---
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

### [Introducing CostBench: an open benchmark for data warehouse cost-performance](https://yomu.fyi/post/introducing-costbench-an-open-benchmark-for-data-warehouse-cost-perfor.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Tom Schreiber, Lionel Palacin
- Published: May 27, 2026

Traditional data warehouse benchmarks evaluate raw execution speed without accounting for vendor-specific compute pricing. CostBench addresses this operational limitation by introducing an open framework designed to measure performance-per-dollar across cloud platforms. The initial benchmark release assesses read-side query efficiency across ClickHouse Cloud, Snowflake, Databricks, BigQuery, and Amazon Redshift using 43 production-derived analytical queries executed against an anonymized dataset. To normalize comparisons across varied billing metrics like credits, DBUs, slot-seconds, compute units, and RPUs, CostBench maps runtime execution directly to actual compute billing models. In this evaluation, ClickHouse Cloud remains in the low-cost, high-performance quadrant as data scales, outperforming its nearest competitor by 23x in cost-performance.


### [Postgres managed by ClickHouse is now in beta](https://yomu.fyi/post/postgres-managed-by-clickhouse-is-now-in-beta.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Sai Srirampur
- Published: May 27, 2026

ClickHouse Cloud has launched a fully managed Postgres service in beta to combine transactional and analytical workloads within a single platform. Backed by local NVMe storage, the service provides up to ten times faster transactional performance alongside native change data capture into ClickHouse. Developers can query across both transactional and analytical layers using the pg\_clickhouse extension without managing custom data pipelines. The platform also offers managed migration workflows via ClickPipes, database branching, point-in-time recovery, high availability with up to two standbys, and support for over ninety PostgreSQL extensions. More than fifty virtual machine configurations are available, ranging from single vCPU instances to clusters with ninety-six vCPUs and sixty terabytes of NVMe storage.


### [Native random sampling in ClickHouse](https://yomu.fyi/post/native-random-sampling-in-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: May 22, 2026

ClickHouse native random sampling enables aggregate queries to execute against a representative fraction of data when querying full datasets is too slow. To maintain sampling accuracy, tables require a sample key derived from high-cardinality columns using hash functions like sipHash64 to ensure an even distribution. The sample key must be included in the primary key ORDER BY expression, preferably at the front to let ClickHouse quickly skip index granules. Queries can sample data deterministically by specifying a fractional ratio or a minimum row threshold, scaling aggregate counts using the virtual column \_sample\_factor. In benchmark tests on a 30-million-row dataset, sampling 10% of records reduced query latency by approximately 80% while retaining approximate metrics suitable for exploratory analysis.


### [Integrating the Rust Delta Kernel into ClickHouse](https://yomu.fyi/post/integrating-the-rust-delta-kernel-into-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Melvyn Peignon, Kseniia Sumarokova, Raúl Marín
- Published: May 22, 2026

ClickHouse initially built native protocol support to read Delta Lake tables, but maintaining pace with the format's evolving specification created high complexity and fragmented features. To resolve this, engineers integrated the Rust Delta Kernel to offload protocol handling while retaining ClickHouse's optimized Parquet data reading. The Delta Kernel parses JSON transaction logs, resolves snapshots, manages transactional write metadata, and exposes schema evolution mapping alongside Change Data Feed streams. ClickHouse also contributed upstream enhancements to the Rust kernel, adding dynamic runtime logging and asynchronous metadata processing via FFI handles. While ClickHouse can attach to existing Delta tables, the integration cannot yet initialize empty Delta Lake tables.


### [May 2026 newsletter](https://yomu.fyi/post/may-2026-newsletter.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: May 21, 2026

The May 2026 ClickHouse newsletter details enterprise observability migrations, ecosystem integrations, and core database enhancements. Digital banking platform Qonto migrated from Grafana Tempo to ClickHouse Cloud, compressing 231 TB of raw trace metadata to 376 GB while expanding query windows to two weeks and introducing an AI incident companion. Similarly, LINE MAN Wongnai consolidated multiple business clusters onto self-hosted ClickHouse, achieving 1.5 million rows per second peak ingest, 100% trace retention, and a 53% reduction in observability costs. Comparative log analytics benchmarks demonstrated that ClickHouse utilized 5x less disk space and achieved 4 to 6x faster cold query execution than Elasticsearch across 50 billion rows. Additional updates include the ClickHouse 26.4 release featuring improved SQL compatibility, new agent telemetry adapters in Mastra, and SQL-based alerting capabilities in ClickStack.


