Interested in seeing how ClickHouse works on your data? Get started with ClickHouse Cloud in minutes and receive $300 in free credits.

Get started today

How to query datetime columns in ClickHouse - from hourly bucketing to rush hour analysis with real taxi data.

large_ClickHouse Blog Banner (4).jpg

medium_ClickHouse Blog Banner (4).jpg

small_ClickHouse Blog Banner (4).jpg

thumbnail_ClickHouse Blog Banner (4).jpg

ClickHouse Blog Banner (4).jpg

Querying DateTimes in ClickHouse

Hello, and welcome to the March 2026 ClickHouse newsletter!

This month, we have an overview of Geospatial, the launch of chDB 4, how Hookdeck made payload search 60 times faster, The Agentic Data Stack, and more!

## Featured community member: Jamie Herre {#featured-community-member}

This month's featured community member is Jamie Herre, Sr. Director of Engineering at Cloudflare.

![](/uploads/mar2026_nl_image4_2c5abe927e.png)

Jamie leads engineering on Cloudflare's analytics infrastructure - a system that processes over 1.61 quadrillion events every day across more than 300 global data centers, built on ClickHouse.

At the <a href="https://clickhouse.com/blog/cloudflare" target="_blank">ClickHouse meetup in August 2025</a>, Jamie shared how his team designed for both explosive growth and catastrophic failure simultaneously. In one live demonstration, a single query scanned 96 trillion events in under 2 seconds - while a simulated North American outage caused European clusters to silently absorb the load without missing a beat.

➡️ <a href="https://www.linkedin.com/in/jherre/" target="_blank">Connect with Jamie on LinkedIn</a>

## Upcoming events {#upcoming-events}

### Global virtual events {#global-virtual-events}

* <a href="https://clickhouse.com/company/events/v26-3-community-release-call" target="_blank">v26.3 Community Call</a> - Mar 26, 2026
* <a href="https://clickhouse.com/company/events/202604-APJ-Korea-Webinar-CDC-ClickPipes" target="_blank">CDC ClickPipes: 데이터베이스를 ClickHouse로 복제하는 가장 빠른 방법</a> - Apr 1, 2026
* <a href="https://clickhouse.com/company/events/202603-AMER-Webinar-MaterializedViews" target="_blank">Under-the-Hood: ClickHouse Incremental Materialized Views and Dictionaries</a> - Apr 9, 2026
* <a href="https://clickhouse.com/company/events/202604-APJ-Webinar-Unified-Data-Stack-ClickHouse-Postgres" target="_blank">Combining Postgres & ClickHouse to Build a Unified Data Stack</a> - Apr 22, 2026

### Virtual training {#virtual-training}

* <a href="https://clickhouse.com/company/events/202603-AMER-EMEA-Observabiity-with-ClickStackLevel3" target="_blank">Observability with ClickStack: Level 3</a> - Mar 25, 2026
* <a href="https://clickhouse.com/company/events/202604-AMER-EMEA-Observabiity-with-ClickStackLevel2" target="_blank">Observability with ClickStack: Level 2</a> - Apr 7, 2026
* <a href="https://clickhouse.com/company/events/202604-APJ-query-optimization" target="_blank">Query Optimization with ClickHouse Workshop</a> - Apr 7, 2026

### Events in AMER {#events-in-amer}

* <a href="https://clickhouse.com/company/events/20250325" target="_blank">SRECon</a> - Seattle - Mar 25, 2026
* <a href="https://clickhouse.com/company/events/20260324RSA" target="_blank">Bay Area Iceberg Meetup: RSA Edition</a> - San Francisco - Mar 24, 2026
* <a href="https://clickhouse.com/company/events/20260326OBS" target="_blank">Seattle Observability Meetup</a> - Seattle - Mar 26, 2026
* <a href="https://clickhouse.com/company/events/20260331SF" target="_blank">Fireside Chat in San Francisco: Column Stores & the Evolution of Observability</a> - San Francisco - Mar 31, 2026
* <a href="https://clickhouse.com/company/events/20260401Start" target="_blank">Seattle Startup Summit</a> - Seattle - Apr 1, 2026
* <a href="https://clickhouse.com/company/events/2026048ICEY" target="_blank">Iceberg Summit</a> - San Francisco - Apr 8, 2026
* <a href="https://clickhouse.com/company/events/20260409SF" target="_blank">AI Demo Night</a> - San Francisco - Apr 9, 2026
* <a href="https://clickhouse.com/company/events/google-cloud-next-2026" target="_blank">Google Cloud Next 2026</a> - Las Vegas - Apr 22, 2026
* <a href="https://clickhouse.com/company/events/2026-houseparty-google-next" target="_blank">House Party, Google Cloud Next</a> - Las Vegas - Apr 22, 2026
* <a href="https://clickhouse.com/company/events/202605-global-open-house" target="_blank">Open House</a> - San Francisco - May 26, 2026

