---
title: "Clickhouse"
description: "High-performance open-source columnar database management system designed for real-time analytics and data warehousing at scale"
---

# Clickhouse
> High-performance open-source columnar database management system designed for real-time analytics and data warehousing at scale

## Articles

### [ClickHouse Release 26.6](https://yomu.fyi/post/clickhouse-release-26-6.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 3, 2026
- Type: Announcement

ClickHouse 26.6 is a release marking the 10-year anniversary of ClickHouse becoming open source, with 56 new features, 79 performance optimizations, and 366 bug fixes. Hypothetical skip indexes let users evaluate set indexes in a session without building them; on a 487.24-million-row UK property dataset, the example estimated skip ratios of 77.0% at granularity 128 and 92.2% at granularity 1. The release overhauls dependencies between refreshable materialized views, addressing schedule drift that could make dependent stages lag or miss a refresh cycle. It also introduces experimental continuous queries, the aiEmbed function, embedding quantization to 8 bits and below, and workload-level memory reservations.


### [How we made ClickStack 5x faster for ClickHouse observability](https://yomu.fyi/post/how-we-made-clickstack-5x-faster-for-clickhouse-observability.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 2, 2026
- Type: Problem & solution

ClickStack’s inherited OpenTelemetry Collector schema was struggling with slow searches, Top N queries, and aggregations that could time out on large observability workloads. The team prioritized common log and trace access patterns, including exact-attribute searches, body-text queries, histograms, individual log lookups, and distinct-key or value introspection. They used fixed production-like workloads and iterative testing with ClickCannon, Kubernetes-managed environments, ephemeral ClickHouse Cloud instances, and controlled schema, query, and version comparisons. The resulting redesign combined primary-key changes, text indexes, query rewrites, alias columns, materialized views, and newer ClickHouse features, reducing representative query latency by more than 5× while balancing ingestion, storage, and operational efficiency.


### [ClickHouse Agents is now available for Managed Postgres](https://yomu.fyi/post/clickhouse-agents-is-now-available-for-managed-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 2, 2026
- Type: Announcement

ClickHouse Agents is now available for all Managed Postgres services in ClickHouse Cloud, extending its plain-English agent interface to operational data. The beta service, powered by Claude and built on LibreChat, combines an agent builder and chat interface with a sandboxed code interpreter; it is read-only by default and supports MCP-compatible systems. Agents can inspect Postgres schemas, run read-only queries, return the SQL used, and reason across Postgres and ClickHouse by querying each engine independently before merging results. Additional skills guide migration from Postgres to ClickHouse through discovery, access-pattern analysis, schema design, type mapping, CDC ingestion, and validation, while other workflows use metrics, Query Insights, and EXPLAIN to rank performance fixes. Planned extensions include scheduled recommendations and safe automated remediation, but the current release focuses on exploration, analysis, monitoring, and guided database work.


### [How we scale PgBouncer in ClickHouse Managed Postgres](https://yomu.fyi/post/how-we-scale-pgbouncer-in-clickhouse-managed-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 1, 2026
- Type: Benchmark

PgBouncer is single-threaded, so a single process can bottleneck connection pooling on multi-vCPU machines even when Postgres has capacity. ClickHouse Managed Postgres runs a fleet of PgBouncer processes sized to available cores, sharing one port through so\_reuseport; peering forwards query-cancellation requests to the process that owns the session, while transaction pooling and per-process connection-budget splits limit Postgres connections. In tests on identical AWS EC2 c7i.4xlarge pooler instances, a 16-process fleet reached roughly 336,000 transactions per second at 256 clients, compared with 76,893 for one process, while using about 49% versus 8% in-guest CPU. The fleet's advantage emerged under concurrency, whereas the single process was slightly faster at eight clients, and the described setup ships by default with every ClickHouse Managed Postgres server.


