---
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

### [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
- Type: Problem & solution

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
- Type: Problem & solution

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
- Type: Problem & solution

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
- Type: Tutorial

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
- Type: Announcement

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
- Type: Announcement

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.


### [Do you still need Elasticsearch for log analytics? ClickHouse says no.](https://yomu.fyi/post/do-you-still-need-elasticsearch-for-log-analytics-clickhouse-says-no.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Tom Schreiber, Lionel Palacin
- Published: Apr 23, 2026
- Type: Benchmark

ClickHouse introduced a redesigned full-text search powered by inverted indexes that feeds matching documents directly into its vectorized analytical engine for observability and log analytics workloads. To evaluate this capability, the open-source versions of ClickHouse and Elasticsearch were benchmarked on single-node AWS EC2 instances across synthetic OpenTelemetry log datasets scaling up to 50 billion rows. The benchmark evaluated nine representative query patterns combining text search with filtering, grouping, counting, and time-series aggregations under both cold and hot cache conditions. ClickHouse achieved a roughly fivefold smaller storage footprint on disk while running cold queries four to six times faster and hot queries approximately 1.7 to 2.6 times faster than Elasticsearch. Furthermore, ClickHouse ingested the 50-billion-row dataset in under four hours out of the box, whereas the tuned Elasticsearch setup required approximately five days.


### [ClickHouse integrates with Lakehouse Runtime Catalog](https://yomu.fyi/post/clickhouse-integrates-with-lakehouse-runtime-catalog.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Melvyn Peignon
- Published: Apr 22, 2026
- Type: Announcement

ClickHouse 26.2 introduced a beta integration with Google's Lakehouse Runtime Catalog, enabling direct queries on Google Cloud Lakehouse Apache Iceberg tables without data movement or metadata syncing. The system communicates with Google Cloud Storage through the Iceberg REST Catalog endpoint at BigLake using Google Application Default Credentials for OAuth authentication. Users create a database using the DataLakeCatalog engine configured either with a local credentials JSON file or with explicit inline OAuth parameters. Once connected, analysts can query external Iceberg tables directly or ingest datasets into native MergeTree tables for repeated, low-latency workloads. Upcoming enhancements will add write capabilities for Iceberg tables and dedicated connection management tools inside the ClickHouse Cloud interface.


### [ClickHouse Cloud on Google Cloud Now Powered by Google Axion Processors: 30–55% Faster Queries, ~15% Fewer Compute Credits](https://yomu.fyi/post/clickhouse-cloud-on-google-cloud-now-powered-by-google-axion-processor.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 22, 2026
- Type: Announcement

ClickHouse Cloud transitioned its Google Cloud infrastructure from x86 N2D instances to Arm-based Google Axion C4A instances to resolve CPU throttling and performance ceilings under peak query loads. The migration leverages Axion's dedicated physical cores per vCPU, higher memory bandwidth, local SSDs, and improved thermal and clock frequency stability. In automated ClickBench testing across 100 million rows, every query in the 43-query benchmark improved by 30% to 55%, while data load times decreased by approximately half. A production analysis of 38 migrated customer services demonstrated an average 15% reduction in compute credits consumed per query across diverse production workloads. ClickHouse Cloud executed this rollout using automated validation checks, scheduled maintenance windows, and capacity coordination with Google Cloud.


### [Using the ClickHouse MCP server with Google Antigravity](https://yomu.fyi/post/using-the-clickhouse-mcp-server-with-google-antigravity.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Dustin Healy
- Published: Apr 22, 2026
- Type: Tutorial

Google Antigravity integrates with the ClickHouse Model Context Protocol server to provide agent-driven analytics within the developer environment. Users install the server from Antigravity's MCP Store panel and authenticate via OAuth, granting the agent read-only access to organizations, services, databases, and tables according to account permissions. When prompted with natural language, the agent uses tools like get\_services\_list and list\_tables to inspect schemas, translating user requests into functional ClickHouse SQL executed through the run\_select\_query tool. Antigravity can also generate interactive React charts as Artifacts, test UI interactions with screen recordings and screenshots in an integrated browser, and refine queries based on visual area comments. This setup allows engineers and analysts to explore datasets interactively without switching between external database clients and ticketing workflows.


