---
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

### [The end-to-end cost-performance of real-time analytics: Snowflake vs. ClickHouse Cloud](https://yomu.fyi/post/the-end-to-end-cost-performance-of-real-time-analytics-snowflake-vs-cl.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 23, 2026
- Type: Benchmark

CostBench compares Snowflake and ClickHouse Cloud as end-to-end real-time analytics systems rather than measuring reads on a static, prepared dataset. It evaluates continuous ingest, raw-data organization, pre-aggregation freshness, and continuous query serving while background work continues, testing Snowflake’s standard-table and Interactive Tables paths alongside ClickHouse. The benchmark is designed around fresh data becoming query-ready as it arrives and distinguishes sustained real-time ingest from bulk loading or backfill. In the reported results, ClickHouse achieved 969× better cost-performance than Snowflake’s standard materialized-view setup and 180× better than its Interactive Tables setup; among the tested paths, only ClickHouse incremental materialized views kept pre-aggregations continuously synchronized without lag.


### [The future of observability won’t be one proprietary AI agent. It will be thousands built by teams.](https://yomu.fyi/post/the-future-of-observability-won-t-be-one-proprietary-ai-agent-it-will.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 22, 2026
- Type: Explainer

Observability vendors are increasingly betting on a universal SRE agent that combines a platform, telemetry, and natural-language investigation. The post argues that debugging depends on team-specific systems, trusted signals, failure history, runbooks, workflows, and institutional knowledge that cannot be packaged into one vendor’s agent. Agents can compare more time windows and pursue more hypotheses than human investigators, creating demand for low-latency query infrastructure and more complete, high-fidelity historical data. It proposes a more open model in which teams build specialized agents on headless observability foundations, with APIs, data storage, and an observability MCP, while persistent investigation surfaces preserve work for collaboration and reuse. Humans remain responsible for direction, judgment, and action, so the near-term future is presented as human-agent collaboration rather than autonomous incident resolution.


### [How Spyne simplified their CDC pipeline with ClickPipes and ClickHouse Cloud](https://yomu.fyi/post/how-spyne-simplified-their-cdc-pipeline-with-clickpipes-and-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 22, 2026
- Type: Problem & solution

Spyne’s CDC pipeline streamed changes from MySQL and MongoDB through self-managed Debezium, Kafka, and ClickHouse, but table onboarding required 1 to 1.5 hours of manual work and schema drift could silently exclude new columns. The team migrated to ClickPipes and ClickHouse Cloud, where pipelines can be configured in minutes and automated schema evolution applies upstream changes to corresponding ClickHouse tables without manual intervention or restarts. To manage snapshot pauses, Spyne isolated about 10 critical historical tables in dedicated ClickPipes and grouped lower-priority tables separately. The migration also required using FINAL in downstream queries to handle ReplacingMergeTree eventual consistency, while binlog gaps may still require full table resynchronization and higher costs. Spyne reports less operational toil, faster feature and analysis workflows, and improved data consistency, accepting reduced recovery control and query-time CPU trade-offs.


### [Ten years of open source: a stained-glass view](https://yomu.fyi/post/ten-years-of-open-source-a-stained-glass-view.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 19, 2026
- Type: Explainer

ClickHouse’s ten-year open-source history is presented through a stained-glass analogy: many contributors remain visible as distinct pieces, while shared infrastructure holds the project together. The article connects that community account to production use through cBioPortal at Memorial Sloan Kettering, where moving heavy filtering into ClickHouse SQL made relevant paths about ten times faster, changing waits from minutes to seconds and supporting continued exploration. It also describes a demonstration built from bare-clone git history, one month of GH Archive events, and the shipped system.contributors table, all queried in ClickHouse. The account reports 100,000 issues and pull requests crossed on March 19, 2026, with 83% of issues and 99% of pull requests resolved at the time of the talk, and presents contributor credit and repair as operating parts of the project.


