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Clickhouse
High-performance open-source columnar database management system designed for real-time analytics and data warehousing at scale
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ClickHouse Agents: Claude-powered agentic analytics, now in public beta
ClickHouse announced the public beta of ClickHouse Agents, a fully managed agentic analytics service in ClickHouse Cloud powered by Claude. It provides a no-code builder for agents grounded in live ClickHouse data, plus a managed chat experience in the Cloud console, so users need no SQL or separate hosting setup. Built on LibreChat, the service supports natural-language questions, SQL and visualizations, sandboxed Bash, Python, and JavaScript execution, shareable artifacts, skills, memories, MCP-compatible connections, and multi-agent workflows. Claude Sonnet and Haiku are pre-configured, while enterprise controls include SSO, encrypted message storage, and governed data access. ClickHouse says its internal deployment handles roughly 70% of data warehouse queries and processes more than 45 million tokens daily, confirming its view that real-time analytics is a natural context for high-volume agent workloads.
How QRT powers real-time research and risk management at petabyte scale
Qube Research & Technologies (QRT), a global quantitative investment manager headquartered in London, uses ClickHouse Cloud for a data platform that serves researchers and for near-real-time risk monitoring, management, and P&L. Founded in 2018 as a cloud-native firm after the spinout of Credit Suisse’s proprietary trading arm, QRT chose a cloud-based approach rather than inheriting decades of on-premise infrastructure. The platform supports two major systems, while ClickStack consolidates logs, metrics, and traces for observability; alerts, log queries, and metric aggregations run against a single ClickHouse instance using standard SQL. QRT says its previous database was constrained by writer and reader counts and total cluster load, whereas ClickHouse provides greater speed and scalability, and that a unified monitoring backend simplifies maintenance while remaining reliably fast.
What's new in clickhousectl v0.3.0
clickhousectl v0.3.0 adds improvements aimed at agentic workflows, additional installation methods, OAuth for Cloud service queries, and custom configuration files for local servers. It detects coding-agent environments and defaults to JSON on stdout, uses gh-style exit codes, reads CLICKHOUSE_ credentials from a working-directory .env file, and hides deprecated API fields from CLI output. Cloud service queries can authenticate with OAuth without provisioning a Query Endpoint; idled services wake automatically, while stopped services fail with a start hint. For local development, selected versions resolve from installed binaries before network lookup, local use can put ClickHouse on PATH, and named XML, YAML, or YML files overlay built-in defaults. The release also adds Python installation options and extends local init with a postgres/ tree alongside clickhouse/.
ClickHouse appoints new leader for Asia Pacific and expands global go-to-market leadership team
ClickHouse announced an expansion of its global go-to-market leadership team, appointing Ed Lenta as Vice President, Asia Pacific and Japan (APJ). Based in Singapore, Lenta will lead regional go-to-market efforts and accelerate adoption of ClickHouse Cloud and the open-source database, following leadership roles at Databricks, AWS, and VMware. The company also appointed Takeshi Kaneko as Country Manager of Japan and added executives for financial services, public sector, global solutions architecture, and revenue strategy and operations. ClickHouse said the appointments support its growth after surpassing $250 million in annual run-rate revenue, more than triple the prior year, and exceeding 4,000 customers, including more than 1,000 net new customers in one quarter.
Monitor SLAs and scale ClickHouse Cloud with clickhousectl and agents
ClickHouse Cloud can automatically scale infrastructure up and down, but teams may need to monitor latency SLAs for specific workloads such as a frontend dashboard requiring p99 under 200 ms. The guide tags queries with log_comment, reads system.query_log through clickhousectl, and aggregates query_duration_ms over a defined window to measure the SLA. When it is breached, operators compare query volume and latency with Prometheus-compatible CPU, memory, and concurrency metrics to distinguish increased concurrency from resource contention, then choose horizontal replica scaling or vertical replica sizing. A cron-based script can scale out when p99 exceeds a threshold, while an agent can inspect evidence and apply one scaling action using clickhousectl; actions are recorded in the ClickHouse Cloud activity log.
