---
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

### [What is WAL backpressure, and why does ClickHouse Managed Postgres need it?](https://yomu.fyi/post/what-is-wal-backpressure-and-why-does-clickhouse-managed-postgres-need.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 5, 2026
- Type: Explainer

Postgres writes every change to the write-ahead log (WAL), and Managed Postgres uploads completed segments to object storage for point-in-time recovery; if uploads lag, retained WAL can fill the disk and cause a PANIC shutdown. ClickHouse Managed Postgres counters this with a data-plane systemd timer that checks pending segments every 15 seconds and uses cgroup v2 I/O controls to throttle client write bandwidth at 80%, 50%, or 20% of provisioned throughput as backlog thresholds rise. The drain path—including the archiver, checkpointer, and related processes—is placed in an immune cgroup, while client backends are throttled and reads remain uncapped. In a test with a 500 MB/s gp3 volume and an archive limited to 4 MB/s, the backlog reached 3,433 segments and 53 GiB before recovery. Once the limit was removed, the archiver cleared the queue, the throttle released automatically, and the disk stayed below 33% full.


### [How Mercado Libre rebuilt its observability platform on ClickHouse Cloud with 50x faster trace queries](https://yomu.fyi/post/how-mercado-libre-rebuilt-its-observability-platform-on-clickhouse-clo.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 4, 2026
- Type: Problem & solution

Mercado Libre rebuilt its O11y events observability platform to answer granular business-level questions such as why a payment failed across a large microservice environment. The previous storage-layer design performed transformation, filtering, and aggregation at query time, causing queries to run longer than five minutes and making high-cardinality analysis expensive. The team moved storage to ClickHouse Cloud on AWS while retaining its existing ingestion path, and shifted heavy work into the database with materialized views, layered retention, and a dedicated lookup table for high-cardinality filtering. O11y events uses OpenTelemetry instrumentation with business-flow attributes and samples all traffic for critical applications. The resulting system reduced trace-query time to about four seconds, a 50x speedup, achieved up to 89% compression, and scaled from 7 million to 400 million spans per minute in ClickHouse.


### [Fixed cadence to seconds: making ClickHouse Cloud autoscaling more reactive](https://yomu.fyi/post/fixed-cadence-to-seconds-making-clickhouse-cloud-autoscaling-more-reac.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 4, 2026
- Type: Problem & solution

ClickHouse Cloud’s vertical autoscaling recommender originally ran on a fixed timer, creating latency when workloads suddenly increased and services needed resources before the next scheduled pass. The redesign keeps the periodic sweep as a safety net while adding a reactive fast path, using controller-runtime as an event-processing layer with sources feeding a shared rate-limited work queue and reconciler. The queue provides deduplication, exponential backoff, bounded concurrency, leader election, and metrics, while ClickHouse stores near-real-time signals that a polling source reads; the excerpt notes polling sets a floor on reaction speed. The reactive path is rolling out cautiously in observe-only mode, with periodic reconciliation remaining the main driver, and the stated goal is to trigger scale-ups for critical events such as out-of-memory conditions in seconds rather than waiting for the next scheduled run.


### [ClickHouse launches ClickHouse Labs with Andy Pavlo as VP of Database Research](https://yomu.fyi/post/clickhouse-launches-clickhouse-labs-with-andy-pavlo-as-vp-of-database.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 3, 2026
- Type: Announcement

ClickHouse announced the launch of ClickHouse Labs, a new research group led by Andy Pavlo, who joins as Vice President of Database Research. The group is intended to advance foundational work in database systems and share it openly with the broader community. Its research spans database and data architectures, query processing, systems performance, and infrastructure for both ClickHouse and PostgreSQL, with applications including real-time analytics, data warehousing, observability, and AI. Researchers will build and test ideas against demanding real-world workloads, collaborate with ClickHouse engineers, and publish findings in leading research venues so others can evaluate, extend, and build on them.


### [Andy Pavlo joins ClickHouse to establish ClickHouse Labs](https://yomu.fyi/post/andy-pavlo-joins-clickhouse-to-establish-clickhouse-labs.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 3, 2026
- Type: Announcement

Andy Pavlo announces that he is joining ClickHouse to establish and lead ClickHouse Labs, an industry research team focused on database systems. He describes ClickHouse’s early use of C++ and vectorized query execution with SIMD in 2016, and says its development has remained relevant to his academic research. Labs will work directly with ClickHouse engineers, customers, collaborators, and industry partners rather than operating separately, while also collaborating with the PostgreSQL team on performance and reliability for its managed service. An immediate priority is validating and pushing an existing backlog of ideas and optimizations into production, followed by research into hardware, algorithms, data structures, execution strategies, and DBMS development for AI and agentic workloads. The stated aim is to combine scientific research with technology that matters to users.