### [Beyond the warehouse: How METRO Markets built a do-it-all data platform on ClickHouse Cloud](https://yomu.fyi/post/beyond-the-warehouse-how-metro-markets-built-a-do-it-all-data-platform.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 18, 2026
- Type: Problem & solution

METRO Markets outgrew its self-hosted Impala and Kudu-based Hadoop stack as the marketplace expanded, with parallel stores, inconsistent reporting, and too much maintenance burdening a lean data team. They evaluated BigQuery, Snowflake, and ClickHouse against production queries, finding faster ClickHouse performance, including one response falling from four seconds to under 200 milliseconds, while its pricing supported more predictable costs. Airbyte moves CDC data from MySQL and Postgres into ClickHouse; ReplacingMergeTree handles deduplication during background merges, and BigQuery-hosted Google Analytics data enters through cloud storage and external tables. The resulting warehouse consolidates company data and supports seller analytics, credit risk features through Query API endpoints, observability, and emerging AI use cases, while teams query a shared dataset through compatible tools; planned Kafka and Flink integration would extend it to streaming workloads.


### [Why everyone is talking about real-time analytics (a note from “the yellow company”)](https://yomu.fyi/post/why-everyone-is-talking-about-real-time-analytics-a-note-from-the-yell.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 18, 2026
- Type: Explainer

Real-time analytics is presented as a foundational workload for customer-facing analytics, observability, fraud detection, operational applications, and AI agents rather than a niche use case. The discussion argues that performance claims require open, transparent, reproducible benchmarks that disclose methodology, datasets, queries, results, and operating conditions, including continuous ingestion, concurrency, memory pressure, and fresh data. A real-time database should support queryable data within milliseconds to a couple of seconds, incremental transformations, bounded tail latency as writes increase, configurable read-time versus write-time work, unified support for text, vector, JSON, and structured analytics, and efficiency as workload scales. The article connects renewed attention to agentic workloads, whose continuous, high-volume querying makes low latency, concurrency, and fresh context increasingly necessary, and calls for shared standards beyond a single latency number.


### [Appcues delivers personalized customer engagement with ClickHouse Cloud](https://yomu.fyi/post/appcues-delivers-personalized-customer-engagement-with-clickhouse-clou.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 18, 2026
- Type: Problem & solution

Appcues replaced an analytics stack that included Snowflake and Airflow after rising scale pushed queries to 20 seconds or more and limited reports to 10-minute windows. The company evaluated ClickHouse Cloud on AWS for real-time segmentation, exporting Snowflake data to Parquet in S3 and testing complex email segments with nested conditions, historical events, and negative logic. Production events now pass through Kafka and a clickwriter consumer, while a clickclient library built on Elixir’s Ecto generates controlled ClickHouse queries; SharedReplacingMergeTree, table engines, and materialized views were tuned during a one-year migration. Across 1.31 PB and 410 billion events, P95 queries fell below two seconds, ingestion latency dropped to about five seconds, and analytics costs decreased 23%, while new funnel reports and AI-powered insights were added.


### [What's new in Postgres Managed by ClickHouse: RBAC, Terraform, ClickPipes, extensions, and more](https://yomu.fyi/post/what-s-new-in-postgres-managed-by-clickhouse-rbac-terraform-clickpipes.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 18, 2026
- Type: Announcement

Postgres managed by ClickHouse, which entered public beta in May, received a broad update covering console operations, per-service RBAC, Terraform, Stripe provisioning, ClickPipes migrations, billing, capacity reservations, and extensions. The redesigned console adds service health, resource, and query views, while RBAC provides View and Manage roles across the console and public REST API; the alpha Terraform provider covers lifecycle operations including resizing, HA, replicas, restore, and password rotation. Postgres-to-Postgres ClickPipes now automate schema migration, usage is integrated into ClickHouse Cloud’s metering and billing, and AWS capacity reservations are allocated dynamically across availability zones. Underneath, pg\_clickhouse and pg\_stat\_ch moved to the header-only clickhouse-c library, removing a C++ exception conflict, using Postgres memory contexts, and reducing the x86-64 Linux extension binary from 4.9 MB to 1.4 MB; the update shipped less than a month after beta, with Terraform and extensions still evolving.