### [What's new in ClickStack - April 2026](https://yomu.fyi/post/what-s-new-in-clickstack-april-2026.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: The ClickStack Team
- Published: May 19, 2026

ClickStack's April release introduces several core enhancements across querying, alerting, and dashboard visualization capabilities. The platform added SQL-powered alerting, allowing teams to express complex statistical rules like rolling baselines and anomaly detection using native ClickHouse SQL and window functions without external pipelines. To improve search and filtering performance across common observability workloads, the default otel\_logs schema was redesigned with ClickHouse text indexes and adjusted ordering keys. Furthermore, heatmaps transitioned into a first-class chart type inside the shared visualization system for latency distribution tracking. The update also delivers persistent alert execution error reporting, expanded threshold comparison operators, and per-series number formatting across dashboards.


### [ClickHouse vs Prometheus for High Cardinality, Part 2: Cardinality in ClickHouse](https://yomu.fyi/post/clickhouse-vs-prometheus-for-high-cardinality-part-2-cardinality-in-cl.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Rory Crispin, Dale McDiarmid
- Published: May 15, 2026

Traditional time-series databases like Prometheus experience write amplification, memory overhead, and operational challenges when high-cardinality label combinations churn rapidly. ClickHouse alters these economics by abandoning dedicated series objects in favor of a wide events model, storing telemetry as rows in columnar tables with dynamic Map attributes and metric measurements. Dynamic attributes can be handled using sharded Map types alongside materialized columns and text indexes for frequently queried labels. This design trades ingestion overhead for query-time processing, relying on columnar compression, partition pruning, and parallel scans to compute aggregations on demand. ClickHouse avoids the ingest-side bottlenecks of high cardinality, though Prometheus remains preferable for moderate cardinality, native PromQL semantics, and single-series lookups.


### [ClickHouse vs Prometheus for High Cardinality, Part 1: Understanding the Problem](https://yomu.fyi/post/clickhouse-vs-prometheus-for-high-cardinality-part-1-understanding-the.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Rory Crispin, Dale McDiarmid
- Published: May 14, 2026

Observability systems frequently encounter high cardinality when metrics contain numerous unique label combinations across multidimensional cloud infrastructure. In Prometheus, every unique label combination instantiates an independent time series that incurs metadata overhead, requires in-memory memSeries registration, and continually expands inverted index posting lists. The ingestion engine stores samples in two-hour Head blocks using compressed 120-sample chunks with XOR-based encodings, but rapid series creation and elevated scrape frequencies multiply chunk allocations and memory consumption. When executing read operations, broad aggregations and regular expressions require intersecting expansive posting lists and decoding entire chunks sequentially because the engine cannot push value predicates directly into compressed storage blocks. Consequently, managing high-cardinality workloads in series-oriented architectures forces operational trade-offs between system stability and granular visibility into ephemeral components.


### [Postgres FDW: Pushdown is a negotiation](https://yomu.fyi/post/postgres-fdw-pushdown-is-a-negotiation.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Kaushik Iska, David Wheeler, Philip Dubé
- Published: May 14, 2026

Postgres Foreign Data Wrapper extensions allow PostgreSQL to query external datastores like ClickHouse by delegating execution to remote engines. Engineering pg\_clickhouse centers on pushdown decisions that determine whether SQL expressions run remotely or stream raw data back across the wire. Because pushdown depends on planner callbacks, deparser translations, and ClickHouse semantic compatibility, a single untranslated clause can block upper-level query pushdown entirely. Resolving pushdown barriers enables complex analytic queries to return hundreds of rows in milliseconds rather than pulling tens of millions of rows for local processing. Ultimately, pushdown development requires an iterative negotiation across differing SQL grammars, occasionally requiring the revocation of translations that fail to maintain strict equivalence.


### [ClickStack SQL Charting and Alerting](https://yomu.fyi/post/clickstack-sql-charting-and-alerting.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Drew Davis, Dale McDiarmid
- Published: May 13, 2026

ClickStack introduced SQL-based visualizations and SQL-based alerting to allow users to build charts and alerts using arbitrary ClickHouse SQL queries. Traditional query builders trade analytical flexibility for simplicity, preventing teams from easily expressing multi-stage computations like rolling baselines, anomaly detection, or dynamic SLO calculations. To maintain interactivity within dashboards, the system supports ClickHouse query parameters and macros that inject active time ranges, interval sizes, dashboard filters, and source tables. Furthermore, SQL-based alerting evaluates the numeric columns output by these queries, such as returning binary flags when values exceed statistical boundaries. By shifting computational logic directly into SQL, teams can implement complex operational checks without relying on static external thresholds.


