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ClickHouse Newsletter

We're well into the summer in the Northern hemisphere, but the content keeps on coming!

Highlights from the 26.6 release include hypothetical skip indexes, AI embedding functions, and experimental support for continuous queries.

Charity Majors explains why ClickHouse is winning the observability wars, on the benchmarking front, Tom Schreiber puts Snowflake Interactive Tables head-to-head with ClickHouse Cloud, measuring cost and performance across the full pipeline, and if you're wrangling lots of small queries, Sanil Upadhyay has a guide to tuning ClickHouse for exactly that.

And don't miss our first-ever virtual hackathon, from July 17-23.

 

 

This month's featured community member is Rafeeq Abdul, Senior Director of Engineering for the Authorize.net payments solution at Visa.

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Rafeeq's team manages $250 million in annual revenue across 2 million authorization events, but until recently, getting an answer out of that data meant waiting 2-3 days for a specialized SQL engineer to write a report.

He led the move to conversational analytics, pairing ClickHouse Cloud with the open-source LibreChat interface and the ClickHouse MCP server so non-technical staff can now ask questions like "show revenue risk by merchant segment" in natural language.

This solution helped surface millions in at-risk revenue and has reclaimed 8-10 hours per user each week. Rafeeq shared the story at ClickHouse's 2026 Open House user conference in San Francisco.

➡️ Connect with Rafeeq on LinkedIn

 

Virtual Summer Hackathon: July 17-23

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We're running our first-ever virtual hackathon, and you should apply!

The ClickHouse & Trigger.dev Virtual Summer Hackathon runs July 17 - 23. Build a production-quality AI agent chat experience using ClickHouse and Trigger.dev, compete for a share of EUR 10,000 in prizes, and get your work in front of a panel of 15+ judges, including ClickHouse founder Alexey Milovidov.

Open to professional developers across NAMER and EMEA. Solo or teams up to 5. Applications close July 16.

➡️ Apply now

 

26.6 release

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My favorite feature from the 26.6 release is hypothetical skip indexes, which let you see whether adding a skip index to a table would improve query performance without actually building it.

This release also cleaned up how dependencies work for refreshable materialized views, so that we have proper cascading refreshable materialized views.

There's also experimental support for continuous queries and AI embedding functions, a PNG output format, documentation built into the CLI, and Geospatial improvements.

➡️ Read the release post

 

Have you heard? ClickHouse is winning the observability wars!

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Charity Majors, Co-founder and CTO of observability company Honeycomb, argues that ClickHouse's columnar architecture, not lower price, is why post-2019 observability vendors can keep scaling past the point where three-pillar tools become "unmanageable."

She quotes Mat Duggan on the core idea: "ClickHouse at 10 TB a day looks like ClickHouse at 1 TB a day, just with more shards."

At 10 TB/day, Duggan's numbers show ClickHouse costing over 35x less than Datadog. He notes that most companies at this scale go hybrid: Datadog for APM and metrics, plus a self-hosted stack (often ClickHouse itself) for logs, plus a "pre-processing pipeline team" whose only job is trimming data to cut the Datadog bill.

What frustrates Charity is that vendors built on that same columnar core hide the architecture and market themselves as cheap Datadog clones instead of owning why they're actually better.

➡️ Read the blog post

 

The end-to-end cost-performance of real-time analytics: Snowflake vs. ClickHouse Cloud

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Tom Schreiber, Mark Needham, and Lionel Palacin use CostBench to compare ClickHouse Cloud against Snowflake's Interactive Tables.

CostBench is an end-to-end benchmark that measures the true cost of real-time analytics across continuous ingestion, data maintenance, freshness, and query execution rather than query speed alone.

Running the full path over 28 hours, ClickHouse delivered better cost-performance, while Snowflake required a continuously running refresh warehouse to keep its pre-aggregated data up to date.

➡️ Read the blog post

 

We don't copy our Postgres tables to ClickHouse. We replay them.

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Sushant Yadav wires up Postgres>ClickHouse change-data-capture from off-the-shelf parts: Debezium decoding the WAL, Kafka carrying the row changes, and a ReplacingMergeTree sink keeping the latest version per key.

He then covers two challenges they encountered: silently-cached schemas on the sink and a replication slot that can fill the disk and freeze every database on the instance.

This blog gives a clear look at CDC on top of Postgres logical replication, which is the same foundation PeerDB / ClickPipes for Postgres CDC builds on, minus the Debezium-and-Kafka assembly you'd otherwise manage yourself.

➡️ Read the blog post

 

chDB as the Agent's Local Data Engine

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An AI agent runs as a loop of 5–20 tool calls per turn, and a large share of those calls are data lookups. But when that data lives across a network, latency compounds and every flaky call has to be retried - and a retry means re-sending the entire context window, which isn't cheap.

chDB fixes this by embedding a full ClickHouse query engine inside the agent's own process, so memory, session state, and hot lookups are now a local function call away.

➡️ Read the blog post

 

Tuning ClickHouse for high concurrency

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Sanil Upadhyay explains how to tweak ClickHouse settings to handle many concurrent small queries rather than a few large ones.

➡️ Read the blog post