### Events in EMEA {#events-in-emea}

* <a href="https://clickhouse.com/company/events/202603-EMEA-Munich-meetup" target="_blank">ClickHouse Meetup Munich</a> - Munich - Mar 19, 2026
* <a href="https://talk.clickhouse.com/MjM4LUZQQy0zMTcAAAGgUzZGmrCsh9LwlOhfQ5S4NuJ8xeCq3mUb8G5BcC77kIWCSLiIIgxcRMEPPICfZJY68i42uok=" target="_blank">KubeCon EU</a> - Amsterdam, 23-26 March - Hall 2 stand 261
* <a href="https://talk.clickhouse.com/MjM4LUZQQy0zMTcAAAGgUzZGmmaesRMWlmOCJFDfP-dRMIF62KX7wSNwW7XuKDPuAZhovJEFU7vmLZKWfQhES_NXQvg=" target="_blank">DW&BI</a> - Utrecht, 24 March
* <a href="https://clickhouse.com/company/events/202603-EMEA-Milan-meetup" target="_blank">ClickHouse x Apache Kafka x AWS</a> - Milan - Mar 26, 2026
* <a href="https://ti.to/maltaawsusergroup/gen-a-i-day-malta/with/lpqxlepi9ww" target="_blank">ClickHouse x AWS x DoiT - Gaming and Betting AI Workshop</a> - Malta - March 26, 2026
* <a href="https://clickhouse.com/company/events/202603-EMEA-Benelux-Amsterdam-LunchandLearn" target="_blank">Lunch & Learn with ClickHouse</a> - Amsterdam - Mar 31, 2026
* <a href="https://aws.amazon.com/events/summits/paris/" target="_blank">AWS Summit Paris</a> - Paris, 1 April - Level 1 (nr. G3)
* <a href="https://clickhouse.com/company/events/202604-EMEA-Observability-with-ClickStack" target="_blank">Amsterdam In-Person Training: Observability with ClickStack</a> - Amsterdam - Apr 8, 2026
* <a href="https://events.confluent.io/awsstartupexchangevienna2026" target="_blank">CTO Networking Event with ClickHouse, AWS, Confluent & DoiT</a> - Vienna - Apr 9, 2026
* <a href="https://luma.com/wjv3v1tn" target="_blank">Scaling the Enterprise AI Operating Model</a> - Berlin - Apr 14, 2026
* <a href="https://clickhouse.com/company/events/202604-EMEA-Paris-Real-time-Analytics-w-ClickHouse" target="_blank">Paris In-Person Training: Real-time Analytics with ClickHouse</a> - Paris - Apr 15, 2026
* <a href="https://www.meetup.com/clickhouse-ireland-user-group/events/313793261" target="_blank">ClickHouse Meetup in Dublin</a> - Dublin - Apr 16, 2026
* <a href="https://grafana.com/events/grafanacon/" target="_blank">GrafanaCON</a> - Barcelona - Apr 20-22, 2026
* <a href="https://clickhouse.com/company/events/202604-EMEA-Barcelona-Real-time-Analytics-w-ClickHouse" target="_blank">Barcelona In-Person Training: Real-time Analytics with ClickHouse</a> - Barcelona - Apr 20, 2026
* <a href="https://aws.amazon.com/events/summits/london/" target="_blank">AWS Summit London</a> - London - Apr 22 - Booth G18
* <a href="https://riseof.ai/conference-2026/" target="_blank">Rise of AI Berlin</a> - Berlin - May 5-6, 2026
* <a href="https://aws.amazon.com/events/summits/tel-aviv/" target="_blank">AWS Summit Tel Aviv</a> - Tel Aviv - May 6, 2026
* <a href="https://datainnovationsummit.com/" target="_blank">Data Innovation Summit</a> - Stockholm - May 6-8, 2026
* <a href="http://gartner.com/en/data-analytics" target="_blank">Gartner Data & Analytics</a> - London - May 11-13, 2026
* <a href="https://www.revolutionbanking.es/" target="_blank">Revolution Banking</a> - Madrid - May 12, 2026
* <a href="https://www.platfor-ma.com/" target="_blank">Platforma 2026</a> - Tel Aviv - May 20, 2026
* <a href="https://aws.amazon.com/events/summits/hamburg/" target="_blank">AWS Summit Hamburg</a> - Hamburg - May 20, 2026
* <a href="https://clickhouse.com/company/events/202605-EMEA-London-Real-time-Analytics-w-ClickHouse" target="_blank">London 2-day In-Person Training: Real-time Analytics with ClickHouse</a> - London - May 19, 2026
* <a href="https://www.meetup.com/clickhouse-london-user-group/events/313759007/" target="_blank">ClickHouse Meetup London</a> - London - May 19, 2026
* <a href="https://cloudonair.withgoogle.com/events/cloud-ai-live-madrid-2026" target="_blank">Google Summit Madrid</a> - Madrid - May 28, 2026

### Events in APAC {#events-in-apac}

* <a href="https://2026.pythonasia.org/" target="_blank">Python Asia 2026</a> - Manila - Mar 21-22, 2026
* <a href="https://clickhouse.com/company/events/meetup-nz-23mar2026" target="_blank">Postgres + ClickHouse: Building a Real-Time Open-Source Data Stack</a> - Auckland - Mar 23, 2026
* <a href="https://dataengbytes.com/2026/auckland" target="_blank">DataEngBytes Auckland</a> - Mar 24, 2026
* <a href="https://clickhouse.com/company/events/meetup-nz-25mar2026" target="_blank">Wellington Data Eng Meetup</a> - Wellington - Mar 25, 2026
* <a href="https://clickhouse.com/company/events/202603-apj-shenzhen-meetup" target="_blank">ClickHouse Shenzhen Meetup</a> - Shenzhen - Mar 28, 2026
* <a href="https://clickhouse.com/company/events/202604-APJ-Korea-Webinar-CDC-ClickPipes" target="_blank">Korean Webinar: CDC ClickPipes: 데이터베이스를 ClickHouse로 복제하는 가장 빠른 방법</a> - Apr 1, 2026
* <a href="https://events.confluent.io/dswt2026mumbai" target="_blank">Data Streaming World Mumbai</a> - Apr 13, 2026
* <a href="https://events.confluent.io/dswt2026bangalore" target="_blank">Data Streaming World Bangalore</a> - Apr 16, 2026
* <a href="https://clickhouse.com/company/events/taipei-open-source-meetup" target="_blank">Taipei Open Source Meetup</a> - Taipei - Apr 16, 2026
* <a href="https://www.meetup.com/clickhouse-bangalore-user-group/events/313739871/" target="_blank">Bangalore Meetup with Alexey Milovidov</a> - Apr 18, 2026
* <a href="https://events.confluent.io/dswt2026jakarta" target="_blank">Data Streaming World Jakarta</a> - Apr 21, 2026
* <a href="https://clickhouse.com/company/events/202604-APJ-Webinar-Unified-Data-Stack-ClickHouse-Postgres" target="_blank">APJ Webinar: Combining Postgres & ClickHouse to Build a Unified Data Stack</a> - Apr 22, 2026
* <a href="https://clickhouse.com/company/events/202604-APJ-HoChiMinh-Real-time-Analytics-with-ClickHouse" target="_blank">Ho Chi Minh In-Person Training: Real-time Analytics with ClickHouse</a> - Ho Chi Minh - Apr 22, 2026
* <a href="https://aws.amazon.com/events/summits/bengaluru/" target="_blank">AWS Summit Bengaluru</a> - Apr 22-23, 2026

## 26.2 release {#26-2-release}

![](/uploads/mar2026_nl_image9_00d00cbf75.png)

My favorite feature in the recent ClickHouse 26.2 release is time-based block flushing for streaming data. This lets you batch inserts by time interval rather than row count, which is useful for low-throughput feeds like Wikimedia recent changes.

The release also brings production-ready text index and QBit data types, 3.2x faster RIGHT/FULL JOINs, and embedded ClickStack for in-product observability.

➡️ <a href="https://clickhouse.com/blog/clickhouse-release-26-02" target="_blank">Read the release post</a>

## Building towards an enterprise-grade Postgres service in ClickHouse Cloud {#building-towards-an-enterprise-grade-postgres-service}

![](/uploads/mar2026_nl_image3_b4e4cebe10.jpg)

Sai Srirampur introduces the enterprise-grade Postgres service, coming soon to ClickHouse Cloud, bringing cross-AZ high availability, point-in-time recovery, automated backups, and failover-safe CDC slots for ClickHouse integration.

In other Postgres news, Kaushik Iska introduces <a href="https://clickhouse.com/blog/pg_stat_ch-postgres-extension-stats-to-clickhouse" target="_blank">pg_stat_ch</a>, an open-source Postgres extension that streams query metrics directly into ClickHouse for latency analysis and error tracking without impacting production performance.