### [How Artemis Security runs 69x faster detection queries with ClickHouse Cloud](https://yomu.fyi/post/how-artemis-security-runs-69x-faster-detection-queries-with-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 1, 2026
- Type: Benchmark

Artemis Security uses ClickHouse Cloud on AWS for real-time threat detection across many terabytes of compressed logs, tens of billions of daily rows, and queries over trillions of rows. To reduce repeated scans, its engineers coalesce hundreds of detection-rule predicates into one query, tagging matching rows by rule ID; batches of more than 100 rules now run in under a second, with a measured 69x speedup. A Claude Code skill called /poke connects ClickHouse system tables, CloudWatch, Datadog, application data, and source code to investigate infrastructure issues through natural-language prompts. For investigative queries over semi-structured JSON, Artemis extracts frequently queried fields into a parallel materialized table and uses a hybrid two-step lookup, producing 30–60x faster queries and up to 400x lower CPU consumption.


### [A Quadrillion Rows across three Clouds: scaling LogHouse](https://yomu.fyi/post/a-quadrillion-rows-across-three-clouds-scaling-loghouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 30, 2026
- Type: Problem & solution

LogHouse now stores 431 PiB of uncompressed data across 1.59 quadrillion rows, spans more than 30 regions across three cloud providers, and serves typical queries in a few hundred milliseconds. The platform grew 23x from 19 PiB in two years, while peak ingestion reached 80 GiB/s and 190 million rows per second. Its write architecture uses local geosharding, multiple cells in busy regions, and ClickHouse Async Inserts to combine small incoming writes into larger parts without overwhelming merges. On the read side, a single Distributed table hides the cell topology, while unavailable shards remain visible by default rather than producing silently incomplete results. Current work includes reducing index and metadata memory, improving async-insert durability, collecting system data from object storage, and expanding OpenTelemetry traces and metrics.


### [Clever ingests 200x more logs at the same cost with ClickHouse Cloud](https://yomu.fyi/post/clever-ingests-200x-more-logs-at-the-same-cost-with-clickhouse-cloud.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 30, 2026
- Type: Problem & solution

Clever, an identity platform for K-12 education, faced a fragmented logging setup as its 400 services and 200 AWS Lambdas generated about 150 TB of uncompressed logs monthly. Datadog indexed only about 10% and retained it for three days, while the remainder was in Athena and required SQL. Clever migrated to ClickHouse Cloud on AWS, indexing all logs with 60-day retention and reporting 200× more searchable log volume at no additional cost, with 10× compression. Its design extracts frequently queried JSON fields into columns, stores remaining attributes in a map alongside raw JSON, uses skipping indexes, date partitions, a 60-day TTL, and reverse Unix timestamp ordering; this ordering produced an 8–10× query speedup. A custom query layer translates LogQL to SQL, while separate development and production services and multi-region deployment address isolation and availability.


### [How Jua delivers the world’s most accurate physics simulations 3x faster with ClickHouse Cloud](https://yomu.fyi/post/how-jua-delivers-the-world-s-most-accurate-physics-simulations-3x-fast.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 30, 2026
- Type: Problem & solution

Jua uses ClickHouse Cloud to deliver data from EPT-2, its physics foundation model, to energy traders and grid operators faster than its previous file-based pipeline. Jua’s forecasts combine outputs from multiple models and add roughly four terabytes of new data daily, producing about one petabyte compressed and over 10–12 petabytes uncompressed. Replacing Zarr and other file-based storage plus manual pre-fetching with ClickHouse Cloud reduced flagship forecast delivery from one hour to 20 minutes, with about 20 seconds of upload overhead. The platform also cut compute costs by roughly a third, reduced two-year single-location historical queries from hours to seconds, and supports both point lookups and regional aggregations; Jua selected the managed service for operational simplicity, separated storage and compute, and cost efficiency.