What to look for when selecting a real-time analytical database
Real-time analytical databases must serve low-latency queries over data that is continuously inserted at high volume, rather than merely query a static dataset quickly. The article frames time-to-insight as the combination of time to ingest, time to transform and prepare data, and time to query it. It argues that batch-oriented ingestion, delayed visibility, scheduled materialized-view refreshes, and cache invalidation during writes can make production latency far worse than headline benchmarks. It recommends evaluating freshness under concurrent ingest and queries, whether one engine supports structured analytics, text search, vector similarity, and JSON, and whether index maintenance can avoid blocking ingestion. As an example, Bullet reportedly moved to ClickHouse Cloud, reducing data latency by 99.4% to under five seconds, achieving millisecond queries, and handling 1,000× more data at comparable cost.
Click-a-thon 2026: 24 hours, Bengaluru, and the fastest analytical database on the planet
ClickHouse announces Click-a-thon 2026, its first in-person hackathon in India, scheduled for Bengaluru on August 1–2, 2026. The event invites working tech professionals to form teams of two to four for a 24-hour build with four tracks—Real-Time Analytics, Observability, Data Warehousing, and Agentic AI and Analytics—and a ₹10,00,000 prize pool. Its stated purpose is to bring developers together to build practical systems under tight constraints and experiment with ClickHouse alongside modern open-source tools. The post points to ClickStack for observability, Langfuse for LLM tracing and evaluation, and LibreChat for model and agent interfaces, while describing combinations such as streaming ingestion, materialized views, projections, analytical queries, and tool-using agents over data. Applications close June 25, with shortlists planned for the first week of July; the team captain applies on behalf of the team.
Logging, Metrics, and Distributed Tracing in .NET with OpenTelemetry and ClickStack
The post presents a walkthrough of instrumenting two ASP.NET services—an Order API and a Payment Service—with OpenTelemetry and sending traces, logs, and metrics to ClickStack. The demo uses SQLite for persistence, with Entity Framework Core instrumentation capturing database operations without manually creating spans. The configuration adds ASP.NET, HTTP client, and EF Core instrumentation, custom sources and meters, OpenTelemetry logging, and OTLP/gRPC exporters directed by OTEL_EXPORTER_OTLP_ENDPOINT. In ClickStack, the signals are correlated so a request can be followed across service boundaries, application work, and database calls, while the UI and ClickHouse SQL provide exploration options. The conclusion is that a relatively small ASP.NET setup replaces isolated timeout logs with an end-to-end view of timing, errors, logs, and database activity.
How ClickHouse became fast at joins
Over two years, ClickHouse focused on making joins faster for analytical SQL workloads, moving them from a performance gap to a first-class strength. The first year established the foundation with parallel hash joins, local automatic join reordering, filter pushdown, and execution improvements, producing a 4.4× speedup on the TPC-H SF100 join-heavy workload by version 25.4, compared with 22.4. The second year made optimizations effective by default, adding correlated-subquery support, lazy column replication, runtime filters, and statistics-based join reordering. Correlated queries can now run directly, while compact representations avoid copying repeated values and runtime filtering skips rows before hash-table lookups. Across the same benchmark and hardware, these changes delivered another 6× speedup, for a total 26× improvement; one six-table query fell from 3903.7 seconds to 2.7 while peak memory dropped from 99.1 GiB to 3.9 GiB.
Building a .NET API Gateway with ClickHouse and Aspire
The tutorial builds a .NET distributed application around a YARP API gateway, two backend APIs, an analytics API, a Blazor dashboard, a load generator, and ClickHouse. Aspire’s AppHost coordinates these resources, injects connection strings and service endpoints, orders startup with dependency health checks, and exposes selected services through stable or external endpoints. The gateway records proxied requests in ClickHouse while emitting OpenTelemetry metrics for request counts, duration, and backend failures. A bounded queue and background ingestion keep request logging off the hot path, while a narrow schema and materialized view precompute minute-level aggregates for volume, p95 latency, error rates, and route comparisons. Aspire supplies live telemetry, whereas ClickHouse retains request history for analytical queries and drill-down into slow requests.
