Last I checked, VMWare had moved away from differential datalog?
They switched from differential Datalog to differential SQL, I think because they realized Datalog is a really tough sell.
It is the only database/query engine that allows you to use the same SQL for both batch and streaming (with UDFs).
I have made an accessible version of a subset of Differential Dataflow (DBSP) in Python right here: https://github.com/brurucy/pydbsp
DBSP is so expressive that I have implemented a fully incremental dynamic datalog engine as a DBSP program.
Think of SQL/Datalog where the query can change in runtime, and the changes themselves (program diffs) are incrementally computed: https://github.com/brurucy/pydbsp/blob/master/notebooks/data...
Flink SQL also checks that box.
There has to be some change in the code, and they will not share the same semantics (and perhaps won't work when retractions/deletions also appear whilst streaming). And let's not even get to the leaky abstractions for good performance (watermarks et al).
The narrator's interjections were a great touch. It's rare to see a post that is this technically deep but also so fun to read. The journey through optimizing the aliasing query felt like a detective story. We, the readers, were right there with you, groaning at the 50GB memory usage and cheering when you got it down to 5GB.
Fantastic work, both on the code and the prose.
I'm encouraged by the other commenters sharing their recent Datalog projects. I am currently building a set of data quality pipelines for a legacy SQL database in preparation of a huge software migration.
We find Datalog much more useful in identifying and looking for data quality issues thatn SQL, as the queries can be incredibly readable when well-structured.
All of this is to say, I think Datalog 2.0's sparse attendance this year may be more indicative of the fact that it is a satellite workshop of an already-lesser-prestigious conference (itself not even the main event! That was ICLP!) rather than a lack of Datalog implementation excitement.
For what it's worth, none of what I'm saying is meant to rebut your high-level point that there is little novelty left in implementing raw Datalog engines. Of course I agree, the research space has moved far beyond that (arguably it did a while ago) and into more exotic problems involving things like streaming (HydroFlow), choice (Dusa), things that get closer to the general chase (e.g., Egglog's chase engine), etc. I don't think anyone disagrees that vanilla Datalog is boring, it's just that monotonic, chain-forward saturation (Horn clauses!) are a rich baseline with a well-understood engineering landscape (esp in the high-performance space) to build out more interesting theories (semirings, Z-sets, etc..).
It is slow going, partly since it is not a priority, partly because I suffer from second system syndrome. Mangle Rust should deal with any size data through getting and writing facts to disk via memory mapping. The golang implementation is in-memory.
This post is nice because it parses datalog and mentions the LSM tree, and much easier to follow than the data frog stuff.
There are very many datalog implementations in Rust (ascent, crepe) that use proc-macros. The downside is that they won't handle getting queries at runtime. For the static analysis use case where queries/programs are fixed, the proc macro approach might be better.
Leynos•6h ago
I'm in the middle of putting together a realtime strategy game using Differential Datalog[1] and Rust, with DDL managing the game's logic. Mostly as an excuse to expose myself to new ideas and engage in a whole lot of yak shaving.
[1] https://github.com/vmware-archive/differential-datalog
Yoric•6h ago
I'll be interested in reading how this goes!
cmrdporcupine•5h ago