI Functions for classification, generation, translation, embeddings, semantic search, and cost controls--all directly from SQL. ## Introducing WalShadow: Sub-second Postgres replication to ClickHouse from physical WAL DevFeed: [Introducing WalShadow: Sub-second Postgres replication to ClickHouse from physical WAL]() Original publisher: [Read original article]() Author: Sai Srirampur Published: 2026-09-10T15:53:42Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Database](), [schema-evolution](), [Latency]() Tags: [analytics](), [architecture](), [benchmarks](), [clickhouse](), [github](), [latency](), [open-source](), [postgres](), [replication](), [schema-evolution](), [transactions]() ### AI overview WalShadow is an open-source engine that replicates PostgreSQL data to ClickHouse from the physical WAL stream. The article describes benchmark results of about 200 ms visibility latency and 289K rows per second, plus support for initial loads, continuous replication, schema evolution, recovery, and source switchovers. ### Source excerpt WalShadow replicates Postgres data directly from physical WAL into ClickHouse, delivering around 200 ms latency and 289,000 rows per second in benchmarks. ## ClickHouse is a launch partner for the Data agent in ChatGPT Work DevFeed: [ClickHouse is a launch partner for the Data agent in ChatGPT Work]() Original publisher: [Read original article]() Author: Aditya Chidurala; Teresa Blanco Published: 2026-09-10T15:13:26Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [AI Chat](), [dashboards](), [AI Bots]() Tags: [agent](), [agent-skills](), [analytics](), [chatgpt](), [clickhouse](), [codex](), [dashboards](), [mcp](), [mcp-server](), [oauth]() ### AI overview ClickHouse announces a ChatGPT Work plugin that connects ClickHouse Cloud through OAuth for natural-language data exploration, reports, and interactive dashboards. ### Source excerpt ClickHouse joins the Data agent in ChatGPT Work, connecting ClickHouse Cloud to natural-language queries, reports, and interactive dashboards. ## Now everyone can put data to work DevFeed: [Now everyone can put data to work]() Original publisher: [Read original article]() Published: 2026-09-10T15:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [data](), [dashboards](), [Authorization](), [Amazon Redshift](), [clickhouse](), [GitHub]() Tags: [agent](), [amazon-redshift](), [analytics](), [chatgpt](), [clickhouse](), [dashboards](), [data](), [github](), [product]() ### AI overview OpenAI introduces a Data agent in ChatGPT Work that connects approved company data sources, answers questions in natural language, and builds interactive dashboards. ### Source excerpt Meet the Data agent in ChatGPT Work. Connect company data, uncover insights, and build interactive dashboards with AI using natural language. ## Announcing On-Demand Compute: Instant compute for your most intensive workloads DevFeed: [Announcing On-Demand Compute: Instant compute for your most intensive workloads]() Original publisher: [Read original article]() Author: Melvyn Peignon Published: 2026-09-10T14:22:58Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [autoscaling](), [cloud-infrastructure]() Tags: [autoscaling](), [clickhouse](), [cloud](), [compute](), [cost](), [infrastructure](), [production](), [scale](), [workers]() ### AI overview ClickHouse announces a private preview of On-Demand Compute, which allocates shared workers to eligible individual queries so intensive workloads can run without competing with a service's primary compute. ### Source excerpt ClickHouse On-Demand Compute lets you scale individual queries with additional workers, run intensive workloads without disrupting production, and use compute when you need it. ## ClickHouse release 26.8 DevFeed: [ClickHouse release 26.8]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-09-10T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [parquet](), [data]() Tags: [bug](), [clickhouse](), [data](), [feature](), [parquet](), [performance](), [release](), [sql](), [tokenizers]() ### AI overview ClickHouse 26.8 is an LTS release that adds background queries, pipelined SQL, text tokenizers, and expanded data lake integrations, alongside performance improvements for Parquet, aggregations, and joins. ### Source excerpt ClickHouse 26.8 LTS introduces background queries, pipelined SQL, new text tokenizers, expanded data lake integrations, and faster Parquet, aggregation, and join queries. ## Loading Parquet data into MySQL with ClickHouse DevFeed: [Loading Parquet data into MySQL with ClickHouse]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [MySQL](), [parquet](), [Databases](), [Amazon S3]() Tags: [clickhouse](), [mysql](), [parquet](), [s3](), [sql]() ### AI overview A tutorial on using ClickHouse to load Parquet data into MySQL and query MySQL through ClickHouse table functions. ### Source excerpt Use ClickHouse to load Parquet files into MySQL, explore remote data, and run MySQL queries with table functions and named collections. ## ClickHouse Cloud vs. Snowflake: What drives the real-time performance-per-dollar gap DevFeed: [ClickHouse Cloud vs. Snowflake: What drives the real-time performance-per-dollar gap]() Original publisher: [Read original article]() Author: Tom Schreiber; Lionel Palacin Published: 2026-09-10T00:00:00Z Content type: comparison Language: en Sources: [ClickHouse Blog]() Topics: [data](), [cloud-infrastructure]() Tags: [analytics](), [architecture](), [clickhouse](), [cloud](), [compute](), [cost](), [data](), [infrastructure](), [latency](), [performance](), [real-time](), [sorting](), [storage](), [sync]() ### AI