### [Agents hate friction: early thoughts on building for agents](https://yomu.fyi/post/agents-hate-friction-early-thoughts-on-building-for-agents.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 29, 2026
- Type: Explainer

Designing software for LLMs and agents requires more than extending human developer experience: a polished text-first interface can still produce an AX that is merely functional. The post frames an agent as a combination of model, harness, context, and skills, with behavior varying across providers, models, and harnesses; it focuses attention on commonly used combinations while testing beyond them. In a test setup, agents receive goals and different amounts of context, and model-harness combinations are evaluated by how they reason, which tools they call, how many steps they take, and how consistently they repeat a task. The resulting design questions include friction, context and token efficiency, familiarity with established tools, and the trade-off between fewer steps and higher context or cost; the central conclusion is that agents may abandon a product when friction persists, making testing necessary to expose that behavior.


### [Benchmarks and Obscurantism: A “red” line that should not be crossed](https://yomu.fyi/post/benchmarks-and-obscurantism-a-red-line-that-should-not-be-crossed.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 29, 2026
- Type: Benchmark

Melvyn Peignon examines a Databricks keynote benchmark in which ClickHouse was shown “crashing” under workloads associated with Reyden, Databricks’ gated low-latency compute product. The post argues that the presentation omitted hardware, cost, configuration, cache settings, deployment details, and sufficient methodology for independent validation. Using the disclosed TPC-H SF1 dataset and Q6 query, ClickHouse was tested on a 30 vCPU, 120 GB node without tuning; it did not crash and sustained about 420 QPS at sub-second P90 latency. The post reports that reaching 15,000 QPS required straightforward sizing to roughly 30–40 untuned nodes, while further gains could come from settings such as thread counts, query cache, and projections. It concludes that benchmarks should be open, reproducible, and detailed enough to inspect rather than accepted as keynote claims.


### [How I hunt for vulnerabilities with AI](https://yomu.fyi/post/how-i-hunt-for-vulnerabilities-with-ai.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 26, 2026
- Type: Tutorial

The author describes using AI-assisted code review to hunt for vulnerabilities in ClickHouse, a large C++ codebase, despite having limited bug-bounty experience. GitHub Copilot, Claude, ChatGPT, and Gemini models were used to inspect code, generate vulnerability hypotheses, explain unfamiliar subsystems, and identify older or newly introduced attack surfaces. Promising ideas were recorded, then investigated with Python scripts against a locally running ClickHouse Docker container, followed by manual review and reproduction to distinguish genuine findings from hallucinations and misconfigurations. The work produced a few real vulnerabilities that were reported to ClickHouse’s security team and bug bounty program, while the broader conclusion is that AI can accelerate exploration and validation but still requires persistent questioning and human verification.


### [ClickHouse achieves AWS Cloud Operations Competency](https://yomu.fyi/post/clickhouse-achieves-aws-cloud-operations-competency.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 26, 2026
- Type: Announcement

ClickHouse has achieved the AWS Cloud Operations Competency in the Monitoring and Observability category, recognizing validated expertise in operating reliable AWS systems with logs, metrics, and traces. The post describes observability as a high-volume, high-cardinality, high-concurrency workload where legacy systems may require sampling, field reduction, or short retention. ClickHouse Cloud uses columnar storage, compression, parallel queries, compute-compute separation, and ClickPipes integrations with Amazon Kinesis, Amazon MSK, and Amazon S3 to support full-fidelity telemetry. Customer examples include Modal’s 1 to 2 million events per minute, Exabeam’s 1.2 million events per second per region, and Qonto’s reduction of 231 TB to 376 GB. The company also describes MCP servers, AI Notebooks, and managed ClickStack as investments for collaborative incident investigation.