### [July 2026 newsletter](https://yomu.fyi/post/july-2026-newsletter.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 16, 2026
- Type: Announcement

The July 2026 newsletter rounds up ClickHouse releases, community projects, benchmarks, engineering work, and upcoming events in real-time data warehousing. Version 26.6 adds hypothetical skip indexes, cascading refreshable materialized-view dependencies, experimental continuous queries, AI embedding functions, PNG output, CLI documentation, and geospatial improvements. A CostBench comparison running the full analytics path over 28 hours found better cost-performance for ClickHouse Cloud than Snowflake Interactive Tables, which required a continuously running refresh warehouse for pre-aggregated data. Other features cover conversational analytics with ClickHouse Cloud, LibreChat, and MCP; Postgres change-data capture through Debezium and Kafka; local agent queries with chDB; high-concurrency tuning; and Silk, whose fiber runtime targets lower I/O tail latency. Quick reads report ingestion, caching, security, and multi-tenant SQL developments.


### [Why Trainy migrated from Amazon RDS Postgres to ClickHouse Managed Postgres](https://yomu.fyi/post/why-trainy-migrated-from-amazon-rds-postgres-to-clickhouse-managed-pos.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 15, 2026
- Type: Problem & solution

Trainy’s Pluto is an open-source experiment tracker that separates analytics metrics in ClickHouse Cloud from relational run metadata in Postgres. Its original metadata store, Amazon RDS Postgres, was too expensive for the volume observed and contributed to slower initial page loads, so Trainy migrated roughly 40–50 GB to ClickHouse Managed Postgres, which uses local NVMe storage and keeps both databases in one account. After migration, initial page loads became 2–3 times faster, while a complex query reported as taking 10–15 minutes previously returned in five seconds; visualization queries over runs with hundreds of thousands of steps typically return in 50–100 milliseconds at the P50. Trainy reports 100% reliable ingestion with zero dropped data points and says the unified stack could support future joins between GPU metrics and run metadata, as well as governed agent access.


### [Replacing the HDB: ClickHouse for historical ticker data](https://yomu.fyi/post/replacing-the-hdb-clickhouse-for-historical-ticker-data.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 15, 2026
- Type: Benchmark

Historical market-data systems separate a durable archive from the real-time trading path, but the archive still must support research, backtesting, transaction-cost analysis, and regulatory reconstruction. The worked example uses Binance’s public archive to load trades and quotes for five USD-M perpetual-futures symbols into ClickHouse, with a MergeTree table ordered by (symbol, ts) and codecs including DoubleDelta and ZSTD. SQL queries calculate VWAP, hourly OHLC candles, and as-of joins, while ClickHouse’s sparse primary index skips most granules for symbol-and-time filters. Across ten months, 16.2 billion rows compress to 76 GiB on disk; the post reports a ClickHouse Cloud cost of $1.88 per month and argues that typical symbol-and-time queries retain low latency at that scale, while broader scans read more data.


### [How Bullet uses ClickHouse Cloud to give DeFi's fastest exchange real-time analytics](https://yomu.fyi/post/how-bullet-uses-clickhouse-cloud-to-give-defi-s-fastest-exchange-real.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 15, 2026
- Type: Problem & solution

Bullet operates a DeFi exchange for perpetuals, spot trading, and lending, with trades settled on a blockchain while users retain custody of their assets. Its sub-millisecond trading system produces more than 150 million event rows per hour, creating a need for historical indexing, real-time analytics, and operational monitoring beyond the blockchain’s state-machine model. ClickHouse Cloud consumes events from Kafka, stores them in dedicated tables, and supports user features, leaderboard and reward calculations, P&L charts, audit logs, and Grafana-backed alerts. Replacing Databricks reduced query latency from 10–15 seconds to milliseconds, improved data freshness from 1–2 hours to under 5 seconds, and removed a separate DynamoDB serving layer at comparable cost while handling 1,000 times more data across staging, testnet, and mainnet.


### [@clickhouse/rowbinary: when your library is also a parser compiler](https://yomu.fyi/post/clickhouse-rowbinary-when-your-library-is-also-a-parser-compiler.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 14, 2026
- Type: Explainer

ClickHouse's @clickhouse/rowbinary is a Node.js reader and writer for the RowBinary, RowBinaryWithNames, and RowBinaryWithNamesAndTypes formats, offering both a generic parser and an accompanying Agent Skill. RowBinary avoids per-row overhead but is difficult to consume in JavaScript because nested types, LEB128 varints, precision-sensitive DateTime64 values, and recursive self-describing types require extensive runtime dispatch; JSON alternatives can also round large UInt64 values. The package provides small, composable, monomorphizable reading primitives, while SKILL.md instructs a coding agent to assemble query-specific parsers with inlined operations and selected policies such as BigInt handling and array materialization. Generated parsers run 1.5–3.4x faster than composing library functions in loops, with a fixed-width benchmark parser reaching 3.41x faster, and produce reviewable source that can be tested and benchmarked like ordinary application code.