### [June 2026 newsletter](https://yomu.fyi/post/june-2026-newsletter.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 18, 2026
- Type: Announcement

ClickHouse’s June 2026 newsletter marks the project’s ten years as open source, noting more than 2,000 contributors and community projects alongside engineering updates. It profiles Vasily Chekalkin’s 26.5 improvements to WebAssembly user-defined functions, including the DETERMINISTIC keyword and visibility in system.functions, and covers native Jaeger storage, the ClickCannon benchmark tool, and Open House announcements. The 26.5 release reports Top-N optimizations that make ORDER BY … LIMIT through JOIN 20.4× faster with 175× less memory on TPC-H SF100, while GROUP BY … LIMIT is 11.9× faster with 185× less peak memory. Other featured systems include a 15-million-rows-per-hour KPI pipeline, join-engineering work yielding a 26× improvement on join-heavy TPC-H workloads, and community guidance on inserts, columnar storage, ArrowStream, and RAG ingestion.


### [GCP Pub/Sub connector for ClickPipes is now in Private Preview](https://yomu.fyi/post/gcp-pub-sub-connector-for-clickpipes-is-now-in-private-preview.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 17, 2026
- Type: Announcement

ClickHouse has announced a GCP Pub/Sub connector for ClickPipes, now in Private Preview, to stream GCP infrastructure logs, application events, and operational signals into ClickHouse Cloud in real time. Previously, routing data from Pub/Sub into ClickHouse required GCS or a Dataflow pipeline; the connector instead manages subscription lifecycle, schema inference, and message delivery after a topic and target table are configured. It supports JSON, Avro, and Protobuf, Pub/Sub Schema Registry integration, automatic decompression, attribute-based filtering, and start positions of latest, earliest, or a specific timestamp. ClickPipes supports horizontal and vertical scaling, configurable replicas, automatic retries, built-in metrics and logs, and programmatic management through OpenAPI and the ClickHouse Terraform Provider. The connector is available upon request and free during Private Preview, with access offered through a waitlist or a ClickHouse Cloud account manager.


### [The open ecosystem around ClickHouse](https://yomu.fyi/post/the-open-ecosystem-around-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 17, 2026
- Type: Explainer

ClickHouse’s open ecosystem spans clients, dashboards, ingestion pipelines, projects built on its engine, and agents that query it, with 2,600+ contributors, 240,000+ commits, and hundreds of millions of downloads cited. It describes open standards including OpenTelemetry for observability, Model Context Protocol (MCP) for AI tooling, and Iceberg and Delta Lake for lakehouse data, plus Iceberg REST catalogs, Apache Polaris, and Unity Catalog. The surrounding stack includes ClickStack, chDB, Langfuse, LibreChat, official language clients, a Kubernetes operator, Postgres bridges such as PeerDB and pg\_clickhouse, and integrations with Airflow, dbt, Grafana, Fivetran, and Sigma. Specific updates include full Iceberg v2 support, Iceberg writes in 25.8, Paimon in 25.10, chDB v4’s DataStore API, and a Python client v1.0 with native async and 4x faster cold starts. The post frames this openness as supporting flexible tooling and community-led growth, reporting more than 200 integrations while noting that the ecosystem’s next direction remains unknown.


### [92x faster queries: How Open Electricity uses ClickHouse to track Australia’s energy transition in real time](https://yomu.fyi/post/92x-faster-queries-how-open-electricity-uses-clickhouse-to-track-austr.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 17, 2026
- Type: Problem & solution

Open Electricity uses ClickHouse to serve live Australian energy data through a public platform and API spanning roughly one billion records. Migrating from Postgres and TimescaleDB addressed queries that could take more than five minutes, reducing a benchmark to just over three seconds and to 60 milliseconds with a materialized view. The new setup keeps heavy relational workloads in Postgres while ClickHouse handles analytics, using ReplacingMergeTree base tables and more than 20 materialized views for different aggregation patterns. It also uses substantially less hardware than the previous configuration: four cores, four GB RAM, and 200 GB disk versus 16 cores, 32 GB RAM, and 1.6 TB, while serving data added daily and API users.