### [Index sharding in ClickHouse Cloud: Petabyte-scale data needs petabyte-scale indexing](https://yomu.fyi/post/index-sharding-in-clickhouse-cloud-petabyte-scale-data-needs-petabyte.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: James Cunningham
- Published: Apr 21, 2026
- Type: Problem & solution

In large-scale ClickHouse Cloud deployments, replicas traditionally load entire primary and secondary indexes from object storage into working memory, creating massive redundant memory overhead as fleets expand. Index sharding resolves this bottleneck by distributing the index analysis phase across available replicas using consistent hashing over data parts. Instead of every node loading full index structures, each replica loads and analyzes only its assigned fraction of primary keys, bloom filters, text indexes, or vector indexes. The query initiator merges the resulting granule match ranges, allowing parallel replicas to stream data without any single machine retaining the whole index. This approach freed working memory for query execution and accelerated index analysis by up to 7.7x in benchmark tests on a 50 billion row table.


### [ClickHouse expands strategic collaboration with Google Cloud, bringing deeper integration, cloud flexibility, and next-generation performance](https://yomu.fyi/post/clickhouse-expands-strategic-collaboration-with-google-cloud-bringing.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Tanya Bragin
- Published: Apr 21, 2026
- Type: Announcement

ClickHouse announced an expanded strategic collaboration with Google Cloud introducing four core architectural milestones. First, native integration with Google Cloud Lakehouse allows direct sub-second queries across data lakes and ClickHouse deployments without ETL pipelines or data duplication. Second, the Bring Your Own Cloud deployment model lets organizations run managed ClickHouse Cloud inside their own Google Cloud Virtual Private Cloud to retain data sovereignty and network controls. Third, ClickHouse Cloud is migrating automatically to Google's custom Arm-based Axion processors, boosting query throughput and cost efficiency for analytical workloads without requiring application changes. Finally, a new ClickHouse MCP server integration connects ClickHouse with Google Antigravity to enhance developer and analyst workflows.


### [Terraform & OpenAPI for ClickPipes is now Generally Available](https://yomu.fyi/post/terraform-openapi-for-clickpipes-is-now-generally-available.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Marta Paes
- Published: Apr 20, 2026
- Type: Announcement

ClickHouse Cloud announced the general availability of ClickPipes resources in Terraform and OpenAPI, expanding infrastructure-as-code support across its managed data ingestion platform. The release resolves previous beta gaps by adding full connector coverage, including Change Data Capture for Postgres, MySQL, and MongoDB, alongside support for BigQuery, Azure Blob Storage, and reverse private endpoints. Operators can provision new data ingestion pipelines, pause or resume sync operations, and scale resources programmatically through OpenAPI endpoints or the ClickHouse Terraform provider starting at version 3.14.0. Existing ClickPipes can also be imported into Terraform state using native import blocks and automated configuration generation. Although SSH tunneling remains under active development, nearly all UI capabilities now map cleanly to programmatic interfaces.


### [otel.fyi - OTel Collector docs made simple](https://yomu.fyi/post/otel-fyi-otel-collector-docs-made-simple.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: The ClickStack Team
- Published: Apr 17, 2026
- Type: Problem & solution

OpenTelemetry Collector documentation is often fragmented across separate contrib repositories as individual README files, making component configuration difficult and time-consuming to navigate. To address this friction, the team created otel.fyi as a dedicated platform to aggregate, simplify, and structure documentation from across the ecosystem. The implementation parses contrib repositories to pull configuration examples and normalize metadata across receivers, processors, exporters, and extensions into a unified format. By surfacing structured component information upfront, the site allows practitioners to assemble working configurations rapidly without digging through multiple GitHub repositories. Originally developed for internal operational workflows, the platform has been released publicly to assist the broader community, with future plans to introduce an AI assistant.


### [“A generational leap”: How Trio unified payment analytics and cut storage by 88% with ClickHouse Cloud](https://yomu.fyi/post/a-generational-leap-how-trio-unified-payment-analytics-and-cut-storage.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 17, 2026
- Type: Problem & solution

Brazilian payment processor Trio faced severe data challenges after scaling and acquiring PayBrokers, processing over 243 million payments in early 2025 across disparate legacy databases. Because financial reconciliation and compliance demand absolute precision, the engineering team unified their analytics layer by migrating to ClickHouse Cloud. To handle out-of-order and duplicate events from their Redpanda streaming pipelines, Trio implemented sliding-window refreshable materialized views alongside ReplacingMergeTree engines rather than recomputing entire historical datasets. A custom ETL service manages schema drift before inserting clean records into ClickHouse, while batch ingestion is coordinated through Airflow. Consequently, Trio reduced storage consumption by approximately 88% through columnar compression, ingested a 5-billion-row migration without issue, and maintained real-time query performance across more than one billion daily ledger data points.


