---
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 26.3](https://yomu.fyi/post/clickhouse-release-26-3.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 7, 2026

ClickHouse 26.3 delivers twenty-seven new features, forty performance optimizations, and over two hundred bug fixes. Common table expressions can now be materialized into temporary tables via the MATERIALIZED clause when enabled, evaluating subqueries once to reduce query execution times and peak memory usage. The query optimizer expands join reordering beyond inner and left or right joins to include anti, semi, and full joins when table statistics are available. In addition, user-defined functions can run compiled WebAssembly code inside sandboxed Wasmtime runtimes. Storage for the Map data type also introduces hash-based bucketing during background merges, accelerating single-key lookups by two to forty-nine times without slowing initial inserts.


### [How Respan is scaling LLM observability with ClickHouse Cloud](https://yomu.fyi/post/how-respan-is-scaling-llm-observability-with-clickhouse-cloud.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 6, 2026

Respan scaled its LLM gateway and observability platform from hundreds of daily requests to 50 million daily events, causing its original Django and Postgres architecture to stall under transaction contention at 50 to 100 requests per second. To overcome Postgres write-ahead logging bottlenecks, the team migrated the ingestion and analytics pipelines to ClickHouse Cloud. Engineers minimized row sizes by truncating large prompt text and writing structured metrics to MergeTree tables, then built incremental minute- and hour-level materialized views alongside scheduled refreshable views for user metadata. Distributed tracing spans are ingested asynchronously with analytical parent-child resolution, utilizing functions like argMax rather than the FINAL modifier to prevent costly table merges. This architectural shift allows Respan to achieve sub-second search across tens of millions of records and support customer dashboards with predictable query performance.


### [PostgresBench: A Reproducible Benchmark for Postgres Services](https://yomu.fyi/post/postgresbench-a-reproducible-benchmark-for-postgres-services.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Lionel Palacin
- Published: Apr 2, 2026

PostgresBench is a public, reproducible benchmarking suite created to compare transactional performance across managed PostgreSQL providers. The benchmark uses the standard pgbench tool with a TPC-B-like write-intensive workload executing across 256 clients and 16 threads for ten-minute durations. Evaluators tested five services—including Postgres managed by ClickHouse, AWS Aurora, AWS RDS, Neon, and Crunchy Bridge—under 100 GB and 500 GB scale factors using default configurations in the us-east-2 region. Across tested configurations, Postgres managed by ClickHouse delivered higher transactions per second and lower P99 latencies due to physically co-located NVMe primary storage. These results demonstrate that remote network storage layers, such as EBS volumes, introduce significant latency overhead during sustained write and fsync operations.


### [Rill and ClickHouse: real-time operational BI for a metered world](https://yomu.fyi/post/rill-and-clickhouse-real-time-operational-bi-for-a-metered-world.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 1, 2026

Granular operations across cloud computing, payment platforms, and artificial intelligence workloads produce high-volume event streams that traditional business intelligence tools struggle to unify and aggregate. Operational BI platform Rill addresses this challenge by combining declarative data loading through the dlt framework with ClickHouse's high-performance analytical aggregation engine. Data transformations, SQL-based measure expressions, and dashboard configurations are defined entirely as code using SQL and YAML. This declarative, metrics-first architecture allows developers to validate analytics locally, track changes in Git, and eliminate intermediate caching layers when querying live data. By querying ClickHouse directly and linking AI-generated insights back to underlying queries, the system provides traceable, real-time operational analytics for enterprise environments.


### [Announcing Role Based Access Control in ClickStack](https://yomu.fyi/post/announcing-role-based-access-control-in-clickstack.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mike Shi
- Published: Apr 1, 2026

Managed ClickStack has introduced Role-Based Access Control (RBAC) to replace a legacy model where all users shared identical instance-wide permissions or required duplicate HyperDX instances for isolation. Integrated with ClickHouse Cloud for identity management, the new system allows administrators to assign users to roles governed by three access tiers: no access, read, and manage. Permissions apply across platform resources including dashboards, saved searches, sources, alerts, webhooks, and notebooks, with support for fine-grained rules matching names, IDs, or tags. While table-level source restrictions work through ClickStack, row-level access currently depends on separate ClickHouse SQL console roles, which the team intends to unify directly inside ClickStack in future releases.