### [What a difference 10 years of open source makes](https://yomu.fyi/post/what-a-difference-10-years-of-open-source-makes.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 16, 2026
- Type: Explainer

ClickHouse marks ten years since becoming open source under the Apache 2.0 license, crediting more than 2,600 contributors and a decade of monthly releases for its growth into one of the world’s most widely deployed analytical databases. Recent engineering work spans joins, updates and deletes, JSON, full-text search, vector search, query acceleration, integrations, and developer tooling. Join-focused changes made the TPC-H SF100 workload 26× faster between versions 22.4 and 26.4, while statistics-based reordering reduced one six-table query from 3,903.7 seconds to 2.7 seconds. The project also publishes open benchmarks, including ClickBench, JSONBench, CostBench, and PostgresBench, with scripts, configurations, data, and results intended for independent validation. Its next phase continues to emphasize community contributions and broader ways to use ClickHouse.


### [Ten years of ClickHouse in open source](https://yomu.fyi/post/ten-years-of-clickhouse-in-open-source.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 15, 2026
- Type: Explainer

ClickHouse’s tenth-anniversary history describes its path from an internal web-analytics experiment to an open-source analytical database with more than 2,000 contributors. The original problem was processing rapidly growing, real-time logs while answering flexible reports quickly; tested MySQL extensions and standalone analytical databases did not meet the stated scale of 100 billion records per day and 500 columns. The project built column-oriented storage, lightweight compression, custom aggregation structures, background merges, and query support for grouping, aggregation, filtering, and sorting. Released publicly on June 15, 2016, ClickHouse is presented as a level-three open-source project with public contribution guidelines, code review, testing, CI, documentation, and a roadmap, while contributors receive credit even when implementations are substantially rewritten.


### [Cisco Talos empowers threat researchers while reducing TCO by 75% with ClickHouse Cloud](https://yomu.fyi/post/cisco-talos-empowers-threat-researchers-while-reducing-tco-by-75-with.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 15, 2026
- Type: Problem & solution

Cisco Talos operates a threat-intelligence reputation service that classifies file hashes from Cisco product telemetry, enabling researchers to make security decisions about benign and potentially malicious artifacts. Previously, a custom pipeline moved data from Databricks Delta and S3 through EKS, where staging and aggregated tables supported an API; the self-managed design retained about a petabyte of compressed, replicated data on 32 high-compute nodes. The team rebuilt the system in four phases, using Terraform, SSO/SAML, sharded ClickPipes for a 12-month backfill, shadow traffic, and a validated cutover to ClickHouse Cloud on AWS. The resulting architecture uses EventBridge, SQS, ClickPipes, SharedMergeTree, object storage, and AWS PrivateLink, delivering less than a minute of queue lag, zero data loss, a 100% response match across more than 1,000 hashes, zero downtime, roughly 90% lower storage costs, and about 75% lower TCO while reclaiming over 300 engineer hours annually.


### [ClickHouse achieves AWS Retail Competency](https://yomu.fyi/post/clickhouse-achieves-aws-retail-competency.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 12, 2026
- Type: Announcement

ClickHouse has achieved the AWS Retail Competency in the Advanced Data Insights category, a designation recognizing validated technical expertise and demonstrated customer success in retail real-time analytics. Retail workloads described here combine high-volume, high-cardinality, high-concurrency data with hard freshness requirements, including inventory, pricing, personalization, and peak seasonal demand. The post attributes the platform's fit to compressed columnar storage, parallel queries, continuous Kafka and Amazon S3 ingestion through ClickPipes, compute-compute separation, and AWS support for private networking, multi-region deployment, and Marketplace billing. Examples include Mercado Libre's 90-TB-per-day observability ingestion and trace lookups reduced from 15–20 minutes to about two seconds, while other customers report cost, speed, and analyst-efficiency gains.


