[RMarcus]

I'm curious to hear a bit more of your opinion. For example, I'm surprised that syscall latency is something near the top of your list. I think the usual wisdom in the DB community is that the cost models are mostly fine, but the cardinality estimation is really bad.

In terms of the deferred / alternative planning, do you think adaptive query execution is a reasonable way to achieve this? It certainly allows for information early in the query execution to impact later plans. My worry with these approaches is that if you get the first couple of joins wrong (which is not uncommon), unless you have something like Yannakakis/SIPs, you still can't recover.

I am obviously biased on the whole "ML for query optimization" thing. One thing I would note is that every "ML for planning" approach I've seen does, under the hood, use ML for cost discovery/estimation. These approaches are just trying to balance the data they collect (exploration) with the quality of the plans they produce (exploitation). Interestingly, if you use ML in a way that is completely removed from planning, you actually get worse query plans despite more accurate estimates: https://people.csail.mit.edu/tatbul/publications/flowloss_vl... (again, I've got a horse in this race, so my opinion should come with a side of salt :D)

[aeyes]

As a DBA I have a very hard time properly tuning parameters like random_page_cost and the default of 4.0 is no longer applicable for most database servers.

I don't want to be tuning this, it takes a lot of time to do it properly and I haven't retested this in a long time. I just set something that has worked in the past which is probably bad.

I completely agree that Postgres should be able to figure this out on its own. This is just an example, there are more such parameters which should be adjusted to the hardware but most people will leave the defaults.

[darksaints]

Syscall latency was a much bigger deal back when we had spinning disks, but it still matters today (you can get dramatically different plans depending on slightly different costs of pulling a page from disk) and I find it silly that we've never even measured our those costs. A bigger impact might be for SQL functions...all of the Postgres SQL functions have configured cost values, but they could easily be measured. Also a simple cost model for functions can be a dramatic oversimplification. For example, some PostGIS functions have O(n) or O(n^x) behavior depending on the size and complexity of the input geometry. If we could measure exact costs, or model costs with statistical distributions, or possibly predict with ML, that would be a huge improvement.

My opinion on ML is that there is nothing in the execution planning side that couldn't be modeled and solved as a linear program, with extremely fast and mathematically-optimal results. By trying to use ML for the planning part, you're really just using ML to reverse engineer LP solvers, and it is a poor use of the compute resources.

The reason why some ML planners might have better results than typical SQL query planners is because typical SQL engines are optimized towards OLTP workloads that require small transactions executed very quickly. In order to do that, they purposefully don't explore the true planning space...they might explore 3-10 alternative ways of executing, whereas there might be hundreds or thousands of ways to do the same thing. While Postgres has explicitly chosen to not implement planning pragmas to override planner behavior, it would be really cool if you could have multiple planners optimized for different types of workloads, and be able to explicitly choose a query planner that takes 3 seconds to plan for a query that takes 1hr to execute and for which a better plan could save several minutes. I would even love a fairly naive query planner which does index scans for a deterministic and exact cardinality before planning joins.

BTW, I really like your blog and your research focuses. You're working on exceptionally hard problems that have a huge impact on computing.

[anarazel]

Where I think ML would be much better than what we (postgres) do, is iteratively improving selectivity estimation. In today's postgres there's zero feedback from noticing at runtime that the collected statistics lead to bad estimates. In a better world we'd use that knowledge to improve future selectivity estimates.

> In order to do that, they purposefully don't explore the true planning space...they might explore 3-10 alternative ways of executing, whereas there might be hundreds or thousands of ways to do the same thing.

FWIW, often postgres' planner explores many more plan shapes than that (although not as complete plans, different subproblems are compared on a cost basis).

> While Postgres has explicitly chosen to not implement planning pragmas to override planner behavior, it would be really cool if you could have multiple planners optimized for different types of workloads,

FWIW, it's fully customizable by extensions. There's a hook to take over planning, and that can still invoke postgres' normal planner if the query isn't applicable. Obviously that's not the same as actually providing pragmas.

[adgjlsfhk1]

My guess is that the reason for putting syscall latency high is that it should be easy to fix. Cardinality tracking is a hard problem, but running a loop on install that measures the cost of a couple dozen syscalls really could be done automatically.

[srcreigh]

Awesome paper! Might be my cue to delve into the world of cost estimates. My naive brain thinks, how can it be so hard :-)