### [Powering self-driving vehicle analytics at Avride with ClickHouse Cloud](https://yomu.fyi/post/powering-self-driving-vehicle-analytics-at-avride-with-clickhouse-clou.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: May 11, 2026

Avride’s expanding autonomous passenger vehicle and delivery robot fleet generated thousands of data points per minute, overwhelming its legacy Apache Iceberg and Parquet metadata indexing architecture. Iceberg’s optimistic concurrency model struggled with parallel writes, pushing ingestion latencies to hours or days, inflating query times to 10–20 seconds, and requiring raw data duplication across storage locations. To resolve these bottlenecks, Avride built a custom indexing and metrics warehouse powered by ClickHouse Cloud on AWS backed by Amazon S3 object storage. The new architecture dropped ingestion delays to seconds, reduced worst-case lookup latencies to under one second, eliminated the need for bespoke caching layers, and removed payload duplication. Beyond operational analytics, the unified ClickHouse platform now supports internal ride-replay debugging tools, simulation workflows, and high-cardinality vehicle C++ performance trace profiling.


### [ClickHouse Release 26.4](https://yomu.fyi/post/clickhouse-release-26-4.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: May 8, 2026

ClickHouse Release 26.4 introduces 39 new features, 45 performance optimizations, and 238 bug fixes, focusing on broader SQL compatibility and indexing improvements. The update expands standard SQL support by allowing VALUES clauses as table expressions with column aliases, PostgreSQL-style EXTRACT units, NATURAL JOIN, and SET TIME ZONE commands. For text search, LIKE and ILIKE pattern queries using splitByNonAlpha tokenizers scan inverted index dictionaries instead of executing full-table scans when the dedicated evaluation setting is active. The release also provides the JSONAllValues function to extract all leaf values from a JSON column into a string array, enabling text skip indexes that significantly reduce scanned data and improve query latency.


### [Introducing Postgres Query Insights in ClickHouse Cloud](https://yomu.fyi/post/introducing-postgres-query-insights-in-clickhouse-cloud.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Amog Iska
- Published: May 7, 2026

ClickHouse Cloud introduced Query Insights in preview for Managed Postgres to help teams diagnose slow database queries. Powered by the open-source extension pg\_stat\_ch, the tool streams per-statement telemetry directly into ClickHouse to enable fast aggregations across billions of execution rows. Postgres normalizes query literals into placeholders during the parse-analyze phase before telemetry leaves the database, preventing sensitive data exposure. Telemetry emission maintains around three percent producer overhead by utilizing a non-blocking try-lock on a shared-memory ring buffer. Users can examine high-level database health metrics, rank query patterns by runtime or latency percentiles, and inspect execution flyouts detailing disk spills, cache hits, and parallel worker behavior.


### [Agentic analytics starts with query-ready data: the write-side cost of Snowflake vs. ClickHouse](https://yomu.fyi/post/agentic-analytics-starts-with-query-ready-data-the-write-side-cost-of.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Tom Schreiber, Lionel Palacin
- Published: May 6, 2026

Agentic analytics workflows introduce bursts of concurrent, low-latency analytical queries over continuously ingested data. Analytical query efficiency relies on columnar storage and chunk-level pruning, which require contiguous, sorted data to avoid full-table scans. Rather than evaluating warehouses solely at query runtime, this benchmark compares the write-side cost of maintaining query-ready data in ClickHouse and Snowflake during continuous ingestion at approximately one million rows per second. Snowflake relies on post-write clustering to organize data, whereas ClickHouse constructs sorted layouts on the write path and refines them via background merges. Across 100B, 200B, and 300B row checkpoints on the ClickBench dataset, ClickHouse achieved a query-ready layout at 22 times lower cost and delivered 28 times better write-side cost-performance.


### [Stop guessing in production: Full fidelity tracing at scale with ClickHouse and Odigos](https://yomu.fyi/post/stop-guessing-in-production-full-fidelity-tracing-at-scale-with-clickh.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Will Searle
- Published: May 5, 2026

Distributed tracing frequently suffers from visibility gaps, broken context across asynchronous boundaries like Kafka, and high operational overhead from manually configuring OpenTelemetry SDKs and collectors. To resolve these issues, Odigos provides zero-code instrumentation via eBPF probes that dynamically attach to running workloads without modifying application code. An intelligent control plane within Odigos dynamically adjusts trace sampling and attribute enrichment based on real-time latency and error rates. The resulting telemetry exports over OTLP into ClickStack, an all-in-one backend featuring an OpenTelemetry Collector, ClickHouse storage, and the HyperDX query interface. This unified pipeline preserves end-to-end trace continuity, captures deep application and database context, and enables fast SQL-based trace analysis.