### [Announcing Silk: a silky smooth fiber runtime for ClickHouse](https://yomu.fyi/post/announcing-silk-a-silky-smooth-fiber-runtime-for-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 25, 2026
- Type: Announcement

Silk is a C++ stackful-fiber library and scheduler created for ClickHouse’s highly concurrent, I/O-bound components, including object-storage I/O, distributed cache lookups, replica coordination, and HTTP fan-out. Its design combines one pinned OS scheduler thread per CPU, NUMA-aware work stealing, io\_uring for asynchronous I/O and timers, and zero heap allocation in the steady-state hot path; fiber stacks are mmap’d from per-fiber pools with guard pages, while runtime containers are intrusive. The post reports benchmarks including roughly 3.6-nanosecond fiber yields with cross-CPU stealing, 5.9 million file IOPS in a working configuration, and about fifteen times boost::asio’s throughput at one connection. At 10,000 concurrent S3-style requests, internal measurements showed a roughly 65% improvement in 99.9th-percentile latency versus an equivalent thread-pool executor, with identical median throughput; distributed cache is the first intended ClickHouse integration.


### [ClickHouse is now a native connection in Notion Custom Agents](https://yomu.fyi/post/clickhouse-is-now-a-native-connection-in-notion-custom-agents.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 25, 2026
- Type: Announcement

ClickHouse is now a native, preconfigured connection in Notion Custom Agents, allowing agents to access ClickHouse Cloud data without leaving Notion. After an OAuth connection, agents can explore schemas, translate plain-language questions into ClickHouse SQL, run read-only queries, and return results in the workspace. The connection exposes 14 read-only tools covering databases and tables, SELECT queries against ClickHouse and Managed Postgres, and organization, service, ClickPipes, backup, and cost information; users can set each tool to run automatically or require approval. Custom Agents can run on a schedule, including a daily observability report that queries error rates, p99 latency, and request volume from a Managed ClickStack service. The connector is in beta and requires a ClickHouse Cloud service with Remote MCP enabled plus a Notion Business or Enterprise workspace.


### [Why we rewrote WAL-G for Postgres backups in Rust: Meet WAL-RUS](https://yomu.fyi/post/why-we-rewrote-wal-g-for-postgres-backups-in-rust-meet-wal-rus.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 25, 2026
- Type: Problem & solution

ClickHouse Cloud built WAL-RUS, an open-source Rust implementation of PostgreSQL backup and WAL archival tooling, after encountering difficulty predicting WAL-G’s memory usage in resource-constrained environments. The project preserves WAL-G’s core functionality and compatibility while using bounded worker pools, controlled concurrency, persistent object-storage connections, and a streaming pipeline that limits buffering and data copies. It uses the same WALG\_ configuration variables, and WAL-G and WAL-RUS can read each other’s archives. In a sustained WAL-heavy benchmark with four archival workers, WAL-RUS stayed below 1 GB of peak virtual memory versus nearly 2.8 GB for WAL-G, while both maintained minimal backlog and comparable CPU usage. The authors plan to make WAL-RUS the default backup and archival mechanism for ClickHouse Cloud’s managed Postgres offering and are working to upstream its Postgres 17 wal summaries support to WAL-G.


### [How Vibe.co handles billions of ad impressions with ClickHouse Cloud](https://yomu.fyi/post/how-vibe-co-handles-billions-of-ad-impressions-with-clickhouse-cloud.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 25, 2026
- Type: Problem & solution

Vibe.co, recently acquired by Walmart, uses ClickHouse Cloud to provide real-time campaign reporting for thousands of Connected TV advertisers across billions of ad impressions. It migrated from Postgres after aggregation-heavy OLAP queries made its pre-aggregation layer a bottleneck, limiting interactive slicing and requiring new jobs for reporting use cases. The team evaluated self-hosted ClickHouse, another managed option, and ClickHouse Cloud, choosing the latter for operational simplicity, support from ClickHouse engineers, and separation of storage and compute with more predictable costs. Vibe uses refreshable materialized views for hot/cold tiering, keeping the last 30 days in a hot table; stored data grew from about 100 GB to over 2 TB without rearchitecting, while more than 90% of client campaign reports respond in under 100ms.