➡️ <a href="https://clickhouse.com/blog/enterprise-postgres-service-in-clickhouse-cloud" target="_blank">Read the blog post</a>

## Announcing chDB 4: Write Pandas, Run ClickHouse, Now on Hex {#announcing-chdb-4}

![](/uploads/mar2026_nl_image10_d4f00e1d08.png)

Ryadh Dahimene and Auxten Wang introduce chDB 4, which adds a Pandas-compatible DataStore API that executes on ClickHouse's engine under the hood.

Operations run lazily as an optimized pipeline, with automatic routing between ClickHouse and Pandas engines, and it's now available natively in Hex notebooks.

➡️ <a href="https://clickhouse.com/blog/chdb.4-0-pandas-hex" target="_blank">Read the release post</a>

## How Trigger.dev built a custom SQL language on top of ClickHouse {#how-trigger-dev-built-a-custom-sql-language}

![](/uploads/mar2026_nl_image6_3d154dfd7b.png)

Matt Aitken, CEO of Trigger.dev, explains how they gave users SQL access to a shared multi-tenant ClickHouse cluster without risking data leaks.

Their solution is TRQL, a SQL-style DSL that compiles to tenant-isolated ClickHouse queries - dangerous operations are grammatically impossible, and tenant filters are injected at compile time.

➡️ <a href="https://trigger.dev/blog/how-trql-works" target="_blank">Read the blog post</a>

## Announcing General Availability of ClickHouse Full-text Search {#announcing-general-availability-of-full-text-search}

![](/uploads/mar2026_nl_image7_a3a43dd469.png)

Melvyn Peignon announces the general availability of full-text search in ClickHouse, which uses native inverted indexes to enable fast token-based filtering at scale.

The implementation supersedes Bloom filters for string matching, delivering deterministic results without false positives and reducing the number of granules scanned by up to 96%.

To see it in action, Lionel Palacin built <a href="https://clickhouse.com/blog/gittrends" target="_blank">GitTrends</a>, an open-source demo that searches and aggregates nearly 10 billion GitHub events in real time, with a live comparison tool showing the performance differences between full-text search, Bloom filters, and a full table scan.

➡️ <a href="https://clickhouse.com/blog/full-text-search-ga-release" target="_blank">Read the blog post</a>

## How we made payload search 60x faster in ClickHouse {#how-we-made-payload-search-60x-faster}

![](/uploads/mar2026_nl_image5_7eaefa6d9a.png)

Maurice Kherlakian at Hookdeck describes how webhook payload search across millions of semi-structured JSON records was timing out at 30+ seconds, making debugging nearly impossible.

The fix: hashing values into typed bucket columns so queries scan a single bucket instead of all, combined with iterative time-window scanning that stops once enough results are found, bringing latency down to under 400ms.

➡️ <a href="https://hookdeck.com/blog/how-we-made-payload-search-60x-faster-in-clickhouse" target="_blank">Read the blog post</a>

## The Agentic Data Stack {#the-agentic-data-stack}

![](/uploads/mar2026_nl_image1_9d8086f2d3.png)

Dustin Healy outlines an open-source agentic data stack that lets AI agents query ClickHouse directly via natural language, replacing dashboards and data tickets with real-time conversational access.

The architecture combines ClickHouse's MCP server with an open-source LLM interface and Langfuse for observability, keeping data and infrastructure under the user's control.

➡️ <a href="https://clickhouse.com/blog/the-agentic-data-stack" target="_blank">Read the blog post</a>

## ClickHouse TTL in production: A safe strategy for data retention and disk optimization {#clickhouse-ttl-in-production}

![](/uploads/mar2026_nl_image8_5f21853f75.png)

Aliakbar Hosseinzadeh shares a production runbook for implementing ClickHouse TTL policies after his cluster hit 97% disk utilization, covering the key mental model shift: TTL runs during background merges, not at insert time.

The winning combination is to align partitioning with your TTL time unit and `set ttl_only_drop_parts=1`, which lets ClickHouse drop whole parts cleanly rather than triggering expensive mutation-style rewrites.

➡️ <a href="https://medium.com/@aliakbarhosseinzadeh/clickhouse-ttl-in-production-a-safe-strategy-for-data-retention-and-disk-optimization-9f1546fe673f" target="_blank">Read the blog post</a>

## Quick reads {#quick-reads}

* Mark Needham <a href="https://clickhouse.com/blog/state-of-geospatial-march-2026" target="_blank">surveys everything ClickHouse can do with geospatial data in 2026</a> - from Geometry types and spatial operations to H3 grid-based analytics that run 12x faster than bounding-box queries on 10 million rows.
* Fiona J. Sylvester shows how to run <a href="https://medium.com/@fiona.j.sylvester/wait-you-can-do-anomaly-detection-directly-inside-a-database-ba6ec8dceb8c" target="_blank">STL-based anomaly detection directly in ClickHouse</a>, catching subtle deviations like a silent 4% transaction drop that would be missed by fixed thresholds.
* Parade suggests that <a href="https://medium.com/@parade4940/stop-calling-it-saas-why-you-should-dump-microsoft-fabric-and-use-clickhouse-for-data-analytics-62e73b3cffbe" target="_blank">Microsoft Fabric's shared capacity model creates unpredictable performance</a> and runaway costs, and that swapping in ClickHouse as the analytics engine keeps Power BI intact while cutting bills from tens to thousands of dollars a month.
* Tom Schreiber <a href="https://clickhouse.com/blog/table-cloning" target="_blank">explains how the CLONE AS command</a> creates instant copies of tables of any size by hard-linking immutable data parts rather than copying bytes, enabling safe experimentation on production-scale data with near-zero storage overhead.
* Shuva Jyoti Kar <a href="https://medium.com/google-cloud/agentic-threat-hunting-conversational-telemetry-with-clickhouse-the-new-mcp-java-sdk-and-google-bb3d98aff1f3" target="_blank">built a SecOps agent</a> that uses Google's MCP Toolbox and ClickHouse to let analysts query millions of rows of security telemetry in plain English, without exposing the schema or letting the LLM write raw SQL.
* Mohamed Hussain S explains how <a href="https://medium.com/@mohhddhassan/the-clickhouse-mental-model-most-engineers-miss-c1f39b18f46f" target="_blank">understanding two ClickHouse internals</a> - aggregation states and argMax - unlocks simpler, more powerful query design that would require subqueries and joins in traditional SQL databases.


Welcome to the March 2026 ClickHouse newsletter, which will round up what’s happened in real-time data warehouses over the last month.

large_Newsletter Cover Facebook Post (18).png

medium_Newsletter Cover Facebook Post (18).png

small_Newsletter Cover Facebook Post (18).png

thumbnail_Newsletter Cover Facebook Post (18).png

Newsletter Cover Facebook Post (18).png

March 2026 newsletter

Dates come in all shapes and sizes - Unix timestamps from event streams, weird looking numeric dates from legacy database exports, ISO 8601 strings from APIs, and more. Lucky for us, ClickHouse has a rich set of functions to handle all of them and that's what we're going to explore in this blog post.

