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Clickhouse
High-performance open-source columnar database management system designed for real-time analytics and data warehousing at scale
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Do you still need Elasticsearch for log analytics? ClickHouse says no.
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.
Tom Schreiber, Lionel PalacinClickHouse integrates with Lakehouse Runtime Catalog
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.
Melvyn PeignonClickHouse Cloud on Google Cloud Now Powered by Google Axion Processors: 30–55% Faster Queries, ~15% Fewer Compute Credits
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.
ClickHouseUsing the ClickHouse MCP server with Google Antigravity
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.
Dustin HealyIndex sharding in ClickHouse Cloud: Petabyte-scale data needs petabyte-scale indexing
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.
James CunninghamClickHouse expands strategic collaboration with Google Cloud, bringing deeper integration, cloud flexibility, and next-generation performance
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.
Tanya BraginTerraform & OpenAPI for ClickPipes is now Generally Available
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.
Marta Paesotel.fyi - OTel Collector docs made simple
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.
The ClickStack Team“A generational leap”: How Trio unified payment analytics and cut storage by 88% with ClickHouse Cloud
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.
ClickHouseApril 2026 newsletter
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.
Mark NeedhamGetting started with clickhousectl: the ClickHouse CLI for local and cloud
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.
Al BrownOur vision for the ClickHouse Grafana plugin
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.
Alex FedotyevIndex-based pruning in ClickHouse
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.
Mark NeedhamWhat's new in ClickStack - March 2026
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.
The ClickStack TeamAgentic coding with ClickHouse. One person, one data stack, one full-stack application
A Solutions Architect at ClickHouse built ClickShop, a full-stack retail analytics platform, within a few days using Cursor to demonstrate production-level capabilities beyond static slides. The platform pairs ClickHouse for sub-second analytical queries across billions of rows with PostgreSQL for transactional workflows, using ClickPipes for real-time change data capture synchronization. LibreChat orchestrates 18 persona-tailored AI agents, while dual observability layers—ClickStack for OpenTelemetry infrastructure metrics and Langfuse for LLM prompt and cost tracking—store their telemetry directly in ClickHouse. Developing with an AI-assisted IDE was accelerated by open-source documentation and community skills, though it required continuous human review to handle unintended code refactoring and token consumption.
Oussama ChakriMintlify boosts NPS 30% and saves 60% with real-time analytics on ClickHouse Cloud
Documentation platform Mintlify experienced major dashboard performance degradation and rate limiting under PostHog as multi-tenant analytics traffic grew rapidly. Because PostHog lacked incremental materialized views, queries scanned full raw datasets and took tens of seconds to load. Mintlify migrated its customer-facing analytics infrastructure to ClickHouse Cloud, ingesting events through Amazon MSK and ClickPipes into a single events table backing incremental materialized views. The team executed the transition over two weeks by dual-writing events and replicating the existing PostHog schema to preserve API surface compatibility. The migration reduced dashboard query latency to sub-one-second, decreased related support bug reports to zero, lifted NPS by an estimated 30%, and lowered analytics infrastructure costs by approximately 60%.
ClickHouseIntroducing clickhousectl: the CLI for ClickHouse local and cloud (beta)
ClickHouse released the beta version of clickhousectl, an official command-line interface designed to unify local operations and cloud infrastructure management for both humans and AI agents. The tool acts as a version manager that stores reusable binaries globally in ~/.clickhouse/versions/ while offering optional project directory scaffolding for SQL files. Developers and agents can run multiple isolated local server instances concurrently with automatic free-port detection and dedicated data directories. For ClickHouse Cloud, the CLI provides complete management coverage with structured JSON output, using read-only OAuth or scoped API key authentication to safeguard modifying operations. Additionally, the tool can install ClickHouse Agent Skills directly into supported coding agents to facilitate automated local and cloud interactions.
Al BrownClickHouse at FOSDEM 2026
The ClickHouse community and development team participated in FOSDEM 2026 in Brussels, hosting a community dinner and delivering technical presentations across multiple developer tracks. Presentations covered a range of engineering topics, including building geospatial analytical applications with Leaflet and ClickHouse, as well as integrating LLVM XRay to dynamically hot-patch production logging without redeployments. Additional ClickHouse sessions addressed the engineering challenges of integrating Rust libraries into large C++ codebases, optimizing low-level memcpy routines, and implementing specialized inverted indexes for large-scale text processing. Contributed community sessions detailed workflows for upstream patches in MariaDB and Postgres, alongside WebShield, an open-source Python daemon that streams web accelerator access logs into ClickHouse for bot analysis.
Tyler HannanThe three villains to agentic observability: retention, sampling and rollups
Observability practices increasingly depend on data sampling, short retention windows, and rollups to control storage costs. While human operators historically compensated for these compromises with intuition, AI agents lack implicit operational context and require full-fidelity telemetry to diagnose issues and justify conclusions. Head sampling drops unobserved spans early, tail sampling risks missing implicit non-error anomalies, and aggressive log expiration prevents long-term cyclical trend analysis. Addressing these constraints requires decoupling storage and compute via high-compression object storage and dedicated compute pools. Deploying real-time analytical databases such as ClickHouse enables high-concurrency, low-latency SQL querying, log clustering, and structured in-database summarization necessary for automated reasoning loops.
Mike ShiClickHouse Release 26.3
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.
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