After watching this video, my first thought was whether recent results from columnar compression (e.g. https://docs.vortex.dev/references#id1) applied "naively" like QOI would have good results.

I started with a 1.79MiB sprite file for a 2D game I've been hacking on, and here are the results:

  PNG: 1.79 MiB
  QOI: 2.18 MiB
  BtrBlocks: 3.69 MiB

So, there's magic to being Quite OK that is more than just applying compression techniques than elsewhere :)

Erik Demaine has a few great lectures on succinct data structures too: L17 and L18 on https://courses.csail.mit.edu/6.851/spring12/lectures/

https://hacks.mozilla.org/2019/11/announcing-the-bytecode-al...

this looks to be solving a different problem than A*, which operates over discrete graphs. this looks to be operating in 2D continuous space instead.

so, what is the algorithm for finding the optimal point on the obstacle's outline for bypass (4)? is it finding the point on the outline nearest the destination?

then, how do you subsequently "backtrack" to a different bypass point on the obstacle if the first choice of bypass point doesn't work out?

there's something interesting here for trying to directly operate on 2D space rather than discretizing it into a graph, but I'm curious how the details shake out.

it's interesting that the wins for batching start diminishing at 8. I'm curious then how the subsequent optimizations fare with batch size 8 (rather than 128).

smaller batch sizes are nice since it determines how much request throughput we'd need to saturate this system. at batch size 8, we need 1s / ~30ns * 8 = 266M searches per second to fully utilize this algorithm.

the multithreading results are also interesting -- going from 1 to 6 threads only improves overhead by 4x. curious how this fares on a much higher core count machine.

and, if we want to keep it as a spinlock, I'm curious how much the immediate wakeup compares to using `tokio::task::yield_now`: https://docs.rs/tokio/latest/tokio/task/fn.yield_now.html

I'd be curious if y'all end up supporting adding filter attributes to the inverted index that can then be pushed down into the posting list traversal.

for example, a restaurant search app may have (1) an embedding for each restaurant but also (2) a cuisine. then, if a restaurant has `cuisine = Italian`, we'd also store its ghost ID in a `cuisine:Italian` posting list.

at query time, the query planner could take a query like `SELECT * FROM t1 WHERE cuisine = 'Italian' ORDER BY DISTANCE(..)` and emit a plan that efficiently intersects the `cuisine:Italian` posting list with the union of the partitions' posting lists.

this feels to me like a potential strength of the inverted indexing approach compared to graph-based approaches, which struggle with general filtering (e.g. the Filtered-DiskANN paper).

I'd be curious how y'all represent the posting lists for each partition in InnoDB:

- what IDs are you storing in the posting lists?

- how are the posting lists represented on disk? are they using compression and/or some form of skip indexing? the paper seemed to use a pretty simple block-based representation, but I'm curious what works well in practice.

- how do the posting list data structures themselves handle incremental updates and MVCC?

if you don't want to manage your own infrastructure, you can use our hosted product, but otherwise it's totally fine to self-host the open source binary.

(convex cofounder here)

we were heavily influenced by the original foundationdb testing talk from strange loop (wwilson, one of the antithesis founders): https://www.youtube.com/watch?v=4fFDFbi3toc

happy to answer any questions.

other than its runtime characteristics, the codebase is well organized and a great resource for learning about information retrieval.

one followup I was thinking of is whether this can generalize to queries other than key value point lookups. if I'm understanding correctly, the article is suggesting to take a key value store, and for every `(key, value)` in the system, split `value` into fragments that are stored on different shards with some `k` of `M` code. then at query time, we can split a query for `key` into `k` subqueries that we send to the relevant shards and reassemble the query results into `value`.

so, if we were to do the same business for an ordered map with range queries, we'd need to find a way to turn a query for `interval: [start, end]` into some number of subqueries that we could send to the different shards and reassemble into the final result. any ideas?