We'll start with the most explicit approaches: converting Unix timestamps with `fromUnixTimestamp`, parsing packed numeric dates with `YYYYMMDDToDate`, and parsing known format strings with `parseDateTime`. Then we'll look at the `parseDateTimeBestEffort` family for when the format is unknown or mixed. 

Finally, we'll cover how casting dates with the `cast_string_to_date_time_mode` setting might be a better choice than explicit function calls for some use cases.

<iframe width="768" height="432" src="https://www.youtube.com/embed/hVEAzVz_xIY?si=-8QknStXsAO9iMjI" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

## Unix timestamps {#unix_timestamps}

First up, Unix timestamps! Unix timestamps represent the number of seconds since January 1st, 1970. We can use the [`fromUnixTimestamp`](https://clickhouse.com/docs/en/sql-reference/functions/date-time-functions#fromUnixTimestamp) function to convert them:

<pre><code type='click-ui' language='sql' runnable='true'>
SELECT
    fromUnixTimestamp(1704067295) AS val1, toTypeName(val1);
</code></pre>

This returns a `DateTime` type. If you have milliseconds since January 1st, 1970, there's a different function — [`fromUnixTimestamp64Milli`](https://clickhouse.com/docs/en/sql-reference/functions/type-conversion-functions#fromUnixTimestamp64Milli) — and the type comes back as `DateTime64(3)`, where the `3` means precision up to milliseconds. 

<pre><code type='click-ui' language='sql' runnable='true'>
SELECT
    fromUnixTimestamp64Milli(1704067295123) AS val2, toTypeName(val2);
</code></pre>

For microseconds, [`fromUnixTimestamp64Micro`](https://clickhouse.com/docs/en/sql-reference/functions/type-conversion-functions#fromUnixTimestamp64Micro) returns `DateTime64(6)`:

<pre><code type='click-ui' language='sql' runnable='true'>
SELECT
    fromUnixTimestamp64Micro(1704067295123456) AS val3, toTypeName(val3);
</code></pre>

## Numeric date formats {#numeric_date_formats}

Sometimes dates are represented as plain numbers encoding the year, month, and day — with no separators or formatting. This is common in legacy database exports or flat files from mainframes. The function [`YYYYMMDDToDate`](https://clickhouse.com/docs/en/sql-reference/functions/date-time-functions#YYYYMMDDToDate) handles this:

<pre><code type='click-ui' language='sql' runnable='true'>
SELECT
    YYYYMMDDToDate(20240115) AS val1, toTypeName(val1);
</code></pre>

If the number also includes time information, [`YYYYMMDDhhmmssToDateTime`](https://clickhouse.com/docs/en/sql-reference/functions/date-time-functions#YYYYMMDDhhmmssToDateTime) handles that too:

<pre><code type='click-ui' language='sql' runnable='true'>
SELECT
    YYYYMMDDhhmmssToDateTime(20240115143022) AS val2, toTypeName(val2);
</code></pre>

## Known format strings {#known_format_strings}

APIs often return dates as strings. If you know the format, you can use [`parseDateTime`](https://clickhouse.com/docs/en/sql-reference/functions/type-conversion-functions#parseDateTime) with a MySQL date format string:

<pre><code type='click-ui' language='sql' runnable='true'>
SELECT
    parseDateTime('15/01/2024 14:30:22', '%d/%m/%Y %H:%i:%s') AS val1,
    toTypeName(val1);
</code></pre>

This returns a `DateTime` including the timezone. 

If you prefer Joda date format strings, there's [`parseDateTimeInJodaSyntax`](https://clickhouse.com/docs/en/sql-reference/functions/type-conversion-functions#parseDateTimeInJodaSyntax) which produces the same output:

<pre><code type='click-ui' language='sql' runnable='true'>
SELECT
    parseDateTimeInJodaSyntax('15/01/2024 14:30:22', 'dd/MM/yyyy HH:mm:ss') AS val2,
    toTypeName(val2);
</code></pre>

## Best effort parsing of DateTimes {#best_effort_parsing}

The previous three approaches all assumed we knew the exact date format. But what if we don't? That's where the [`parseDateTimeBestEffort`](https://clickhouse.com/docs/en/sql-reference/functions/type-conversion-functions#parseDateTimeBestEffort) family of functions comes in. Imagine we have dates in a mix of different formats:

<pre><code type='click-ui' language='sql' runnable='true'>
WITH dates AS (
    SELECT '2024-01-15T14:30:22.000Z' AS raw
    UNION ALL
    SELECT '2024-01-15' AS raw
    UNION ALL
    SELECT '1704067295' AS raw
)
SELECT raw, parseDateTimeBestEffort(raw) AS val, toTypeName(val)
FROM dates;
</code></pre>

We can also convert to `DateTime64` using [`parseDateTimeBestEffort64`](https://clickhouse.com/docs/en/sql-reference/functions/type-conversion-functions#parseDateTime64BestEffort), like the earlier functions:

<pre><code type='click-ui' language='sql' runnable='true'>
WITH dates AS (
    SELECT '2024-01-15T14:30:22.000Z' AS raw
    UNION ALL
    SELECT '2024-01-15' AS raw
    UNION ALL
    SELECT '1704067295' AS raw
)
SELECT raw, parseDateTime64BestEffort(raw) AS val, toTypeName(val)
FROM dates;
</code></pre>

What happens if we include a completely invalid date? 

<pre><code type='click-ui' language='sql' runnable='true'>
WITH dates AS (
    SELECT '2024-01-15T14:30:22.000Z' AS raw
    UNION ALL
    SELECT '2024-01-15' AS raw
    UNION ALL
    SELECT '1704067295' AS raw
    UNION ALL
    SELECT 'not a date' AS raw
)
SELECT raw, parseDateTime64BestEffort(raw) AS val, toTypeName(val)
FROM dates;
</code></pre>

ClickHouse throws an exception! 