overview A comparison of ClickHouse Cloud and Snowflake for a continuous real-time analytics workload, examining how ingestion and pre-aggregation affect freshness, query work, latency, and cost. ### Source excerpt ClickHouse Cloud delivered 412x better performance per dollar than Snowflake in CostBench. We trace the gap from fresh data arriving to fast answers coming back. ## Announcing ClickHouse Managed Postgres on Google Cloud DevFeed: [Announcing ClickHouse Managed Postgres on Google Cloud]() Original publisher: [Read original article]() Author: Kunal Gupta Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Google](), [Databases](), [cloud-infrastructure]() Tags: [analytics](), [clickhouse](), [gcp](), [google-cloud](), [postgres](), [replication]() ### AI overview ClickHouse announces a private preview of its managed Postgres service on Google Cloud, with NVMe-backed storage, native CDC into ClickHouse, and pg_clickhouse for a unified query layer. ### Source excerpt ClickHouse Managed Postgres is expanding to Google Cloud, bringing NVMe-backed storage, native CDC into ClickHouse, and a unified query layer via pg_clickhouse to GCP private preview customers. ## Introducing chdb Postgres extension: High-performance imports from cloud storage DevFeed: [Introducing chdb Postgres extension: High-performance imports from cloud storage]() Original publisher: [Read original article]() Author: David Wheeler Published: 2026-09-08T15:42:52Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [cloud-infrastructure](), [Amazon S3](), [parquet](), [CSV]() Tags: [benchmark](), [clickhouse](), [cloud](), [duckdb](), [extension](), [json](), [parquet](), [performance](), [postgres](), [s3](), [storage]() ### AI overview chdb is a new Postgres extension that uses the in-process ClickHouse engine to import and export data across cloud storage systems and formats. The article presents import benchmarks, format support, and usage through a query function and a COPY hook module. ### Source excerpt The chdb Postgres extension brings fast imports and exports across cloud storage platforms and data formats, powered by the embedded ClickHouse engine. ## Measuring real-time performance per dollar under continuous load: CostBench's first end-to-end results DevFeed: [Measuring real-time performance per dollar under continuous load: CostBench's first end-to-end results]() Original publisher: [Read original article]() Author: Tom Schreiber; Lionel Palacin Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [AI, ML & Data Engineering](), [Amazon Redshift]() Tags: [analytics](), [architecture](), [benchmark](), [bigquery](), [clickhouse](), [cloud](), [cost](), [data](), [performance](), [real-time](), [serverless](), [streaming](), [systems](), [tracing](), [work]() ### AI overview CostBench benchmarks the cost and performance of real-time cloud data warehouses under continuous ingestion and query load. It compares ClickHouse Cloud with Snowflake, BigQuery, and Redshift Serverless, reporting better end-to-end performance per dollar for ClickHouse Cloud in the tested workload. ### Source excerpt CostBench puts cloud data warehouses under continuous load. Across the complete path from fresh data to fast answers, ClickHouse Cloud delivers 412-1,996x better performance per dollar. ## How MCP Toolbox turns agent text into ClickHouse vectors DevFeed: [How MCP Toolbox turns agent text into ClickHouse vectors]() Original publisher: [Read original article]() Author: Pete Hampton Published: 2026-09-07T09:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [MSP MCP]() Tags: [ai-agent](), [clickhouse](), [databases](), [embedding](), [gemini](), [go](), [google](), [how-to](), [mcp](), [search](), [sql](), [vector]() ### AI overview A tutorial for configuring Google's MCP Toolbox with ClickHouse to embed text during inserts and searches, then return cosine-ranked results without a separate embedding service. ### Source excerpt Google's MCP Toolbox for Databases embeds agent text into vectors on insert and search, then lets ClickHouse rank the results - no embedding service to build or maintain. Here's how to set it up, and what it looks like end to end. ## ClickHouse as a streaming HTTP API DevFeed: [ClickHouse as a streaming HTTP API]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-05T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [web applications]() Tags: [api](), [architecture](), [clickhouse](), [data](), [features](), [how-to](), [http](), [streaming](), [validation]() ### AI overview A tutorial on using ClickHouse 26.8 to expose controlled queries as a streaming HTTP API with named HTTP handlers, access control, and result limits. ### Source excerpt Learn how to build a streaming HTTP API directly in ClickHouse 26.8 with named handlers, typed parameters, pagination, framing formats, and access control. ## Pipelined SQL in ClickHouse 26.8 DevFeed: [Pipelined SQL in ClickHouse 26.8]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-04T12:16:04Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Databases]() Tags: [clickhouse](), [feature](), [learn](), [sql]() ### AI overview ClickHouse 26.8 introduces pipelined SQL, which expresses a query as sequential transformations. The article compares conventional, FROM-first, and pipelined queries and explains that pipelines are translated to optimized standard SQL before execution. ### Source excerpt Learn how ClickHouse 26.8's pipelined SQL syntax lets you