### [April 2026 newsletter](https://yomu.fyi/post/april-2026-newsletter.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: Apr 16, 2026
- Type: Announcement

ClickHouse 26.3 LTS introduces experimental materialized common table expressions, WebAssembly user-defined functions, and enables asynchronous inserts by default. Alongside the release, Alasdair Brown launched clickhousectl to manage local installations and cloud deployments with built-in agentic development features and authentication safeguards. User case studies show Goldsky accelerating historical blockchain backfills by twelve times through direct ClickHouse reads using Apache Arrow, replacing their prior Kafka and Avro architecture. Additionally, the engineering team redesigned the full-text search index with front-coding compression, a sparse lookup index, and adaptive posting lists to handle expensive random reads on object storage. ClickHouse also previewed community architecture patterns for lightweight SIEM setups using Clickdetect and Wazuh log collection.


### [Getting started with clickhousectl: the ClickHouse CLI for local and cloud](https://yomu.fyi/post/getting-started-with-clickhousectl-the-clickhouse-cli-for-local-and-cl.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Al Brown
- Published: Apr 16, 2026
- Type: Tutorial

ClickHouse developers often face friction managing local versions, isolated instances, project scaffolding, and Cloud operations without relying on complex shell scripts. The open-source command-line tool clickhousectl addresses this by unifying local environment management and ClickHouse Cloud workflows. Locally, it downloads shared binaries, initializes standardized directory structures, and spins up named, isolated servers with automatic port assignments to facilitate upgrade testing. For cloud workflows, the CLI enforces a split authentication model where browser OAuth provides read-only access and API keys permit state mutations like service creation and scaling. Furthermore, the tool includes Claude Code agent skills covering best practices and deployment workflows, alongside support for CI/CD pipeline automation via environment variables and JSON output.


### [Our vision for the ClickHouse Grafana plugin](https://yomu.fyi/post/our-vision-for-the-clickhouse-grafana-plugin.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Alex Fedotyev
- Published: Apr 15, 2026
- Type: Problem & solution

ClickHouse is widely used with Grafana for observability and real-time analytics, but requiring users to write raw SQL creates onboarding and exploration friction. To simplify the user experience, planned updates to the ClickHouse Grafana plugin introduce native interfaces such as click-to-filter actions, categorized OpenTelemetry attribute displays, and guided variable editors. A new compact search mode leverages the hasToken function for fast log filtering, while a visual metrics builder simplifies aggregating OpenTelemetry and Kubernetes metrics without manual queries. Prebuilt dashboards for logs and traces will accelerate initial setup alongside automated annotations derived from service version changes and Kubernetes lifecycle events. Future prototypes also explore bidirectional SQL AST parsing for seamless query editing, JWT user identity forwarding for fine-grained access control, and conversational query generation.


### [Index-based pruning in ClickHouse](https://yomu.fyi/post/index-based-pruning-in-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: Apr 15, 2026
- Type: Explainer

ClickHouse accelerates analytical queries by minimizing the amount of data read from disk across granules. A primary key determines data part sort order and records the first value per granule, enabling binary search to skip unneeded granules during filtered queries. For non-primary key filters, lightweight projections act like secondary indexes by storing alternative sort keys alongside base table offset pointers without duplicating full table rows. Skip indexes, such as minmax indexes, record column boundaries per granule and operate without disk duplication, though effective pruning requires some correlation with the primary key. Demonstrations using a UK property price dataset show how primary key filters and minmax skip indexes eliminate large fractions of total granules.


### [What's new in ClickStack - March 2026](https://yomu.fyi/post/what-s-new-in-clickstack-march-2026.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: The ClickStack Team
- Published: Apr 14, 2026
- Type: Announcement

ClickStack announced several observability updates in its March 2026 release to improve root cause analysis, dashboard organization, and local development. The Event Deltas tool now displays baseline span distributions immediately, uses deterministic cityHash64 sampling for trace heatmaps, and applies proportional comparison scoring to accurately evaluate attributes across uneven sample sizes. Furthermore, users can directly include or exclude values from attribute comparison bars to streamline query creation during incident investigations. Dashboard capabilities expanded with a curated OpenTelemetry template gallery, multi-value filtering with optional lookup WHERE clauses, and broadened metric display units. Finally, local mode introduces browser localStorage persistence through createEntityStore, allowing developers to save dashboards and favorites without a running backend API.


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