We can work around this with the [`parseDateTimeBestEffort64OrNull`](https://clickhouse.com/docs/en/sql-reference/functions/type-conversion-functions#parseDateTime64BestEffortOrNull) variant, which returns `NULL` instead:

<pre><code type='click-ui' language='sql' runnable='true'>
WITH dates AS (
    SELECT '2024-01-15T14:30:22.000Z' AS raw
    UNION ALL
    SELECT '2024-01-15' AS raw
    UNION ALL
    SELECT '1704067295' AS raw
    UNION ALL
    SELECT 'not a date' AS raw
)
SELECT raw, parseDateTime64BestEffortOrNull(raw) AS val, toTypeName(val)
FROM dates;
</code></pre>

Or if you'd rather get an actual datetime value, [`parseDateTimeBestEffort64OrZero`](https://clickhouse.com/docs/en/sql-reference/functions/type-conversion-functions#parseDateTime64BestEffortOrZero) falls back to January 1st, 1970 at midnight:

<pre><code type='click-ui' language='sql' runnable='true'>
WITH dates AS (
    SELECT '2024-01-15T14:30:22.000Z' AS raw
    UNION ALL
    SELECT '2024-01-15' AS raw
    UNION ALL
    SELECT '1704067295' AS raw
    UNION ALL
    SELECT 'not a date' AS raw
)
SELECT raw, parseDateTime64BestEffortOrZero(raw) AS val, toTypeName(val)
FROM dates;
</code></pre>

## Casting {#casting}

If you'd rather avoid calling explicit parse functions throughout your queries, you can cast string values directly to date types using `::DateTime`. However, there's an important setting to be aware of: `cast_string_to_date_time_mode`.

By default it's set to `basic`, which handles standard formats like `YYYY-MM-DD` and `YYYY-MM-DD HH:MM:SS`, but anything else will fail. For broader format support, change it to `best_effort`. Note that this setting still throws an exception for completely invalid dates.

You can pass the setting inline per query:

<pre><code type='click-ui' language='sql' runnable='true'>
WITH dates AS (
    SELECT '2024-01-15T14:30:22.000Z' AS raw
    UNION ALL
    SELECT '2024-01-15' AS raw
    UNION ALL
    SELECT '1704067295' AS raw
)
SELECT raw, raw::DateTime AS val, toTypeName(val)
FROM dates
SETTINGS cast_string_to_date_time_mode = 'best_effort';
</code></pre>

Or configure it at the session level so you don't need it in every query:

<pre><code type='click-ui' language='sql'>
SET cast_string_to_date_time_mode = 'best_effort';
</code></pre>

Then the same query works without the `SETTINGS` clause:

<pre><code type='click-ui' language='sql'>
WITH dates AS (
    SELECT '2024-01-15T14:30:22.000Z' AS raw
    UNION ALL
    SELECT '2024-01-15' AS raw
    UNION ALL
    SELECT '1704067295' AS raw
)
SELECT raw, raw::DateTime AS val, toTypeName(val)
FROM dates;
</code></pre>

Finally, imagine that we have the following file that contains a variety of dates:

*dates.csv*
```csv
raw
2024-01-15T14:30:22.000Z
2024-01-15
1704067295
```

We can parse the dates in that file using the same approach:

<pre><code type='click-ui' language='sql'>
SELECT raw, raw::DateTime AS val, toTypeName(val)
FROM file('dates.csv', CSVWithNames);
</code></pre>

```shell
┌─raw──────────────────────┬─────────────────val─┬─toTypeName(val)─┐
│ 2024-01-15T14:30:22.000Z │ 2024-01-15 14:30:22 │ DateTime        │
│ 2024-01-15               │ 2024-01-15 00:00:00 │ DateTime        │
│ 1704067295               │ 2024-01-01 00:01:35 │ DateTime        │
└──────────────────────────┴─────────────────────┴─────────────────┘
```

5 Ways to Parse Dates and DateTimes in ClickHouse

large_Image 1920x1080.jpg

medium_Image 1920x1080.jpg

small_Image 1920x1080.jpg

thumbnail_Image 1920x1080.jpg

Image 1920x1080.jpg

5 ways to parse Dates and DateTimes in ClickHouse

Everything you need to know about ClickHouse as a geospatial analytics engine: its type system, indexing grids, strengths, and limitations.

large_Geospatial Blog Banner 2026.jpg

medium_Geospatial Blog Banner 2026.jpg

small_Geospatial Blog Banner 2026.jpg

thumbnail_Geospatial Blog Banner 2026.jpg

Geospatial Blog Banner 2026.jpg

State of Geospatial in ClickHouse in March 2026

One of my favorite features of ClickHouse 25.12 was the [`HMAC` function](https://clickhouse.com/blog/clickhouse-release-25-12#hmac) for message authentication using a shared key.

ClickHouse has always been able to act as a webhook endpoint, but with the HMAC function we can now verify webhook requests, filtering out those that didn't come from the expected source.
We'll start by seeing how this works with a local example, before moving onto a real-life example using ClickHouse Cloud as a GitHub webhook endpoint.

## The webhook pattern

One cool thing about ClickHouse is the ability to chain tables together using materialized views. Data gets ingested into one table, a materialized view acts as a SQL trigger, and the results are written to another table.

This becomes especially powerful when combined with the Null table engine. Rather than storing data, the Null table engine forwards
everything it receives to any connected materialized views — making it the perfect staging layer.

The result is a clean three-step pattern:
1. A staging table receives all incoming data
2. A materialized view validates and transforms it
3. A final table stores only the verified data

![Webhook Diagram Issue 1399.jpg](/uploads/Webhook_Diagram_Issue_1399_0e9e53eb69.jpg)

## Create the tables

Let's have a look at how to set this all up in ClickHouse.
If you want to follow along, you'll need to have a ClickHouse server or ClickHouse Cloud service running.

First, let's create the staging table:

<pre><code type='click-ui' language='sql'>
CREATE TABLE webhook_staging (
    received_at DateTime DEFAULT now(),
    raw_payload String,
    signature String DEFAULT getClientHTTPHeader('X-Hub-Signature-256')
) ENGINE = MergeTree()
ORDER BY received_at
SETTINGS allow_get_client_http_header=1;
</code></pre>

The `allow_get_client_http_header` setting is required because reading client headers is disabled by default.

> We're creating the `webhook_staging` table with the `MergeTree` engine, so that we can debug incoming requests. In a production system we would use the `Null` engine and might also have a second materialized view that captures invalid requests.

Next, we have a table that's going to store only verified rows:

<pre><code type='click-ui' language='sql'>
CREATE TABLE webhook_logs (
    received_at DateTime,
    payload JSON
) ENGINE = MergeTree()
ORDER BY received_at;
</code></pre>

And finally, we have a materialized view that validates the data:

<pre><code type='click-ui' language='sql'>
CREATE MATERIALIZED VIEW webhook_validator TO webhook_logs AS
SELECT
    received_at,
    raw_payload::JSON as payload
FROM webhook_staging
WHERE signature = 'sha256=' || lower(hex(HMAC('SHA256', raw_payload, 'my_secret_key')));
</code></pre>

We're comparing the signature from the request header to the expected signature computed using the shared secret key.
Our shared secret key is `my_secret_key`.

If the incoming row doesn't match the expected signature, it won't be written to the `webhook_logs` table.

## Create a restricted user

When experimenting on our own machine, we could use the default admin user, but for a production system it's safer to create a dedicated user with minimal permissions.
The user that we create will be used in the webhook URL that we construct later.