TPC-H for less than a cent: ClickHouse Cloud vs. Snowflake, Databricks, BigQuery, and Redshift
ClickHouse Cloud is benchmarked against Snowflake, Databricks, BigQuery, and Redshift on the join-heavy TPC-H workload, with the main comparison using SF100: 100 GB, 866M rows, and 22 queries. Using a single 59-core AWS compute node, the benchmark measures fastest hot runs from three executions with result caching disabled, applies public billing models and comparable Enterprise-tier regional pricing, and publishes scripts, queries, and result files for reproduction. ClickHouse completed SF100 in 19.8 seconds for $0.063 and ranked first on the cost-performance score, while Snowflake Large was faster at 15.9 seconds but cost $0.143; at SF10, ClickHouse ran the workload in 2.9 seconds for $0.009. The results are attributed to two years of join engineering, including faster hash joins, improved planning, correlated subquery support, lazy column replication, runtime filters, and statistics-based join reordering.
ClickHouse Release 26.5
ClickHouse 26.5 is a monthly release containing 38 new features, 51 performance optimizations, and 224 bug fixes. It reports a record number of performance optimizations and introduces a filesystem table function for running SQL against a local file system. Its main query-planning examples push ORDER BY … LIMIT through eligible LEFT or RIGHT JOINs when the sort key depends only on the preserved side, allowing ClickHouse to limit rows before joining. In a TPC-H benchmark at scale factor 100, enabling this optimization reduced fastest runtime from 1.878 seconds to 0.092 seconds, peak memory from 1.88 GiB to 10.98 MiB, and data read by 1.5×. Additional changes include a GROUP BY … LIMIT shortcut for eligible unordered queries, an experimental in-browser clickhouse-client, and per-subquery query-cache controls.
Executable UDFs are now in public beta on ClickHouse Cloud
Executable UDFs are available in public beta on ClickHouse Cloud, allowing Python functions packaged as zip files to be called from SQL like built-in functions. ClickHouse runs them in a pool of long-lived sandboxed processes, keeping model code near the data and supporting use in ad-hoc queries, joins, and materialized views. The demonstration wraps a roughly 270K-parameter PyTorch autoencoder in an embed_trade UDF that consumes 14 features, produces a 32-dimensional embedding and reconstruction error, and scores trades as they enter through a materialized view. Training uses streamed Parquet chunks, incremental StandardScaler fitting, early stopping, and saved model artifacts; the deployed pool keeps the 2MB model hot, so inference occurs once per trade and downstream queries read the resulting table.
Open House 2026 Day 2: 10 years of open source and what the ecosystem is building next
Open House 2026 Day 2 recaps ClickHouse’s tenth year as an open-source project and the ecosystem forming around its data platform. The event reported more than 2,600 contributors, nearly 48,000 GitHub stars, over 200 integrations, and the launch of the House Mates partner program. Announcements included Fivetran’s generally available ClickHouse Cloud destination, a Google Cloud Dataflow Pub/Sub template, Sigma’s public-beta connector and ETL Cache Layer, and a ClickHouse dbt Fusion adapter available in alpha through the CLI. The recap also describes MCP and agent-oriented observability work, including a ClickStack MCP server that translates investigations into optimized ClickHouse queries. Internal testing reported up to 25% fewer tool calls, 2.5 times greater consistency, and nearly 20% better evaluation results.
Open House observability announcements: MCP server, AI Notebooks, and ClickStack Cloud
At Open House, ClickHouse announced three observability updates: ClickStack Cloud entered private preview, AI Notebooks entered beta for Managed ClickStack, and a ClickStack MCP server was introduced in open-source ClickStack. ClickStack Cloud is a fully managed, serverless platform that accepts OpenTelemetry data through a managed endpoint, while Managed ClickStack is generally available for teams controlling collectors, ingestion, compute sizing, schema design, and tuning. AI Notebooks provide a persistent, shareable investigation workspace in which prompts, queries, charts, reasoning steps, findings, branches, intermediate results, and generated queries remain visible and editable. The MCP server exposes the investigative tools used by Notebooks to external agents, routing requests through optimized searches, aggregations, and visualizations rather than relying entirely on ad hoc SQL generation; it can also persist findings in dashboards. Together, the announcements present a choice between managed convenience, operational control, and programmable observability workflows.