build multi-stage queries as a readable sequence of transformations. ## Build a real-time market data app with ClickHouse and Massive DevFeed: [Build a real-time market data app with ClickHouse and Massive]() Original publisher: [Read original article]() Author: Lionel Palacin Published: 2026-09-04T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [App](), [data](), [Node.js](), [Back end](), [API keys]() Tags: [analytics](), [api](), [app](), [backend](), [clickhouse](), [compression](), [data](), [demo](), [events](), [how-to](), [latency](), [low-latency](), [node](), [node-js](), [react](), [real-time](), [rest-api](), [storage](), [streams](), [visualization]() ### AI overview A tutorial for building a real-time market-data application that ingests, stores, and queries trade and quote ticks with Massive and ClickHouse, using Node.js for backend work and React for live visualization. ### Source excerpt Learn how to build a real-time financial analytics application with Massive and ClickHouse that scales to thousands of events per second. ## Introducing Scheduled Upgrades in ClickHouse Managed Postgres DevFeed: [Introducing Scheduled Upgrades in ClickHouse Managed Postgres]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-09-03T16:22:48Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [configuration](), [Operating system]() Tags: [clickhouse](), [enterprise](), [platform](), [postgres](), [scale](), [security](), [storage](), [updates]() ### AI overview ClickHouse Managed Postgres introduces configurable maintenance windows for routine platform upgrades. Scale and Enterprise organizations can schedule when maintenance occurs, with Enterprise users also able to select days of the week. ### Source excerpt ClickHouse Managed Postgres now supports scheduled upgrade windows, giving Scale and Enterprise users more control over when routine platform maintenance occurs. ## Managing ClickHouse Dedicated Clusters Through Tinybird's Organization UI and API DevFeed: [Managing ClickHouse Dedicated Clusters Through Tinybird's Organization UI and API]() Original publisher: [Read original article]() Author: Aitana Azcona Published: 2026-09-03T15:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [clickhouse](), [ui](), [API]() Tags: [api](), [clickhouse](), [product-updates](), [ui]() ### AI overview Tinybird provides controls to observe workload signals, resize Dedicated clusters, and rebalance traffic through its Organization UI or API while it operates ClickHouse. ### Source excerpt Observe workload signals, resize Dedicated clusters, and rebalance traffic from the Organization UI or API while Tinybird operates ClickHouse. ## MySQL CDC connector for ClickPipes is now Generally Available DevFeed: [MySQL CDC connector for ClickPipes is now Generally Available]() Original publisher: [Read original article]() Author: Marta Paes Published: 2026-09-02T14:03:04Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [MySQL](), [Infrastructure as code](), [Terraform]() Tags: [analytics](), [api](), [clickhouse](), [cloud](), [data](), [infrastructure-as-code](), [mysql](), [observability](), [release](), [terraform]() ### AI overview ClickHouse announces general availability of its MySQL CDC connector for ClickPipes, which replicates MySQL and MariaDB data into ClickHouse Cloud. The release adds automatic parallel snapshotting, reliability and observability improvements, and configuration through the Cloud API and Terraform. ### Source excerpt Replicate MySQL and MariaDB data into ClickHouse Cloud with the generally available MySQL CDC connector, featuring faster parallel snapshots, safer production defaults, improved observability, and infrastructure-as-code support. ## Using coding agents on a migration: Three practices that mattered DevFeed: [Using coding agents on a migration: Three practices that mattered]() Original publisher: [Read original article]() Author: Chandler Ortman Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [migration](), [clickhouse](), [AI-assisted coding](), [coding](), [configuration](), [Deployment](), [Environment Variables]() Tags: [cleanup](), [clickhouse](), [coding-agents](), [deployment](), [environment-variables](), [migration](), [prompt](), [pull-requests](), [temporal-voices]() ### AI overview The article examines coding agents used during a migration of Temporal Cloud usage and billing data to ClickHouse. It finds that the agents were most useful for cleanup and removal work, with durable cleanup instructions and exhaustive searches helping make that work practical. ### Source excerpt Three lessons from using coding agents during a ClickHouse migration, from cleanup automation to writing durable instructions that stay useful over time. ## From Neon Postgres to ClickHouse Managed Postgres DevFeed: [From Neon Postgres to ClickHouse Managed Postgres]() Original publisher: [Read original article]() Author: Sai Srirampur Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [migration](), [Latency]() Tags: [analytics](), [clickhouse](), [latency](), [migration](), [performance](), [postgres](), [production]() ### AI overview Three teams describe migrating production Postgres workloads from Neon to ClickHouse Managed Postgres. They cite reliability, performance, operational, and cost concerns, and report completing data migrations with ClickPipes in hours. ### Source excerpt Three teams share why they migrated production Postgres workloads from Neon to ClickHouse Managed Postgres and how ClickPipes helped them cut over in hours. [Next page]()