First, let's create the user:

<pre><code type='click-ui' language='sql'>
CREATE USER webhook_receiver
IDENTIFIED WITH sha256_hash
BY 'e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855';
</code></pre>

The hash is the SHA256 of an empty string, so no password is needed when we construct the webhook URL.

Next, let's grant the user the permissions it needs:

<pre><code type='click-ui' language='sql'>
GRANT INSERT ON default.webhook_staging TO webhook_receiver;
GRANT SELECT ON default.webhook_staging TO webhook_receiver;
GRANT SHOW TABLES ON default.webhook_staging TO webhook_receiver;
GRANT SHOW DATABASES ON default.* TO webhook_receiver;
</code></pre>

If we want to be extra cautious, we could also apply rate limits to this user to guard against abuse.

## Construct the webhook URL

ClickHouse accepts inserts over HTTP, which means the whole webhook endpoint is a URL with the INSERT query embedded:

```
http://localhost:8123/?user=webhook_receiver&query=INSERT+INTO+webhook_staging+(raw_payload)+FORMAT+RawBLOB
```

The key parts:
- `user=webhook_receiver` -the restricted user we created
- `query=INSERT INTO webhook_staging (raw_payload) FORMAT RawBLOB` -the URL-encoded query that inserts the raw request body

`FORMAT RawBLOB` tells ClickHouse to treat the entire request body as a single string value rather than trying to parse it.

We can then test it out by sending a valid request i.e. one where the signature is computed with the correct key:

<pre><code type='click-ui' language='bash'>
PAYLOAD='{"event":"user_login","user_id":456}'
SIGNATURE=$(echo -n "$PAYLOAD" | openssl dgst -sha256 -hmac "my_secret_key" | cut -d' ' -f2)

curl -X POST "http://localhost:8123/?user=webhook_receiver&allow_get_client_http_header=1&query=INSERT%20INTO%20webhook_staging%20(raw_payload)%20FORMAT%20RawBLOB" \
  -H "X-Hub-Signature-256: sha256=$SIGNATURE" \
  -d "$PAYLOAD"
</code></pre>

Let's now connect to our ClickHouse Server using ClickHouse Client:

<pre><code type='click-ui' language='bash'>
clienthouse client
</code></pre>

And we can return the contents of `webhook_staging` and `webhook_logs`:

<pre><code type='click-ui' language='sql'>
SELECT * FROM webhook_staging;
</code></pre>

```shell
Row 1:
──────
received_at: 2026-02-20 15:42:22
raw_payload: {"event":"user_login","user_id":456}
signature:   sha256=5a23c796b6248c725a6ec7fc2cf0788117d69d376ee6241f411a8887297d3ca4

1 row in set. Elapsed: 0.001 sec.
```

<pre><code type='click-ui' language='sql'>
SELECT * FROM webhook_logs;
</code></pre>

```shell
Row 1:
──────
received_at: 2026-02-20 15:42:22
payload:     {
    "event": "user_login",
    "user_id": 456
}

1 row in set. Elapsed: 0.003 sec.
```

Both tables have the data. Now how about we send a request where we've computed the signature with a different key:

<pre><code type='click-ui' language='bash'>
PAYLOAD='{"event":"user_login","user_id":456}'
SIGNATURE=$(echo -n "$PAYLOAD" | openssl dgst -sha256 -hmac "rogue_key" | cut -d' ' -f2)

curl -X POST "http://localhost:8123/?user=webhook_receiver&allow_get_client_http_header=1&query=INSERT%20INTO%20webhook_staging%20(raw_payload)%20FORMAT%20RawBLOB" \
  -H "X-Hub-Signature-256: sha256=$SIGNATURE" \
  -d "$PAYLOAD"
</code></pre>

Let's run our queries against `webhook_staging`:


```shell
Row 1:
──────
received_at: 2026-02-20 15:43:21
raw_payload: {"event":"user_login","user_id":456}
signature:   sha256=01f65041f2505f2b245f3caff410913a22a0ff8c7e8414c9fe1e861359973a7a

Row 2:
──────
received_at: 2026-02-20 15:42:22
raw_payload: {"event":"user_login","user_id":456}
signature:   sha256=5a23c796b6248c725a6ec7fc2cf0788117d69d376ee6241f411a8887297d3ca4

2 rows in set. Elapsed: 0.002 sec.
```

And now, `webhook_logs`:

```shell
Row 1:
──────
received_at: 2026-02-20 15:42:22
payload:     {
    "event": "user_login",
    "user_id": 456
}

1 row in set. Elapsed: 0.004 sec.
```

`webhook_staging` gets a new row, `webhook_logs` doesn't  - the signatures didn't match so the materialized view dropped it.

## GitHub webhook

With the pattern established locally, let's wire it up to a real GitHub repository using ClickHouse Cloud. The structure is almost identical, with just one difference - the staging table picks up an extra `event_type` column (from the `X-GitHub-Event` header GitHub sends) and `webhook_logs` picks an `event_type` column as well.

<iframe width="768" height="432" src="https://www.youtube.com/embed/9-2N58cZhoQ?si=acW0rRIHSWrjRHBg" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>


The queries below create our tables and materialized view.

<pre><code type='click-ui' language='sql'>
CREATE TABLE webhook_staging (
    received_at DateTime DEFAULT now(),
    raw_payload String,
    event_type String DEFAULT getClientHTTPHeader('X-GitHub-Event'),
    signature String DEFAULT getClientHTTPHeader('X-Hub-Signature-256')
) ENGINE = MergeTree()
ORDER BY received_at
SETTINGS allow_get_client_http_header=1;
</code></pre>

<pre><code type='click-ui' language='sql'>
CREATE TABLE webhook_logs (
    received_at DateTime,
    event_type String,
    payload JSON
) ENGINE = MergeTree()
ORDER BY received_at;
</code></pre>

<pre><code type='click-ui' language='sql'>
CREATE MATERIALIZED VIEW webhook_validator TO webhook_logs AS
SELECT
    received_at,
    event_type,
    raw_payload::JSON as payload
FROM webhook_staging
WHERE signature = 'sha256=' || lower(hex(HMAC('SHA256', raw_payload, 'my_secret_key')));
</code></pre>

In ClickHouse Cloud, click **Connect** to get your cluster URL, then build the webhook URL the same way - just swap `localhost:8123` for your Cloud host and add `allow_get_client_http_header=1`:

```
https://<host>:8443/?user=webhook_receiver&allow_get_client_http_header=1&query=INSERT+INTO+webhook_staging+(raw_payload)+FORMAT+RawBLOB
```

### Configure the GitHub webhook

In your GitHub repository, go to **Settings → Webhooks → Add webhook**:

1. Paste your ClickHouse URL into **Payload URL**
2. Set **Content type** to `application/json`
3. Enter your secret key in the **Secret** field -this must match `my_secret_key` from the materialized view
4. Choose which events to send (push events are fine to start)
5. Click **Add webhook**

![2026-02-20_15-48-01.png](/uploads/2026_02_20_15_48_01_9837c2f742.png)

GitHub will immediately send a `ping` event. Refresh the webhook page and you should see "Last delivery was successful".

