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Comment by davidrowley | Hacker News Reader

There is a pg_hint_plan extension. I think the danger with hints is that they might only be correct when written. If the table sizes or data skew changes, they might make things worse. I don't have a link to hand, but last time I recall a discussion on hints there was no general objection to them, providing the implementation could be done in a way that didn't force the planner's hand too strongly and still allowed it to adapt to the underlying data changing. For example, indicating there's a correlation between two columns, rather than specifying a given predicate matches 10 rows.

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>I think the danger with hints is that they might only be correct when written.

Not "correct when written", but "scaling as written". That means if you force the execution that scales linearly or quadratically, that's what you get all the time. If the row number increases, you know what will happen. You can monitor that ahead of time and plan for the increase.

On the other hand without the hints, you don't know when and how the plan will change without testing. At some random point postgres can decide to do something terribly stupid and at that point you get to figure out what happened and how to fix that in an emergency mode. Do you know how to adjust the right statistics? Do you need to change the indexes? Do you know how long that will take?

I had this happen for the first time to some prod jobs the other day in spark. We made a pretty normal update to a join with an additional condition, our integration tests which run local Spark succeeded. But something about it running on the cluster… it was generating a completely different query plan than it ran locally. We eventually had to rewrite the whole query to work around it because it was trying to broadcast a 3TB table and couldn’t be talked out of it.

There certainly are valid reasons for this. For example, adding a join condition with an OR clause. The only join operator that supports non-equi joins is Nested Loop. If you went from a Hash or Merge join to that, then you'd likely notice some performance degradation. If you have a link to anywhere you've asked for help on this, then I'd be interested to see more details.

I see you’re definitely familiar with the space. I think the condition was using ‘or array_contains’ in the join.

This was really the only resource I found acknowledging it. It sounds like it has do with presumption of nulls (e.g. spark can’t assume they won’t be there) but it would be great to be able to say “don’t worry spark I promise there are no nulls/if there are just disregard”) https://kb.databricks.com/sql/disable-broadcast-when-broadca...

The way we go around this feels so brutish. Literally just did two separate joins and then unioned the results. The recommendation to use ‘not exists’ couldn’t be applied as array_contains must be using ‘in’ under the hood and couldn’t be changed.

UNION is certainly one way to eliminate the OR condition.

In theory, a hash join is possible with a condition like `ON t1.a = t2.a OR t1.b = t2.b`, but Hash Join would need to build two hash tables and only probe the 2nd one if the first lookup found nothing. I don't know if there are any RDBMSs that allow multiple hash tables in a hash join.

Yeah it feels like for some arbitrarily sized array contains join having something like a bloom filter involved could help reduce the search space. But I’m not a db engineer so perhaps the specific implementation details of that would turn out to make it a bad idea.

We don't use PostgresSQL (yet), but have had issues with random production outages due to planner suddenly deciding to do dumb stuff like full table scans instead of using an index.

To avoid having to sprinkle hints all over, we've added a background job that forces recalculation of statistics once a week or so...

> I think the danger with hints is that they might only be correct when written. If the table sizes or data skew changes, they might make things worse.

they will work in prod in the way engineer is expecting. Current planner also can change its mood in unpredictable way and often generates sub-optimal plans for complex queries, because can't reason about what specific subquery will return exactly, and you learn about it when queries start work very slow in production in the middle of the night.

(Postgres committer and blog author here) Personally, I don't have any objection to hints. The resolution of any statistics is never going to be high enough to always be accurate enough for all cases. I think it would be good to give DBAs a better way to coax the planner into making or not making a certain decision. It would also be nice if the planner was a little more risk-averse. Currently, it's happy to do things like Nested Loop join because it thinks some complex WHERE clause will only match 1 row. Nested Loop works best for that, but if there are 2 rows, then generally, any other join type is better, especially so when the inner side of the join is expensive.

One way to look at this is that the most accurate way to "estimate" how fast a certain plan would run, is to actually run it on the full dataset. But that obviously doesn't make sense, as the optimizer is expected to come up with a plan in matter of milliseconds (or less for simple queries) and you don't want your "optimizer stats" to be as big as the whole dataset itself. So optimizer has limited information, by design, and it has to come up with _something_ in a very short amount of time.