Thank you for building with us
At its 2026 user conference in San Francisco, ClickHouse reports surpassing 4,000 customers and $250 million in annual run-rate revenue, while marking nearly ten years since Alexey Milovidov open-sourced the database. The company attributes its growth to a community of contributors and describes use across AI products, financial services, observability, analytics, and infrastructure. New offerings include ClickHouse Agents, a fully managed agentic analytics service powered by Anthropic’s Claude; CostBench, an open benchmark using each warehouse’s real billing model; and House Mates, a partner program launching with more than 50 partners across six continents. Additional updates include Managed Postgres in public beta, Managed ClickStack and Langfuse for AI-era observability, generally available full-text search, and automatic JOIN optimization, with cost-performance identified as the central constraint for high-concurrency, low-latency workloads.
ClickHouse tops $250M ARR and 4,000 customers, launches Claude-powered agents at Open House 2026
At Open House 2026, ClickHouse announced that its serverless cloud offering exceeded $250 million in annual run-rate revenue and reached 4,000 customers, with ARR more than tripling year-over-year and more than 1,000 net new customers added since January. The company launched ClickHouse Agents, a fully managed, no-code agentic analytics service powered by Anthropic’s Claude, with a chat interface, sandboxed code interpreter, artifacts, skills, memory, multi-agent workflows, and MCP-compatible integrations. It also published CostBench, an open benchmark applying each vendor’s real compute billing model to the same workload; ClickHouse Cloud was reported to have 23x better cost-performance than the nearest cloud data warehouse. Other announcements included Managed Postgres in public beta, managed ClickStack, generally available full-text search, cross-region replication, and the House Mates partner program.
Introducing House Mates: the ClickHouse partner community and program
ClickHouse introduces House Mates, a partner community and program bringing together more than 60 ISV, consulting, services, and channel partners to help customers build, scale, and procure ClickHouse solutions. The inaugural cohort includes over 25 ISVs developing validated, production-ready integrations—among them dbt Labs, Fivetran, Temporal, Grafana Labs, Confluent, and Sigma—and over 35 consulting, services, and reseller partners trained and certified through ClickHouse Academy. The program addresses an ecosystem with more than 200 integrations in the wild, demand for trusted regional expertise and preferred procurement channels, and the limits of managing partnerships through one-off arrangements. Its Ignite, Accelerate, and Prime tiers define progression from enablement and first projects or integrations to deeper go-to-market, incentives, co-innovation, and strategic collaboration at scale, while customers gain validated tools, regional experts, and preferred purchasing routes.
5 countries, 12 days, one ClickHouse: inside Alexey's APJ AI Tour
ClickHouse CTO and creator Alexey Milovidov completed a 12-day APJ tour spanning India, Singapore, Indonesia, South Korea, and Japan, with customer meetings, industry events, executive sessions, press interviews, and community meetups. The itinerary covered six cities and included three Confluent Data Streaming World Tour events, four ClickHouse meetups, and more than 10 enterprise customer meetings across varied industries and workloads. Discussions centered on real-time analytics, connected products, and analytics at industrial scale, while in-person conversations supplied feedback that the post says helps improve ClickHouse. The trip concluded with the ClickHouse Japan Launch Event on April 27, marking the company’s official entry into that market. The account presents physical presence as a way to extend ClickHouse’s open-building culture beyond GitHub and strengthen its feedback loop with APJ customers and communities.
Open House 2026 Day 1: Real-time data without lock-in and what teams can build next
ClickHouse’s Open House 2026 Day 1 announced updates spanning ClickHouse Cloud, Postgres, distributed query execution, observability, AI agents, and lakehouse interoperability. ClickHouse Postgres entered public beta with local NVMe storage, high availability, S3-backed point-in-time recovery, and a native CDC path into ClickHouse; early benchmarks reported more than 5x the transactions per second of AWS RDS. Multi-stage distributed queries repartition intermediate data between stages to scale joins and high-cardinality aggregations, reducing TPC-H SF100 runtime from 117.6 seconds on one node to 54.7 seconds on eight nodes. Additional launches included a Claude-powered ClickHouse Agents public beta, an AI-enhanced query builder, Monitoring v2, and Langfuse V4’s reported 200x query performance improvement, while the wrap-up framed the releases around real-time systems without lock-in.
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