tbragin

Congrats! I'll be watching :)

Agreed that marketing yourself is not toxic. I follow "swyx" on Twitter and find his insight valuable, and so do a lot of my peers. Btw, looks like his Github profile has not been updated for some time - he's no longer Head of DX at Airbyte and is now an independent consultant. https://www.swyx.io/about

For a user perspective, you can also check out this recent blog: https://clickhouse.com/blog/hifis-migration-from-bigquery-to...

TL;DR * HiFi moved because BQ pricing and query latency did not fit the needs for their customer-facing app (music royalty data analytics) * Caveat when moving to ClickHouse: they had to adjust how they handle JOINs

Disclaimer: I also work at ClickHouse.

> Clickhouse the company is now providing a hosted solution. We are in India and were waiting for it to land in the India region to try it out.

FYI - ClickHouse Cloud is now available for self-service in ap-south-1 (Mumbai).

Hi Cliff - A write unit does not correspond to a single INSERT. A single INSERT with less than 16MB of data generates ~0.01 “write unit”, so a single “write unit” typically corresponds to ~100 INSERTs. In your example, that would come closer to $11 a day. Depending on how small of batches you plan to write in that examples, there may be ways to reduce that spend further, by batching even more or turning on "async_insert" inside ClickHouse.

So sorry about that! Could you send us an email at support@clickhouse.com and we'll get to the bottom of it?

An INSERT can consume less than one write unit, depending on how many rows and bytes it writes to how many partitions, columns, materialized views, data types, etc. So, a "write unit", which corresponds to hundreds of low-level write operations, typically translates to many batched INSERTS. We are working to improve our examples in the FAQ to clarify - thank you so much for asking the question!

This benchmark focuses on analytical query latency for representative analytical queries, so yes - lower number is better.

With an analytical database like ClickHouse, you can write many rows with a single INSERT statement (thousands of rows, millions of rows, and more). In fact, this kind of batching is recommended. Larger inserts will consume more write units than smaller inserts. Check out our billing FAQ for some examples, and we will be enhancing it with more detail as questions from our users come in (we'll work on clarifying this specific point): https://clickhouse.com/docs/en/manage/billing/ We also provide a $300 credit free trial to try out your workload and see how it translates to some of the usage dimensions. Finally, this is a Beta product, so keep the feedback coming!