### [Agentic coding at ClickHouse](https://yomu.fyi/post/agentic-coding-at-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Alexey Milovidov
- Published: Apr 1, 2026

Industry perspectives on AI coding agents remain divided between dismissal and excessive automation claims. ClickHouse integrated agentic coding across internal workflows, progressing from simple boilerplate scripts to active backend C++ development. Engineers primarily use command-line agents such as Claude Code with Claude Opus 4.5 and Codex CLI alongside observability platforms like Langfuse and Librechat. While agents effectively investigate continuous integration logs, generate internal dashboards, and assist with small C++ tasks, they lack architectural competence and require rigorous code review. ClickHouse treats agents as pair programmers for contained tasks, retaining human ownership over product strategy, attention to detail, and core code quality.


### [We taught ClickStack to read your logs like a detective novel](https://yomu.fyi/post/we-taught-clickstack-to-read-your-logs-like-a-detective-novel.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: The ClickStack Team
- Published: Apr 1, 2026

HyperDX introduced AI Summarize, a feature designed to translate raw logs, traces, and log patterns into narrative summaries directly within its observability interface. Instead of relying on backend large language models, the tool runs entirely client-side using OpenTelemetry-aware data extraction combined with hand-written phrase pools. The generation logic inspects Kubernetes and OpenTelemetry attributes—such as service names, endpoints, exception details, and query durations—to tailor tone across detective noir, Shakespearean drama, and nature documentary themes. Because processing occurs inside the browser tab without external API calls, the system avoids third-party latency, token expenses, model hallucinations, and data privacy risks. Users can access summaries across logs, spans, and pattern drawers or re-enable the interface toggle via URL parameter through April 2026.


### [ClickHouse BYOC on Google Cloud  now Generally Available](https://yomu.fyi/post/clickhouse-byoc-on-google-cloud-now-generally-available.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Aashish Kohli, Yiyang Shao
- Published: Mar 31, 2026

ClickHouse Bring Your Own Cloud is now generally available on Google Cloud across all public ClickHouse Cloud Google regions without requiring a waitlist. The architecture separates the ClickHouse-managed control plane from the customer data plane, which runs entirely within the user Google Cloud project on Google Kubernetes Engine and Google Cloud Storage. An outbound-only, encrypted Tailscale zero-trust tunnel restricts control plane communication strictly to orchestration traffic, ensuring query data never leaves the customer environment. Deployment involves a three-step Terraform setup that establishes least-privilege IAM roles, configures VPC networking, and automates cluster provisioning. ClickHouse manages database and infrastructure maintenance through ArgoCD, employing a make-before-break strategy to add updated replicas before decommissioning older nodes across customizable release channels.


### [How Padlet uses ClickHouse Cloud to power real-time classroom analytics](https://yomu.fyi/post/how-padlet-uses-clickhouse-cloud-to-power-real-time-classroom-analytic.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Mar 30, 2026

Padlet needed a real-time analytics system to provide teachers with immediate insights into student engagement across 40 million monthly unique users. To avoid the operational overhead of managing database clusters with a small engineering team, the company adopted ClickHouse Cloud and ClickPipes for managed data ingestion. Active user sessions emit 30-second heartbeat events that stream alongside page views into ClickHouse. Materialized views pre-aggregate engagement duration and view counts by date, while native HyperLogLog sketches process unique visitor calculations efficiently across arbitrary time ranges. In production, this architecture ingested roughly eight billion events in a single month and delivered analytical query results with a 45-millisecond median latency.


### [How we’re building a data platform for a new user: agents](https://yomu.fyi/post/how-we-re-building-a-data-platform-for-a-new-user-agents.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Al Brown
- Published: Mar 27, 2026

AI agents are emerging as autonomous database users that generate SQL, execute queries, and iterate in seconds. Because traditional analytics workflows built for human analysts cannot keep up with these rapid loops, ClickHouse is adapting its platform for agentic workloads. The proposed Agentic Data Stack combines LibreChat for conversational interfaces, ClickHouse with Model Context Protocol servers for the data layer, and Langfuse for observability. Supporting developer tooling includes chDB 4 with a Pandas-like API, IDE integrations, and ClickHouse Agent Skills to enforce database design best practices. Platforms like Anthropic and Character.AI already leverage ClickHouse's high-concurrency aggregation and real-time ingestion capabilities to handle massive data scales required by modern AI applications.


