For example, if you take a look at our LSM compaction, regardless of the table size, we compact at the 512 KiB block granularity, and everything is streaming.

The same principle applies everywhere.

In our experience writing TigerStyle (and for all our internal code and tooling, not only TB as DBMS), we’ve never had a scenario where static allocation was not applicable or didn’t produce a better design.

You also tend to become more memory efficient, not less. Again, since you’re streaming. (You’re not allocating a massive buffer, just because a file is multi-GiB.)

The runtime allocation is type-aware, workload-aware, and schedule-aware. The last is most important. There are two places that can act as a sink for heavy memory demands: storage (i.e. paging to disk) and network (e.g. streaming results). These have their own limitations because I/O bandwidth is finite. Effectively, your allocation rate is equivalent to available I/O bandwidth.

The most powerful lever you have to manage this is total control of the schedule. Demands on memory are created by a set of operations or queries visible to the software. The scheduler doesn't incrementally execute these operations randomly, it continuously selects execution based on the availability of memory or bandwidth to absorb the allocation demand of the operation. The scheduler has the ability to control the allocation rate to instantaneously match availability.

This is essentially the very old idea of "optical buffering" -- treating fiber optic cables as RAM -- taken to its logical architectural conclusion.

The caveat is that this requires direct I/O in userspace, which places limits on software architecture. But if you care about performance, you'd be using this type of software architecture regardless.

That said, network latency usually follows a distribution. For example, the median might be 130 ms while p99 is 200 ms. So one important goal is to avoid being affected by the high-latency tail.

In consensus and replication systems such as TigerBeetle, you can reduce the impact quite a bit by taking advantage of the fact that you only need a quorum. We have six replicas, and under normal operation we only need acknowledgements from three (including the primary, since we use flexible quorums). That means the primary only has to wait for the two fastest replicas to respond. This is very effective at reducing tail latency.

Then, to get as close as possible to speed-of-light latency, you want to avoid adding unnecessary latency inside the system itself. We've done quite a few algorithmic optimizations there over the past year. For example, introducing radix sort and tournament trees to make CPU processing more efficient.

I'd check out tigerstyle.dev, pick up Zig, and then make an HTTP server or file format parser. Those are great ways to learn and experience this kind of programming. At some point, you start to realize that it's just easier to build API services in this way.

But for sure, you can learn so much in JVM-based languages. They make you appreciate low-level techniques all the more!

We still write, read (and have an independent engineer review) each line of code by hand.

We go faster like that, but, most of all, it’s the guarantee we make to our users, also to continue to invest in our own understanding, because second order that’s valuable for the kind of high performance safety work we do.

Long term, I’m sure LLMs will improve, but right now they’re just not there.