In my recent blog post I remarked that database benchmarking is in a crisis because AI agents start to overfit on individual benchmarks with questionable techniques, which drives execution times towards zero. This has happened with TPC-D in the past, and broke the benchmark. TPC-H tries to fight that with rules, but most people who publish (unofficial) TPC-H results simply ignore the rules. In combination with overly eager AI agents, this leads to brutal overfitting.

We are seeing the next level of that with the GenDB-V system. The corresponding paper commands “Stop Handcrafting Your Engine! GenDB-V: Towards AI-Generated and Formally Verified Database Systems”, and reports speedups of 22× to 61× over Umbra and DuckDB, and all that in a fully verified system. These are indeed impressive claims, and would suggest that traditional database engines are in trouble.

Taking a step back, though, it should be clear that a performance improvement of a factor of 22 over Umbra is infeasible, at least if we are talking about traditional query processing. At SF10, the 22 TPC-H queries read roughly 15GB of (compressed) base data, and on a 9950X3D Umbra runs all queries in a total of roughly 540ms. On that machine the memory touched for data divided by the memory bandwidth (54GB/s) alone is about 280ms; anything below that is infeasible. Better compression might bring that down a bit, but not by an order of magnitude.

Unsurprisingly, the most dramatic gains of GenDB-V come from precomputation. The agents have carefully handcrafted auxiliary data structures for each query, which are computed at load time, and which avoid most of the work. The physical design is heavily tuned to the queries, too, for example, there is an extra structure that lists the supplier rows in s_acctbal DESC order, which makes Q2 trivial. Similar per-query data structures are built for most queries. And we see that in the performance numbers: Several queries already run in near-constant time at SF100: Q2 in 0.7 ms, Q17 in about 15 ms and Q4 in about 17 ms on one thread. That is the TPC-D symptom, without stored aggregates.

There is also a “plain-array” mode, which, at first glance, is more fair, as it avoids these excessive preparations and also reports performance improvements of just roughly a factor of 3, which seems achievable. But when looking closely, we see that here the agents did something even more insane: They hard-coded their knowledge about the data generator. They “know” that each customer k will be at row k-1. And they know that dbgen numbers the i-th order 32·(i/8) + i%8, so key k should be at row 8·(k/32) + k%32 − 1. Which makes the join in query Q18 super cheap.

How does all this fit together with the claim of a fully verified database engine? Well, the code does check if the hard-coded knowledge about the data generator holds in practice, e.g., by checking if the guessed join partner row is correct. If not, it falls back to a super slow generic nested loop implementation, but as that never happens with the official TPC-H data generator, that code path is irrelevant in the reported experiments.

Which brings me back to my original blog post claim: Letting AI agents optimize for a given database benchmark makes the benchmark useless. They will do their best to find insane tricks to speed up this particular benchmark, but none of that would work in a real system. The high tuning and synthesis time aside, deleting a single tuple (or inserting one in the middle) can most likely cause the whole approach to fall off a cliff, as it forces the slow fallback path. Which is not something you want in a production system.