Looking into SQLite’s innards is a great source of inspiration. Thanks for this post.
Edit: I'm prototyping in Python and implementing in Rust with intent to create a C API and embedding a Scheme runtime for "server"-side query and constraint parsing.
The hard part is getting all of that consistent with concurrent writes. Can rows change while you scan? can indexes? How do you check that your write is valid immediately before committing, etc. things like that.
I think SQL makes that pretty hard already, but in a "database-as-a-bag-of-data-structures" mode I think that's going to get even harder.
To have good query optimization, you need to implement, at the very least, some form of dynamic programming, otherwise you will not be able to optimize queries that have more than half a dozen tables in selects. Then you have to implement selection of the best plan or approximation to it, which would make you implement beam search through space of all solutions you generated, and that's simplest case. For guaranteed optimization, you need to implement or utilize pseudoboolean optimization engine.
I am a database engine developer right now. ;)
Either way, using a WCO join combined with a data-structure that allows for efficient range estimate and dynamic variable ordering, completely obliviates the need for query planning.
Yes, of course, having implementation of all that completely obliviates the need for query planning. ;)
I can't help being sarcastic for a moment, sorry.
In my opinion, in your comment above you clearly demonstrated that even avoidance of query planning is hard, using as example (multi)set of triples for which it is possible to realize all indices.
If what you described is simpler than query planning, then query planning is hard.
Building an immutable path compressed radix tree is pretty straightforward and requires around 1-2kloc, and it's easy to keep track of the n-smallest hashes of leaf values, as well as the total count of leafs in the nodes. The sampling done by the min-hashes give you a good indication of two nodes overlap which the jaccard index is just a different name for.
The query engine itself is like 0.5kloc, and is just walking the different radix-trie indices simultaneously. The basic insight of worst case optimal (WCO) joins, is that it's a lot cheaper to join everything at once and treat it as a constraint propagation problem.
A LINQ style query parser takes up another 1-2kloc and is just a bunch of ol' boilerplate.
In total that's about as much code as your average large C++ codebase CMake file.
You could sketch the entire thing on a napkin and build it in a week if you've build something like it before.
Please, excuse my sarcasm again. But, tell me what to do if I didn't built something like this before? What if I built something like equality saturation engine, pseudoboolean optimization using SAT solver and/or beam search? Will it help me somehow?
You estimated code size at 4.5KLOC max. Given that C++ programmer delivers 20-25 debugged lines of code per hour in the long run (IBM's stats), it would take 225 hours of work. Given that PSP/TSP recommends planning for 4 hours-on-task per day, it will take 56 work days. My calculation suggests 2.5 work months to implement all that in the worst case of 4.5KLOC. Even the best case of 2.5KLOC would take a month and a half of work.
Yours' proposition is not a week's project. Not at all, you can't squeeze it that much.
Query planning is hard. Even if you try your best to avoid it.
And we have not even started talking about WHERE, GROUP BY and ORDER BY clauses' optimizations.
[1] shows the use of loop nest optimization combined with beam search. SQLite uses translation of joins into loop nests, and it transforms loop nests into a graph, the path in the graph represents a solution. The [1] shows simplified nesting graph that is linear, and in general it will be quadratic to the number of tables.
[1] https://www.sqlite.org/queryplanner-ng.html
I really like that approach. This is exactly an equality saturation [2] (saturate loop nesting through loop nesting commutativity) with the beam search as a selection phase.
[2] https://rosstate.org/publications/eqsat/
I think that equality saturation with beam search is a week long project if you already have built something like that. The difference? It will work for arbitrary jons.
Of course I am making fun of your statements.
Equality saturation requires quite careful planning and will not work for couple of months, you will keep finding something that fails. Beam search is just as hard. You can encode optimal solution selection problem as a pseudoboolean optimization problem, which is more straightforward than beam search, and [2] shows that Pueblo is no slouch there.
