- Vector databases and hybrid search?

- Object storage for all the things? Lake houses. Parquet and beyond.

- Continuously materialized views? I'm not sure this one has made the splash but I think about Naiad (Materialize) and Noria (Readyset)

- NewSQL went mostly mainstream (Spanner wasn't included in the last one, but there's been more here with things like CockroachDB, TiDB, etc)

https://github.com/hyperium/tonic

Another interesting thing about TPC-C is how the cross-warehouse contention was designed. About 10% of new order transactions need to do a cross-warehouse transaction. If you can keep up with the workload, then the rate of contention is relatively low; most of the workload isn't pushing on concurrency control. If, however, you fall behind, and transactions start to take too long, then the contention will pile up.

When you run without the keying time, it turns out that concurrency control begins to dominate. For distributed databases, concurrency control and deadlock detection is fundamentally more expensive than it can be for single-node databases -- so it makes sense that a classically single-node database would absolutely trounce distributed databases. I like to think of tpcc "nowait" as really a benchmark of concurrency control because, due (I believe) to Amdahl's law the majority of its execution time ends up just in the contended portion of the workload.

Also very interesting that, as Justin points out, the workload sets up the warehouses so there is never cross-node contention. That's wild! I'm glad they didn't go and benchmark against even more distributed databases (like YugabyteDB, Spanner, or CockroachDB) and call it a fair fight.

I understand there are practical reasons why you might want to just choose a concurrency and let it rip at a fixed warehouse size and say, “I ran TPC-C”, but you didn’t!

TPC-C when run properly is effectively an open-loop benchmark that scales where the load scales with the dataset size by having a fixed number of workers per warehouse (2?) that each issue transactions at some rate. It’s designed to have a low level of builtin contention that occurs based on the frequency of cross warehouse transactions, I don’t remember the exact rate but I think it’s something like 10%.

The benchmark has an interesting property that if the system can keep up with the transaction load by processing transactions quickly, it remains a low contention workload but if it falls behind and transactions start to pile up, then the number of contending transactions in flight will increase. This leads to non-linear degradation mode even beyond what normally happens with an open loop benchmark — you hit some limit and the performance falls off a cliff because now you have to do even more work than just catching up on the query backlog.

When you run without think time, you make the benchmark closed loop. Also, because you’re varying the number of workers without changing the dataset size (because you have to vary something to make your pretty charts), you’re changing the rate at which any given transaction is going to be on the same warehouse. So, you’ve got more contending transactions generally, but worse than that, because of Amdahl’s law, the uncontended transactions will fly through, so most of the time for most workers will be spend sitting waiting on contended keys.

We really wanted a model that could convincingly handle and reasonably schedule arbitrary combinations of schema change statements that are valid in Postgres. Unlike mysql postgres offers transactional schema changes. Unlike Postgres, cockroach strives to implement online schema changes in a protocol inspired by f1 [0]. Also, you want to make sure you can safely roll back (until you’ve reached the point where you know it can’t fail, then only metadata updates are allowed).

The model we came up with was to decompose all things that can possibly change into “elements” [1] and each element had a schedule of state transitions that move the element through a sequence of states from public to absent or vice versa [2]. Each state transitions has operations [3].

Anyway, you end up wanting to define rules that say that certain element states have to be entered before other if the elements are related in some way. Or perhaps some transitions should happen at the same time. To express these rules I created a little datalog-like framework I called rel [4]. This lets you embed in go a rules engine that then you can add indexes to so that you can have sufficiently efficient implementation and know that all your lookups are indexed statically. You write the rules in Go [5]. To be honest it could be more ergonomic.

The rules are written in Go but for testing and visibility they produce a datomic-inspired format [6]. There’s a lot of rules now!

The internal implementation isn’t too far off from the search implementation presented here [7]. Here’s unify [8]. The thing has some indexes and index selection for acceleration. It also has inverted indexes for set containment queries.

It was fun to make a little embedded logic language and to have had a reason to!

0: https://static.googleusercontent.com/media/research.google.c... 1: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa... 2: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa... 3: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa... 4: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa... 5: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa... 6: https://github.com/cockroachdb/cockroach/blob/master/pkg/sql... 7: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa... 8: https://github.com/cockroachdb/cockroach/blob/f48b3438a296aa...

Also, there’s usually some language (and compiler) specific garbage that makes the dwarf hard to use and requires special treatment.

I want it to be easier to have more crates. The overhead of converting a module tree into a new crate is high. Modules get to have hierarchy, but crates end up being flat. Some of this is a direct result of the flat crate namespace.

A lot of the toil ends up coming from the need to muck with toml files and the fact that rust-analyzer can’t do it for me. I want to have refactoring tools to turn module trees into crates easily.

I feel like when I want to do that, I have to play this game of copying files then playing whack-a-mole until I get all the dependencies right. I wish dependencies were expressed in the code files themselves a la go. I think go did a really nice job with the packaging and dependency structure. It’s what I miss most.

https://github.com/ajwerner/voronoi

It’s a very beautiful algorithm.

However, after that project I sort of came to dislike Fortune’s algorithm because it isn’t numerically stable with floating point numbers. If you have points that are colinear, or nearly colinear in fp, things can break. The delaunator is better in this regard iirc: https://github.com/mapbox/delaunator