LambdaMART's approach seems better in that respect.

https://medium.com/@nikhilbd/pointwise-vs-pairwise-vs-listwi...

The Husky blog post was released after we released a few versions of quickwit if I recall correctly. It was not an inspiration either.

As far as I know, the similarities are fortuitous.

The sentence in the blog post is a tad misleading. I suspect François is not really talking about VCs that had already invested in quickwit, but about the usual flow of other VCs who contacted us, to know about the company and be part of our eventual series A.

It just generally felt like we were "at a crossing".

No one twisted our arm.

I don't know SeekStorm's team and I did not dig much into the details, but my impression so far is that their benchmark's results are fair. At least I see no reason not to trust them.

    - it does not do vector search. It can rank docs using BM25, but usually people just want to sort by timestamp.
    - its does not use an SSD cache. Quickwit reads directly into the object storage.
    - it is append-only (you can't modify documents)
    - it scales really well and typically shines on the 1TB .. 100PB range
    - it has a Elastic search compatible API.

Also 100PB is measured as the input format (JSON). Internally Quickwit will have more efficient representations.

Medium retention (1 or 2 months) is still very appreciable if some issue in your bugtracker stay stale for this amount of time.

The high compression rate is VERY specific to logs.

- What happens when you alter an index configuration? Or add or remove an index?

Changing an index mapping was not available in 0.8. It is available in main and will be added in 0.9. The change only impacts new data.

- Or add or remove an index?

This is handled since the beginning.

- What about cold storage?

What makes Quickwit special is that we are reading everything is on S3. We adapted our inverted index to make it possible to read straight from S3. You might think this is crazy slow, but we typically search into TBs of data in less than a second. We have some in RAM cache too, but they are entirely optional.

> 2. Sampled data, generally for debugging. I would generally try to keep this at 10TB or less;

Sometimes, sampling is not possible. For instance, some of Quickwit users (including Binance) use their logs for user support too. A user might come asking details about something fishy that happened 2 months ago.

With a microservices architecture in particular that can pile up rapidly.

I'd be really surprised if you can make a 10x improvement here.

Usually our users search into > 1TB.

Let's imagine you have to search into 10TB (even after time/tag pruning). Distributing over 10k cores over 2 second is not practical and does not always economically make sense.

This log search infra is handled by two engineers who do that for the entire company.

They have some standardized log format that all teams are required to observe, but they have little control on how much data is logged by each service.

(I'm quickwit CTO by the way)

There are no equivalent technology, apart maybe:

- Chaossearch but it is hard to tell because they are not opensource and do not share their internals. (if someone from chaossearch wants to comment?)

- Elasticsearch makes it possible to search into an index archived on S3. This is still a super useful feature as a way to search punctually into your archived data, but it would be too slow and too expensive (it generates a lot of GET requests) to use as your everyday "main" log search index.