### [How Visa went from multi-day reporting to conversational analytics agents with ClickHouse Cloud and LibreChat](https://yomu.fyi/post/how-visa-went-from-multi-day-reporting-to-conversational-analytics-age.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 24, 2026
- Type: Problem & solution

Visa used ClickHouse Cloud to modernize reporting on Authorize.net payments data, replacing multi-day analyst-led turnaround with natural-language queries for internal teams. The solution pairs LibreChat, an open-source conversational interface, with the ClickHouse MCP server and runs on ClickHouse Cloud on AWS, allowing agents to explore and query a roughly 40 PB data footprint containing millions of rows. ClickHouse materialized views pre-compute and incrementally update results, reducing scanned data and bringing example analyses down to 100–200 milliseconds, with reported speedups of roughly 30–70 times. The platform also uses encrypted transfers, Amazon S3, VPC PrivateLink, change data capture, and federated access controls, while Visa reports identifying millions of dollars in revenue risk and reclaiming 8–10 hours per user each week.


### [Announcing the Managed ClickStack MCP Server](https://yomu.fyi/post/announcing-the-managed-clickstack-mcp-server.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 24, 2026
- Type: Announcement

Managed ClickStack in ClickHouse Cloud now includes the ClickStack MCP server, extending its observability investigation capabilities to external agents and AI workflows. Unlike the generic ClickHouse MCP server’s SQL-oriented interface, it offers semantic tools for log patterns, outlier correlation, slow traces, and repeatable investigations across logs, metrics, and traces. For event patterns, the server samples matching events, processes them with Drain3 in the MCP layer, and extrapolates and ranks counts, reducing output cardinality and making query overhead fixed rather than linear in the full dataset. Internal benchmarks report 25% fewer tool calls, 2.5x greater consistency, and an almost 20% increase in evaluation scores versus the standard ClickHouse MCP. Managed deployments also support full-fidelity telemetry, dedicated compute for agent workloads, and orchestration such as persisted dashboards, while native SQL remains available for unusual or deeper questions.


### [What's New in pg\_clickhouse v0.3.2: Postgres 19, TLS, Regex, and Memory](https://yomu.fyi/post/what-s-new-in-pg-clickhouse-v0-3-2-postgres-19-tls-regex-and-memory.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 23, 2026
- Type: Announcement

pg\_clickhouse v0.3.2 updates the Postgres interface for querying ClickHouse, with changes centered on PostgreSQL 19 Beta1 support, TLS configuration, regular-expression pushdown, and memory consumption. The release adds CREATE SERVER options for forcing TLS or plaintext, selecting a minimum TLS version, and enabling ClickHouse native-protocol compression. Revised pushdown logic maps Postgres regular-expression flags to ClickHouse behavior, while regexp\_match() can now be pushed down when its pattern has no capturing groups. Two memory issues were fixed: one involving unbuffered HTTP-driver queries and another involving repeated foreign scans such as parameterized inner scans in nested-loop joins. The minor update requires no reload, restart, or ALTER EXTENSION UPDATE, and also fixes empty-array ANY() errors on ClickHouse versions before 25.


### [What's new in ClickStack - May 2026](https://yomu.fyi/post/what-s-new-in-clickstack-may-2026.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 23, 2026
- Type: Announcement

ClickStack’s May and early-June release focused on making observability dashboards more useful during investigations and expanding access to related workflows. Dashboard table actions can open searches against selected sources with templated WHERE clauses or link to other dashboards, while scoped filters prevent source-specific conditions from affecting unrelated tiles. Number visualizations gained fixed or threshold-based colors and automatic text scaling, and the service map added latency percentiles, throughput metrics, server-side filtering, and focus mode. The release also includes a Browser RUM dashboard template, experimental PromQL support through the ClickHouse TimeSeries Engine, expanded MCP tools, AI Notebooks beta for Managed ClickStack, and ClickStack Cloud in private preview.


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