### [Gala supercharges analytics performance with ClickHouse on AWS](https://yomu.fyi/post/gala-supercharges-analytics-performance-with-clickhouse-on-aws.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: May 4, 2026

Blockchain gaming platform Gala struggled to ingest and query expanding volumes of telemetry and blockchain data on its Databricks-based data infrastructure, resulting in multi-minute query times and heavy engineering maintenance overhead. To solve these bottlenecks, the company migrated its data platform to ClickHouse on AWS, ingesting data through Kafka, Airbyte, Amazon S3, and Fivetran. Gala leveraged ClickHouse's indexing capabilities alongside saved queries and API endpoints to power company-wide Metabase business intelligence dashboards for non-technical teams. Following the migration completed in December 2024, data capacity expanded from 3 TB to 9 TB while query times dropped from minutes to sub-second on previously unoptimized tables. The switch also lowered initial infrastructure costs by 30 percent, with plans underway to implement ClickHouse ClickPipes for improved data processing pipelines.


### [Goodbye limitations, hello data: How Qonto is rethinking observability with ClickHouse Cloud](https://yomu.fyi/post/goodbye-limitations-hello-data-how-qonto-is-rethinking-observability-w.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse Team
- Published: May 1, 2026

Qonto struggled with slow queries, system instability, and aggressive data sampling when handling distributed traces in Grafana Tempo. To enable wide-event querying without strict cardinality limits, the team migrated their telemetry storage to ClickHouse Cloud. Telemetry from applications, Kubernetes, and GitHub routes through OpenTelemetry collectors over AWS PrivateLink into ClickHouse, while Grafana remains the primary dashboard interface. By compressing 231 TB of uncompressed trace attributes down to 376 GB, Qonto achieved substantial storage savings and integrated an AI incident companion using the ClickHouse Model Context Protocol. The new architecture eliminated cardinality policing, allowed product and support teams to self-serve incident investigations, and enabled real-time streaming pre-aggregations using Apache Flink.


### [Comparing ClickHouse versions with clickhousectl](https://yomu.fyi/post/comparing-clickhouse-versions-with-clickhousectl.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: Apr 29, 2026

Evaluating query performance differences across ClickHouse versions previously required managing Docker containers or manually downloading historical binaries from GitHub releases. The clickhousectl command-line interface simplifies this process by downloading specific ClickHouse binaries, running multiple background server instances concurrently with automatic port assignment, and executing queries across local environments. Users can copy table schemas between local instances by piping raw output from SHOW CREATE TABLE queries and replicate datasets using ClickHouse's remote table function. In a benchmark comparing ClickHouse 25.12 and 26.3 on an S3-backed Parquet dataset, version 26.3 introduced a Parquet metadata cache that reduced repeat query times from roughly 9 seconds to 1–2 seconds. System tables like system.server\_settings and system.metrics enable operators to inspect cache configurations, eviction policies, and memory consumption directly.


### [ClickHouse at Google Cloud Next '26](https://yomu.fyi/post/clickhouse-at-google-cloud-next-26.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 27, 2026

ClickHouse announced several product updates and integrations designed to deepen native compatibility with Google Cloud. Migration of ClickHouse Cloud to Google Axion ARM-based processors demonstrated a 30 to 55 percent query speed increase and reduced data load times from 64 to 26 seconds on ClickBench benchmarks. An integration with Google Lakehouse Runtime Catalog enables querying Iceberg tables on Google Cloud Storage via the DataLakeCatalog engine without data movement. Furthermore, Bring Your Own Cloud reached general availability on Google Cloud Marketplace, and ClickPipes added an unordered mode using Pub/Sub notifications for GCS ingestion. Google Antigravity integrated ClickHouse through an MCP server to translate natural language into SQL and generate interactive React charts.


### [What's New in pg\_clickhouse - JSONB Support, SQL value functions, Streaming, and more](https://yomu.fyi/post/what-s-new-in-pg-clickhouse-jsonb-support-sql-value-functions-streamin.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: David Wheeler
- Published: Apr 24, 2026

Recent releases of pg\_clickhouse, a Postgres foreign data wrapper for ClickHouse, introduce query pushdown enhancements and streaming result handling. Version 0.1.10 adds pushdown for JSONB accessor operators and extraction functions by mapping them to ClickHouse sub-column syntax and toJSONString calls outside SELECT clauses. In version 0.2.0, date and time functions such as CURRENT\_DATE, CURRENT\_TIMESTAMP, and clock\_timestamp() push down to ClickHouse while honoring the active Postgres session time zone and precision settings. To address memory pressure and out-of-memory risks from large foreign queries, the extension adds query result streaming to the HTTP driver with a default batch buffer of approximately 50MB. Container benchmarking against the NYC taxi dataset showed streaming reduced peak memory consumption from over 600 MiB to under 86 MiB.


[Older posts](https://yomu.fyi/company/clickhouse/page/2.md)