### [Top 10 best practices tips for ClickHouse](https://yomu.fyi/post/top-10-best-practices-tips-for-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Yonatan Dolan
- Published: Mar 26, 2026

ClickHouse delivers rapid analytical queries across massive datasets, but suboptimal schemas and queries can significantly reduce performance and storage efficiency. Aligning table definitions with ClickHouse storage and execution mechanics resolves these bottlenecks. Key practices include placing low-cardinality columns first in the ORDER BY clause to enable granule pruning, and selecting optimal data types while avoiding Nullable wrappers to cut memory overhead. Developers should treat partitioning as a data management tool for TTL drops or merge engines rather than a generic speed booster, preventing fragmented parts from slowing query execution. Recent improvements like the Analyzer query planner have also made joins viable, though dictionaries remain faster for static lookups.


### [More Visibility, Less Guesswork: ClickHouse Cloud's New Monitoring Capabilities](https://yomu.fyi/post/more-visibility-less-guesswork-clickhouse-cloud-s-new-monitoring-capab.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mihir Gokhale
- Published: Mar 25, 2026

ClickHouse Cloud released new monitoring capabilities and dashboards to give platform administrators deeper visibility into deployment health and server behavior. A newly introduced Overview page consolidates critical signals into a unified view for rapid health checks. Additionally, an updated Infrastructure page tracks service scaling over time, presenting CPU and memory utilization with new aggregation options alongside modals explaining automatic scaling decisions. The platform now sends automated email and Slack notifications when deployments encounter risks such as excessive parts, failed mutations, or query concurrency saturation. These additions allow administrators to proactively identify operational bottlenecks and configure scaling limits without spending excessive time searching for telemetry data.


### [ClickHouse is data lake ready](https://yomu.fyi/post/clickhouse-is-data-lake-ready.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Karolina Ruiz Rogelj, Melvyn Peignon
- Published: Mar 25, 2026

ClickHouse announced expanded data lake capabilities, enabling direct querying, accelerated ingestion into MergeTree, and writes across Apache Iceberg and Delta Lake tables. Open lake architectures provide storage portability across cloud providers and catalogs, but standard query engines often struggle with latency for real-time workloads. Over two years of development, ClickHouse engineered a native Parquet reader with page-level parallelism, integrated the Delta Rust Kernel, and introduced support for schema evolution, time travel, and DML operations. The platform functions across AWS Glue, Unity Catalog, Polaris, and Iceberg REST Catalog on storage systems including Amazon S3, Google Cloud Storage, and Azure Blob Storage. These enhancements allow organizations to execute federated, high-concurrency SQL queries across multiple catalogs or write processed analytical results back to their lakes without vendor lock-in.


### [Intelligent security at ClickHouse speed: How Cogent Security built an AI-native vulnerability management platform](https://yomu.fyi/post/intelligent-security-at-clickhouse-speed-how-cogent-security-built-an.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Mar 24, 2026

Enterprise security defenders face shrinking exploitation windows alongside massive volumes of vulnerability findings across disconnected tools. To overcome query bottlenecks from traditional relational databases, Cogent Security designed a lakehouse serving architecture using Apache Iceberg and per-customer ClickHouse instances. The architecture relies on query-driven denormalized tables, custom compression codecs, and pre-computed projections to serve hundreds of millions of rows with sub-second latency. On top of this data layer, Cogent implemented an agentic text-to-SQL system that iteratively queries live data, references an in-house Ontology Service for semantic context, and applies majority-voted evaluations. This combination improved text-to-SQL accuracy from 40% to 94% while providing sub-three-second response times across 98.7% of queries at 100 million rows.


### [Smarter Auto-Scaling for ClickHouse: The Two-Window Approach](https://yomu.fyi/post/smarter-auto-scaling-for-clickhouse-the-two-window-approach.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Ashwath Singh, Manas Alekar
- Published: Mar 24, 2026

ClickHouse Cloud required a vertical auto-scaling mechanism that preserves rapid scale-ups while avoiding prolonged over-provisioning after traffic drops. Operating on a single 30-hour lookback window forced scale-downs to take up to 30 hours, whereas an earlier fixed-factor CPU algorithm caused severe oscillations. The engineering team deployed a dual-window recommender pairing a 3-hour window for responsive scale-downs with a 30-hour window for single-step scale-ups. Alongside a trend-based hunting check to resolve conflicting window recommendations, the team introduced target-tracking CPU scaling using geometric mean watermarks and 10-minute median smoothing. This dual-window architecture reduced scale-down latency to 3 hours, prevented scaling oscillations, and significantly reduced infrastructure costs for variable database workloads.