![2026-02-20_15-48-31.png](/uploads/2026_02_20_15_48_31_e808d6d9c3.png)

### Verify it's working

We can then explore the contents of each table by clicking their name.

You should see the `ping` event in both, which confirms the HMAC validation passed. Now make a change to your repository -commit to a branch, open a PR, merge it -and push events will appear in both tables.

![2026-02-20_16-11-08.png](/uploads/2026_02_20_16_11_08_fbcc3df256.png)

### Testing signature validation

To confirm the validation is working, go back to GitHub, change the webhook secret to something different, then make another commit.

`webhook_staging` will have the new rows but `webhook_logs` won't - the materialized view computed a different HMAC and filtered them out.

![Webhook Diagram for Blog.jpg](/uploads/Webhook_Diagram_for_Blog_ae59da5240.jpg)

Ready to create your own webhook endoint?

Using ClickHouse as a webhook endpoint with HMAC verification

large_YouTube Thumbnail ClickHouse Issue 1394.jpg

medium_YouTube Thumbnail ClickHouse Issue 1394.jpg

small_YouTube Thumbnail ClickHouse Issue 1394.jpg

thumbnail_YouTube Thumbnail ClickHouse Issue 1394.jpg

YouTube Thumbnail ClickHouse Issue 1394.jpg


Hello, and welcome to the February 2026 ClickHouse newsletter!

This month, we have ClickHouse’s $400M Series D, the release of the official Kubernetes operator, a data modelling guide, how ClickHouse optimizes Top-N queries, and more!

## Featured community member: Ino de Bruijn {#featured-community-member}

This month's featured community member is Ino de Bruijn, Data Visualization Team Lead at Memorial Sloan Kettering Cancer Center's Cancer Data Science Initiative.  

![](/uploads/feb2026_nl_image1_a784b3580c.png)

Ino leads a team of engineers building software tools for cancer research, visualizing and disseminating data from major consortia including HTAN, Break Through Cancer, AACR GENIE, and the Gray BRCA Pre-Cancer Atlas. 

For nearly 11 years, he's also been instrumental in developing cBioPortal - the most popular cancer genomics tool worldwide, with over 3,000 daily users and more than 25,000 citations.

At the ClickHouse New York Meetup in December, [Ino presented on his team's work](https://clickhouse.com/videos/nymeetupdec82025) building a conversational AI interface for cBioPortal using ClickHouse, Anthropic's Claude, and LibreChat - a fully open-source solution making cancer research data more accessible to researchers and clinicians.

➡️ [Connect with Ino on LinkedIn](https://www.linkedin.com/in/inodb/)

## Upcoming events {#upcoming-events}

### Global virtual events

* [v26.1 Community Call](https://clickhouse.com/company/events/v25-12-community-release-call) - 26th February   
* [CDC ClickPipes: The Fastest Way to Replicate Your Database to ClickHouse](https://clickhouse.com/company/events/202602-APJ-Webinar-CDC-ClickPipes) - 26th February  
* [Under-the-Hood: ClickHouse Incremental Materialized Views and Dictionaries](https://clickhouse.com/company/events/202602-EMEA-Webinar-Materialized-Views) - 4th March

### Virtual training

* [ClickHouse Query Optimization Workshop](https://clickhouse.com/company/events/202602-amer-emea-query-optimization) - 19th February  
* [chDB: Data Analytics with ClickHouse and Python](https://clickhouse.com/company/events/202603-AMER-EMEA-chDB:Data-Analytics-with-ClickHouse-and-Python) - 18th March  
* [Real-time Analytics with ClickHouse](https://clickhouse.com/company/events/202603-AMER-Real-time-Analytics-w-ClickHouse) - 5th March

**Data Warehousing**

* [Data Warehousing with ClickHouse: Level 2](https://clickhouse.com/company/events/202602-AMER-data-warehousing-Level2) - 25th February  
* [Data Warehousing with ClickHouse: Level 1](https://clickhouse.com/company/events/202603-APJ-data-warehousing-Level1) - 3rd March  
* [Data Warehousing with ClickHouse: Level 2](https://clickhouse.com/company/events/202603-APJ-data-warehousing-Level2) - 4th March  
* [Data Warehousing with ClickHouse: Level 3](https://clickhouse.com/company/events/202603-APJ-data-warehousing-Level3) - 5th March

### Events in AMER

* [Toronto Meetup](https://www.meetup.com/clickhouse-toronto-user-group/events/312881151/?slug=clickhouse-toronto-user-group&eventId=310164482&isFirstPublish=true) - 19th February  
* [Seattle Meetup](https://luma.com/jsctpwoa) - 26th February  
* [LA Meetup](https://luma.com/wbkqmaqk) - 6th March   
* [Atlanta In-person Training: Real-time Analytics with ClickHouse](https://clickhouse.com/company/events/202603-AMER-Atlanta-Real-time-Analytics-w-ClickHouse) - 5th March

### Events in EMEA

* [ClickHouse Meetup in Tbilisi Georgia](https://www.meetup.com/clickhouse-georgia-meetup-group/events/312852206/) - 24th February

### Events in APAC

* [ClickHouse Melbourne Meetup](https://clickhouse.com/company/events/202602-APJ-Melbourne-Meetup) - 24th February  
* [Dink & Data: Executive Pickleball Social, Singapore](https://clickhouse.com/company/events/202602-APJ-Singapore-DinkDataHappyHour) - 25th February  
* [Webinar: CDC ClickPipes: The Fastest Way to Replicate Your Database to ClickHouse](https://clickhouse.com/company/events/202602-APJ-Webinar-CDC-ClickPipes) - 26th February  
* [ClickHouse + GDG + Deutsche Bank Bangalore Meetup](https://www.meetup.com/clickhouse-bangalore-user-group/events/313325219/) - 28th February  
* [ClickHouse Tokyo Meetup - LibreChat Night](https://www.meetup.com/clickhouse-tokyo-user-group/events/313275265/) - 9th March  
* [Data Streaming World Melbourne](https://clickhouse.com/company/events/202603-APJ-3PC-Melbourne-DataStreamingWorld) - 5th March  
* [Hackomania Singapore](https://hackomania.geekshacking.com/) - 7th - 8th March  
* [PGConf India](https://pgconf.in/conferences/pgconfin2026) - 11th - 13th March  
* [AWS Unicorn Day 2026 Seoul](https://clickhouse.com/company/events/202603-APJ-3PC-Seoul-AWSUnicornDay) - 17th March  
* [Python Asia 2026](https://2026.pythonasia.org/) - 21st - 23rd March

## 26.1 release {#release}

![](/uploads/feb2026_nl_image6_adff8e72ed.png)

The first release of 2026 adds support for the sparseGrams tokenizer to the text index, which also now supports arrays of Strings or FixedStrings.