### [How we configure huge pages in ClickHouse Managed Postgres](https://yomu.fyi/post/how-we-configure-huge-pages-in-clickhouse-managed-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 14, 2026
- Type: Tutorial

ClickHouse Managed Postgres configures Linux huge pages to reduce the per-connection page-table cost and translation overhead of a large PostgreSQL shared\_buffers cache. The configuration reserves a quarter of machine memory before PostgreSQL starts, verifies HugePages\_Total, sets huge\_pages = 'on' to prevent silent fallback, and sizes shared\_buffers against the complete shared-memory segment, including buffer descriptors, WAL buffers, and lock tables. On an r7i.4xlarge with 32GB shared\_buffers, 4KB pages added 31.1MB of page tables per connection and reached 6.12GB at 200 connections, while 2MB huge pages reached 111MB. A select-only pgbench test at 100 clients also rose from 373,083 TPS to 418,087 TPS, a 12% increase, while startup failure without a sufficient pool made misconfiguration explicit.


### [How Insider One uses ClickHouse Cloud to power real-time customer engagement at scale](https://yomu.fyi/post/how-insider-one-uses-clickhouse-cloud-to-power-real-time-customer-enga.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 13, 2026
- Type: Problem & solution

Insider One’s Unified Customer Database (UCD) unifies event data, user attributes, identity resolution, and profiles across channels to support real-time segmentation, personalization, and analytics. The platform initially ran ClickHouse in a self-managed AWS deployment, but growth brought larger datasets, more concurrent queries, and increasing work tuning clusters and balancing performance against cost. In a proof of concept, the team migrated an anonymized dataset from one of its largest customers to ClickHouse Cloud and replayed UI segmentation, audience-count, and scheduled-campaign workloads. Average query latency fell 50–60% without significant changes, reaching 70–75% for the largest customers, while latency percentiles became more predictable; the managed service also reduced operational burden and gave the team more capacity for product development and its AI-oriented roadmap.


### [ClickHouse on Docker Hardened Images](https://yomu.fyi/post/clickhouse-on-docker-hardened-images.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 10, 2026
- Type: Announcement

ClickHouse is now available as Docker Hardened Images (DHI), stripped-down builds for the ClickHouse server, ClickHouse Keeper, Kubernetes Operator and Helm chart, and metrics exporter. The standard image’s Ubuntu 22.04 base includes unused packages such as Perl, wget, and apt, whose known CVEs can block enterprise deployments even though ClickHouse does not use them. DHI replaces that base with a minimal image that has no package manager or network tools, runs as non-root, provides SLSA Level 3 provenance, and is maintained for CVE patching; Docker Scout reported 0 medium and 14 low findings for DHI, versus 8 medium and 11 low across 111 packages in the standard image. Migration preserves volumes and configuration, while development tooling remains available through a dev variant or temporary docker debug attachment.


### [One Driver, One Format, Every Language: ADBC](https://yomu.fyi/post/one-driver-one-format-every-language-adbc.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 10, 2026
- Type: Announcement

ClickHouse announces an official ADBC driver, a modern Arrow-native alternative to ODBC and JDBC for analytics and AI applications. It provides end-to-end columnar movement: query results leave ClickHouse as Apache Arrow and arrive in applications without a row-oriented conversion or staging step. A single Rust-built implementation connects through ClickHouse's HTTP interface, supports TLS, and serves languages and tools with ADBC bindings, including Ruby, R, and C. Users can install precompiled, signed binaries through the ADBC Driver Foundry and connect with a clickhouse:// URI; dbt Fusion distributes the driver directly. The driver also supports streaming Arrow inserts, while schema discovery, improved bulk ingest, usage examples, and JSON support remain on the roadmap.


### [How Trigger.dev is using ClickHouse to scale observability for long-running AI workflows](https://yomu.fyi/post/how-trigger-dev-is-using-clickhouse-to-scale-observability-for-long-ru.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 9, 2026
- Type: Problem & solution

Trigger.dev, an open-source platform for serverless asynchronous tasks, needed to scale observability for increasingly complex, long-running AI workflows spanning thousands of tenants. Its initial Postgres design stored logs, traces, and events in a growing table, making indexes, analytics, and operational reliability increasingly difficult to manage. The team moved observability workloads to ClickHouse after evaluating performance, scalability, open-source compatibility, deployment flexibility, and support for dynamic telemetry, including its native JSON data type. Migration occurred gradually at the task-execution level, routing each run’s logs to either database without downtime or customer intervention. ClickHouse now supports real-time telemetry analysis, approximately 200-millisecond P95 latency for queries returning tens of thousands of rows, and a dramatically smaller telemetry storage footprint.