### [Building high-performance full-text search for object storage](https://yomu.fyi/post/building-high-performance-full-text-search-for-object-storage.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Elmi Ahmadov, Jimmy Aguilar, and George Larionov
- Published: Mar 24, 2026

ClickHouse redesigned its native full-text index to address latency bottlenecks caused by random read patterns on remote object storage. Instead of using a Finite State Transducer dictionary, the new architecture organizes indexed tokens into fixed-size sorted blocks compressed with front-coding. Each data part separates index data across a small in-memory sparse index file, a sequential dictionary file, and a posting list file. This layout allows the query engine to pinpoint token locations with minimal random I/O and resolve multiple full-text functions directly from index files. Consequently, ClickHouse Cloud maintains predictable search latency across shared object storage while matching local disk performance and scaling through parallel query execution.


### [Structured Logging in .NET with Serilog and ClickHouse](https://yomu.fyi/post/structured-logging-in-net-with-serilog-and-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Alex Soffronow Pagonidis
- Published: Mar 23, 2026

Traditional logging solutions like Seq, ELK, and cloud platforms often face scaling bottlenecks, including slower queries, higher operational costs, and complex pipeline management. To address this, an ASP.NET service can write structured logs directly to ClickHouse using Serilog and the Serilog.Sinks.ClickHouse library. The sink transmits data over HTTP and provides a fluent C# builder to configure table schemas, typed JSON columns, full-text indexes, and MergeTree engine parameters. ClickHouse stores log data in a columnar format that delivers 10 to 20 times compression while enabling direct SQL queries, JSON property filtering, and real-time aggregations through materialized views. Furthermore, the sink includes an in-memory buffer to handle transient network drops with configurable batching, retry mechanisms, and callback hooks for failure alerting.


### [Querying DateTimes in ClickHouse](https://yomu.fyi/post/querying-datetimes-in-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: Mar 20, 2026

ClickHouse provides specialized functions to filter, bucket, and compute durations on native DateTime columns without requiring external processing tools. Using the New York City taxi dataset imported into a MergeTree table, queries bucket pickup timestamps into hour and 15-minute intervals using toStartOfHour and toStartOfFifteenMinutes while casting values to the Time type to group across dates. The dateDiff function calculates journey durations in minutes, which enables trip speed calculations across time windows and powers inline ASCII visualizations generated by the bar function. By combining toDayOfWeek with conditional aggregations like countIf and the lag window function, queries uncover distinct morning surge trajectories between weekdays and weekends. The analysis demonstrates that weekday trip volumes peak around 8:45 AM alongside dropping speeds, whereas weekend volumes increase steadily across the morning.


### [March 2026 newsletter](https://yomu.fyi/post/march-2026-newsletter.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: Mar 19, 2026

The March 2026 ClickHouse newsletter details major platform updates, community deployments, and architectural practices for high-throughput analytics. Recent product announcements include ClickHouse 26.2 featuring time-based block flushing for streaming feeds, general availability of native inverted indexes for full-text search, and the release of chDB 4 with a Pandas-compatible DataStore API. Community implementations show Cloudflare managing 1.61 quadrillion daily events, while Trigger.dev isolated multi-tenant SQL execution using a domain-specific query language that compiles safe ClickHouse queries. In data optimization techniques, Hookdeck accelerated webhook payload search from over 30 seconds to under 400 milliseconds by partitioning payload hashes into typed bucket columns and scanning iterative time windows. Additional operational guidance emphasizes setting the ttl\_only\_drop\_parts parameter and aligning table partitioning with time units so background merges drop entire data parts cleanly without triggering mutation rewrites.


### [How ClickStack makes ClickHouse faster for observability](https://yomu.fyi/post/how-clickstack-makes-clickhouse-faster-for-observability.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mike Shi
- Published: Mar 18, 2026

ClickHouse delivers high performance for telemetry data, but unoptimized queries on high-cardinality observability workloads can bypass pruning, inflate intermediate state, and exhaust cluster resources. ClickStack addresses this challenge by embedding engine-specific optimization best practices directly into how queries are generated, structured, and executed. For search workflows, the platform executes progressive queries across bounded time windows using optimize\_read\_in\_order to return initial rows rapidly without scanning entire historical ranges. ClickStack also segments large chart aggregations into parallel bucket-aligned queries, automatically redirects map attribute filters to pre-materialized columns, and extends lazy materialization thresholds to minimize runtime input-output overhead. These query formulation strategies are being packaged into specialized analytical endpoints to power notebooks and external tools without requiring manual SQL tuning or exposing raw database interfaces.


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