This is not to say that query planning is easy when you do it my way. Query planning is hard. NP-hard, actually. Sometimes you can get away with a simpler less general solution, but it will bite you sooner than you expect.
`WHERE, GROUP BY and ORDER BY` are relatively straightforward to tack on into the variable ordering.
Having written a sat solver would kinda help you, because modern sat solving algorithms are fundamentally very similar to worst case optimal join algorithms.
The query plan produced by combining binary joins is always going to be off by up to an exponential factor when compared to a WCO join. If you're fine throwing all that complexity onto your problem to generate sub-par query plans, be my guest.
Citation greatly needed. Why is it so?
See:
https://www.cs.stanford.edu/people/chrismre/papers/paper49.N...
https://justinjaffray.com/a-gentle-ish-introduction-to-worst...
The difference is not exponential, if I may nitpick. It is sublinear in the case of the "triangles example" - WCO would produce O(n^(1.5)), binary join will produce O(n^2), the difference is O(n^(0.5)) or O(sqrt(n)). The difference is big, but not exponential.
Good nitpick, I think you can construct larger rings where the difference becomes larger than one, but I might be wrong. The dynamic variable ordering trick makes a huge difference in practice, especially when skew is in play. (https://arxiv.org/pdf/1310.3314.pdf)
In general you're right, WCO joins are a relatively young field of study and sometimes struggle with large constant factors, but they are maturing quickly and in a limited (triple) setting like the one OP "wished for", a lot more feasible than for the general case.
Thanks for the interesting discussions, looking forward to reading the references you provided in depth!
Edit: I just remembered this paper, which might be of interest to you. They seem to recover WCO bounds in a pairwise setting, by choosing very smart intermediary join representations: http://www.cs.ox.ac.uk/dan.olteanu/papers/co-tr16.pdf
My old idea was to perform planning after some of the work has been done, because remaining statistics can be different. These papers are of great help!
There are at least two different types of them, conflict-derived clause learning and variants and stochastic search and variants.
Which one is more similar to WCO join algorithm?
Tseitin transformation prevents exponential expansion in conversion from DNF to CNF.
The fact that paper's algorithm employs disjunctive normal form suggests the use of binary decision diagrams. ROBDDs represent DNFs naturally, for one example. ZDDs represent sets of sets and were relatively successfully used in non-trivial approaches to the SAT solving problem like [1].
[1] https://web.eecs.umich.edu/~imarkov/pubs/book/b002.pdf
Also, the paper you mentioned has this right in abstract: "However, there is still the quest of making the new worst-case optimal join algorithms truly practical in terms of (1) ease of implementation and (2) secondary index efficiency in terms of number of indexes created to answer a query."
WCO is hard to implement and it might be computation-wise prohibitive.
Yet, it's quite interesting, thank you very much.
Until the day you load a bunch of new data and it gets skewed, or you delete a bunch of data without shrinking and oops, your join order and method is not efficient anymore.
You can have this today by running XTDB[1] on top of SQLite via JDBC.
> Written in something like C, Rust, or Zig.
And then compiling your application into native executables with GraalVM Native Image[2].
Embeddable, check. Datomic data model and Datalog query, check. Storage written in C, check.
Edit: it's written in Clojure, so JVM. Extra bleh
No longer actively maintained, but maybe a nice starting point for hacking on your dream!
qpdb/mentat [2] seems to be the largest (+131 commits) and most recently modified (May this year) fork of mozilla/mentat.
[0]: https://github.com/mozilla/mentat/network/members - Seriously, how am I supposed to use this? Hundreds of entries, but no counts for stars, contributors, or commits, no details about recent commits. Just click every one?
The obvious issue is that they're fairly deeply embedded in the Clojure(Script) ecosystem.
You can see me prototype here: https://git.sr.ht/~chiefnoah/quark
I think I gave up when trying to implement one-many relationships because the SQL was getting too gnarly.