There’s support for the Variant data type in all functions, new syntax for indexing projections, deduplication of asynchronous inserts with materialized views, and more!

➡️ [Read the release post](https://clickhouse.com/blog/clickhouse-release-26-01)

## ClickHouse raises $400M Series D, acquires Langfuse, and launches Postgres {#series_d}

![](/uploads/feb2026_nl_image7_c50bd14a9f.png)

ClickHouse closed a $400 million Series D funding round led by Dragoneer Investment Group, with participation from Bessemer Venture Partners, GIC, Index Ventures, Khosla Ventures, Lightspeed Venture Partners, T. Rowe Price Associates, and WCM Investment Management.

Alongside the funding announcement, ClickHouse [acquired Langfuse](https://clickhouse.com/blog/clickhouse-acquires-langfuse-open-source-llm-observability), an open-source LLM observability platform with over 20K GitHub stars and more than 26M+ SDK installs per month. Additionally, ClickHouse launched [an enterprise-grade PostgreSQL service](https://clickhouse.com/blog/postgres-managed-by-clickhouse) integrated with its platform.

➡️ [Read the blog post](https://clickhouse.com/blog/clickhouse-raises-400-million-series-d-acquires-langfuse-launches-postgres)

## Provable Completeness:  Guaranteeing Zero Data Loss in Trade Collection from Crypto Exchanges {#provable_completeness}

![](/uploads/feb2026_nl_image9_6dde790b1e.png)

Unreliable WebSocket connections and network interruptions create a persistent challenge to data quality in cryptocurrency market data collection. Koinju, a crypto platform built for finance professionals, ingests millions of trades per day across hundreds of markets. For their clients, even a single missing trade can distort volumes, P&L calculations, risk exposures, and regulatory reports - making data completeness non-negotiable.

In this blog post, Dmitry Prokofyev, CTO of Koinju, describes a novel solution using only ClickHouse to detect and automatically remediate missing trades from Coinbase. The architecture combines three ClickHouse features to create a self-healing system: Refreshable Materialized Views for detection, a separate validation service for REST API backfilling, and ReplacingMergeTree for automatic deduplication of resolved gaps.

➡️ [Read the blog post](https://koinju.io/blog/provable-completeness-how-we-achieve-zero-data-loss-in-trade-collection-from-crypto-exchanges)

## Introducing the Official ClickHouse Kubernetes Operator: Seamless Analytics at Scale {#kubernetes_operator}

![](/uploads/feb2026_nl_image5_891092f82b.png)

Grisha Pervakov introduces ClickHouse's official open-source Kubernetes Operator, designed to simplify the deployment and management of ClickHouse clusters on Kubernetes.

The operator enables rapid provisioning of production-ready clusters with built-in sharding and replication capabilities while eliminating the need for separate ZooKeeper installations by using ClickHouse Keeper for cluster coordination. 

➡️ [Read the blog post](https://clickhouse.com/blog/clickhouse-kubernetes-operator)

## AI-Generated analytics without wrecking your cluster {#ai_generated_analytics}

![](/uploads/feb2026_nl_image4_f4c62f344a.png)

Luke from Faster Analytics Fridays outlines three guardrail patterns for safely enabling AI-generated database queries without crashing clusters: 

1. Using pre-vetted query templates with parameter binding instead of raw SQL generation  
2. Exposing curated materialized views rather than raw tables, and   
3. Enforcing query budgets that validate estimated row scans and execution time before queries hit the database.

➡️ [Read the blog post](https://www.newsletter.hypequery.com/p/ai-generated-analytics-without-wrecking-your-cluster)

## Data modeling guide for real-time analytics with ClickHouse {#data_modeling_guide}

![](/uploads/feb2026_nl_image3_0addcbc0a4.png)

Simon Späti has written a comprehensive guide to designing optimized data models in ClickHouse for sub-second real-time analytics, emphasizing that performance comes from shifting computational work from query time to insertion time. 

The article covers core principles, including denormalization to minimize joins, partitioning by time and secondary dimensions for query pruning, and predicate pushdown optimization that moves filters closer to data sources. 

➡️ [Read the blog post](https://www.ssp.sh/blog/practical-data-modeling-clickhouse/)

## PostgreSQL + ClickHouse as the Open Source unified data stack {#postgres_clickhouse}

![](/uploads/feb2026_nl_image2_90bf827bd5.png)  
Lionel Palacin introduces an open-source unified data stack that combines PostgreSQL for transactional workloads with ClickHouse for analytics. 

It uses PeerDB for near-real-time CDC replication and the pg_clickhouse extension for transparent query offloading without rewriting SQL, enabling teams to start with PostgreSQL and add ClickHouse when analytical performance becomes critical.

➡️ [Read the blog post](https://clickhouse.com/blog/postgres-clickhouse-oss)

## Quick reads {#quick-reads}

* Mikhail Zharkov describes [building a scalable price distribution pipeline for trading systems using ClickHouse](https://mikhail-zharkov.medium.com/price-server-0b4da9d38f29).  
* Abhinaav Ramesh [built Ollama-Local-Serve](https://medium.com/@contactabhinaav/building-a-self-hosted-llm-server-youll-actually-use-dae13111447a), a self-hosted LLM server with complete observability, using ClickHouse for time-series analytics, OpenTelemetry instrumentation, FastAPI monitoring APIs, and a React dashboard with streaming chat.  
* Pranav Mehta describes [investigating ClickHouse connection retry warnings](https://medium.com/@pranavmehta94/clickhouse-internals-a-deep-dive-into-clickhouse-distributed-connection-pooling-d9e956b5eb57) in an on-prem environment that initially appeared to be a critical connection leak but turned out to be expected behavior when the connection pool attempts to reuse stale connections after idle periods.  
* Lionel Palacin [redesigned the data pipeline of ClickPy](https://clickhouse.com/blog/clickpy-2-trillion-rows), a ClickHouse-backed service that contains 2.2 trillion rows of Python package analytics. Data was previously ingested using custom batch scripts but has been migrated to ClickPipes and uses ClickHouse's lightweight deletes to correct historical data without rebuilding the entire dataset.  
* Tom Schreiber explains [how ClickHouse optimizes Top-N queries](https://clickhouse.com/blog/clickhouse-top-n-queries-granule-level-data-skipping) using granule-level data skipping with min/max metadata filtering, achieving 5-10× speedup and 10-100× reduction in data processed.


Welcome to the February 2026 ClickHouse newsletter, which will round up what’s happened in real-time data warehouses over the last month.

Mark Needham

Blog posts by Mark Needham

Querying DateTimes in ClickHouse

March 2026 newsletter

5 ways to parse Dates and DateTimes in ClickHouse

State of Geospatial in ClickHouse in March 2026

Using ClickHouse as a webhook endpoint with HMAC verification

February 2026 newsletter