momothereal

Writing to Kafka allowed them to continue their current ingestion process into MariaDB at the same time as ClickHouse. Kafka consumer groups allow the data to be consumed twice by different consumer pools that have different throughput without introducing bottlenecks.

From experience the Kafka tables in ClickHouse are not stable at a high volumes, and harder to debug when things go sideways. It is also easier to mutate your data before ingestion using Vector's VRL scripting language vs. ClickHouse table views (SQL) when dealing with complex data that needs to be denormalized into a flat table.

What is awful about the Helm provider for Terraform?

What happens when the tables and columns have cryptic names/acronyms? Do you need to inject documentation?

I'm working on a tool that does the opposite: to split layers into smaller, deterministic deltas.

Something tells me it will get shortened to "Apple unveils M3" soon

It looks like it just got taken offline

I feel like I used to see a lot more Launch HN, Y Combinator batches, etc. Or maybe I started tuning them out

I was going to ask the same. That is a really important feature to have to replace traditional indexes and usually poorly implemented in vector search libraries.

For example, filtering by arbitrary time range.