### [Introducing Postgres to Postgres ClickPipes in ClickHouse Cloud](https://yomu.fyi/post/introducing-postgres-to-postgres-clickpipes-in-clickhouse-cloud.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 11, 2026
- Type: Announcement

Postgres-to-Postgres migrations between providers can require manual schema handling, data movement, CDC, and cutover coordination, with risks around database objects and long-running loads. Postgres to Postgres ClickPipes, now in public beta, provides a fully managed migration service in ClickHouse Cloud designed to reduce operational overhead and support minimal downtime. It automates schema dump and restore, including secure connections, role conflicts, version differences, transactional application, retries, and UI or notification-based error reporting, while PeerDB supplies parallel initial loads and CDC capabilities. Independent pull and push processes use retries and alerting for transient errors, and the roadmap includes features to assist with cutover into Postgres by ClickHouse.


### [Building ClickCannon - a tool for benchmarking ClickHouse](https://yomu.fyi/post/building-clickcannon-a-tool-for-benchmarking-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 11, 2026
- Type: Benchmark

ClickCannon was created to answer how much CPU, memory, and disk are needed for ClickStack observability workloads, while accounting for schema, ingestion rate, hardware, and concurrent queries. It replays about 2 TiB of real OpenTelemetry trace data, converted to ClickHouse Native format with ZSTD compression, rather than relying only on synthetic datasets. A custom multithreaded Go reader/inserter provides control over uncompressed throughput and exposes stream metrics such as compressed and uncompressed bytes, row counts, and block counts, while the framework simulates users and query workloads during ingestion. The project evolved into an open-source, generic ClickHouse benchmarking tool for custom insert and query workloads, with fine-grained control over throughput, concurrency, and user behavior; the post reports that it can push substantial throughput from a single machine.


### [Datadog and ClickHouse partner to bring full-fidelity data to modern observability](https://yomu.fyi/post/datadog-and-clickhouse-partner-to-bring-full-fidelity-data-to-modern-o.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 10, 2026
- Type: Announcement

ClickHouse and Datadog announce a partnership aimed at reducing the tradeoff between retaining complete telemetry and keeping it accessible through familiar observability workflows. New Preview capabilities let organizations route logs directly to ClickHouse through Datadog Observability Pipelines and search those logs from the Datadog Log Explorer. Pipelines can parse, enrich, filter, transform, and redact data before storage, while ClickHouse Cloud’s separation of storage and compute supports long-term retention, independently scaled query resources, and workload isolation. Federated log search queries data directly in ClickHouse, avoiding duplicate copies or re-ingestion while allowing high-volume logs to remain searchable and historical investigations to use full-fidelity telemetry. The partnership combines Datadog’s collection and investigation experience with ClickHouse’s storage and analytics foundation; the native integration and federated search are available in Preview.


### [Agents can now provision ClickHouse and Postgres on ClickHouse Cloud](https://yomu.fyi/post/agents-can-now-provision-clickhouse-and-postgres-on-clickhouse-cloud.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 10, 2026
- Type: Announcement

ClickHouse Cloud is now available through Stripe Projects, a Stripe CLI workflow that lets developers and AI agents provision real infrastructure from the command line. Running stripe projects init my-app and adding a service provisions ClickHouse or managed Postgres, returns working credentials to the environment through .env, and hands control to clickhousectl; clickhousectl greater than v0.3.0 is required. The integration supports ClickHouse for real-time analytics and managed Postgres, identified as a public beta, for transactional workloads; both services use the same CLI workflow, land in the same account, and share credentials, while Postgres uses local NVMe storage and is described as offering microsecond latency and up to 10x faster performance on I/O-heavy workloads. The feature is in developer preview, and agents can continue through clickhousectl for data ingestion with ClickPipes and service queries without manual credential handling or context switching.


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