### [Introducing pg\_re2, fast, RE2-powered regular expressions in Postgres](https://yomu.fyi/post/introducing-pg-re2-fast-re2-powered-regular-expressions-in-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 8, 2026
- Type: Announcement

pg\_re2 is a Postgres extension that brings RE2-powered regular expression functions and operators to Postgres, designed to address both execution speed and syntax compatibility with ClickHouse. It uses finite-automata-based RE2 rather than Postgres's backtracking POSIX engine, and version 0.4.0 adds the @~ operator plus btree and GIN index support. Benchmarks on an AWS m6g.2xlarge with PostgreSQL 18 report RE2's functions running at 1.8x to 8.6x the speed of native equivalents, while indexed operations are 1.1x to 1.8x faster. With pg\_clickhouse, using RE2-backed functions such as re2match() keeps regex behavior consistent between Postgres execution and ClickHouse pushdown, including patterns such as Quake\\b. The extension duplicates ClickHouse's suite for string search, replacement, extraction, and splitting.


### [AI needs the best-of-breed data stack: Postgres and ClickHouse](https://yomu.fyi/post/ai-needs-the-best-of-breed-data-stack-postgres-and-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 7, 2026
- Type: Explainer

AI-native applications generate large volumes of operational data, while agents and users increasingly require real-time answers from that data. This pressure raises both query concurrency and reliability requirements, with a cited sample of companies using Postgres and ClickHouse showing more than 1,000% data growth in six months and over 85 TB added. The proposed stack assigns Postgres low-latency transactional workloads and ClickHouse real-time analytics, connecting them through PeerDB and ClickPipes CDC with single-digit-second replication, pg\_clickhouse query pushdown, and pg\_stat\_ch telemetry streaming. The conclusion is that AI makes the OLTP/OLAP distinction more important, favoring an open best-of-breed stack over a single platform while preserving each database's specialized role.


### [chDB as the Agent's Local Data Engine](https://yomu.fyi/post/chdb-as-the-agent-s-local-data-engine.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 7, 2026
- Type: Explainer

Agents often make 5 to 20 tool calls per turn, and many of those calls retrieve data such as prior memory, recent events, or catalog records. When data is remote, round-trip latency compounds across sequential calls, while packet loss can trigger retries that increase both task time and token usage. The article presents chDB, which embeds a full ClickHouse query engine in the agent process, as a way to make data access local and CPU-bound rather than network-bound. For local memory, it combines structured, time-series, vector, and text-index capabilities with local storage and an embedding model, while append-only MergeTree rows preserve belief revisions, deletion markers, and point-in-time history. It also describes syncing trickle-written local memory to a warehouse on a slower cadence and using a materialized-view mirror for frequently accessed current state.


### [98% faster queries, 50% lower cloud costs: Verihubs’ journey from Postgres to ClickHouse](https://yomu.fyi/post/98-faster-queries-50-lower-cloud-costs-verihubs-journey-from-postgres.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 6, 2026
- Type: Problem & solution

Verihubs rebuilt its data warehouse after a Postgres-based, daily-batch pipeline became slow, unreliable, and difficult to correct when late vendor callbacks required backfills. The new architecture uses Kafka, Kafka Connect, and Debezium for real-time ingestion into ClickHouse, with MergeTree for append-only logs and ReplacingMergeTree for datasets updated through change data capture. Materialized views create daily and monthly rollups, projections accelerate existing query patterns, and Dagster handles complex detection workflows that can notify Slack. The migration, completed in about six months, reduced query times from 20–30 minutes to two or three seconds on datasets up to 18 million rows, representing as much as 98% faster queries and 50% lower infrastructure costs. Verihubs says the resulting real-time OLAP system gives finance and operations fresher data for traffic analysis, reconciliation, performance reporting, and invoicing.


### [Introducing AI dashboard and workflow generation in ClickStack](https://yomu.fyi/post/introducing-ai-dashboard-and-workflow-generation-in-clickstack.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 6, 2026
- Type: Announcement

ClickStack now supports AI-generated dashboards and linked investigative workflows from natural-language prompts, targeting the effort required to understand telemetry, write queries, choose visualizations, and iterate. Rather than asking an LLM to return a finished artifact, ClickStack uses an AI Notebook with the same MCP tools available to external agents, where queries, visualizations, findings, and reasoning are captured in a persistent, editable investigation. The system explores telemetry, validates assumptions, assembles dashboards, and can connect overview and service-level dashboards through dashboard actions; generated charts remain editable. The same MCP server exposes these capabilities to agents such as Claude, Cursor, and Codex, so users can create, refine, and extend dashboards through one toolchain.


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