I don't know much about Postgres optimizer, but I imagine that in addition to table/column stats, is also uses structural info as its inputs, like existence of (enabled & valid) constraints for example. If the optimizer knows that some column never has NULLs or is guaranteed to be unique, all kinds of transformation & shortcuts become possible.

(There are plenty of large big-vendor ERP/CRM/etc apps out there that do not use DB constraints for the sake of "portability"... not fun to work with these).

> you don't want your "optimizer stats" to be as big as the whole dataset itself. So optimizer has limited information, by design, and it has to come up with _something_ in a very short amount of time.

This is very true. PostgreSQL does not do any proactive plan caching, so it's important that the planner remains fast. It is possible to adjust the number of stats targets to control the size of the histograms and most common values list. Upping that can be useful for OLAP-type workloads.

> I imagine that in addition to table/column stats, is also uses structural info as its inputs, like existence of (enabled & valid) constraints for example.

Yes. Foreign key constraints are used to assist with join selectivity estimations. PG17 (when released) should be able to make more use of NOT NULL constraints to improve plans.

> I don't know much about Postgres optimizer, but I imagine that in addition to table/column stats, is also uses structural info as its inputs, like existence of (enabled & valid) constraints for example.

Here's one that surprised me when I found out about it years ago, because I'd never really given it thought: There's a correlation statistic on columns for how well the values in that column match the row order on disk, which can influence a few different things.

In my case a query that retrieved a ton of data with an ORDER BY was using a sort and taking like two hours to run (a data source for an ETL process) - turned out because of a really bad correlation postgres was refusing to use the index, because the random access would be even slower, so it did a table scan then sort. After figuring this out and discovering the CLUSTER command (reorders the data on disk to match an index), it did an index scan and didn't need to sort at the end, was able to start streaming results immediately, and finished the entire query in like ten minutes.

Just a nice example of where the obvious "use query hints to make it use the index" would have been the worst option, instead figuring out why postgres didn't want to use it and fixing that resulted in something much better.

> Just a nice example of where the obvious "use query hints to make it use the index" would have been the worst option, instead figuring out why postgres didn't want to use it and fixing that resulted in something much better.

I come to the opposite conclusion. Clustering a table results in an access exclusive lock, and due to MVCC the ordering isn’t permanent.

Here, you as the engineer know you’d like to use a sorted index to stream results out even if the overall query end to end is slower due to the I/O cost. In my opinion there should be a way to express this within the query.

I did test that and like I said, it was strictly worse - went from something like a 2 hour runtime to 5+ hours. Random disk access and not being able to take advantage of the disk cache really is that bad.

Streaming results doesn't mean anything if the total runtime is that much worse, it just means the overall system will take hours longer to complete. And the good version doesn't need to continuously run CLUSTER, the correlation just has to be high enough for it to be the better choice, so we settled on running it once a week - only takes like 2 minutes to run the CLUSTER.

You should be thinking of it the other way around: my final 10 minute result was the ideal situation I wanted, but when circumstances were bad for it, the postgres query planner was smart enough to tell it wouldn't work and switch to the 2-hour plan instead of blindly following the original plan and taking 5 hours.

It would be neat if you could at least provide expressions (that can't hit any actual tables) to compute bounds for how many rows are expected to come back from any particular row source.

I think Oracle style hints are not a good thing to have - especially because you have to change the query itself which sometimes isn't possible in a production environment. Additionally, for me they quite frequently made things worse after minor Oracle upgrades.

I would prefer having "externally attached" hints for a query (e.g. identified by it's queryid) like Oracle's stored outlines.

I do wonder if one could eventually just turn off nestloops in such a case (e.g. inner side contains a seqscan), like the JOB paper recommended. Yes, it will have marginally higher estimated cost, but the upside is _much_ safer query plans when the statistics are off.

That could be useful if there was a way to just disable non-parameterized nested loop, however enable_nestloop=0 also disables parameterized nested loops. Parameterized nested loops are useful to avoid sorting or hashing some large relation when only a small subset of that relation is likely to have a join partner. This is even more true when you consider that since PG14, Memoize exists to act as a cache between Nested Loop and its inner subnode to cache previously looked-up values.

It's also important to consider that with enable_nestloop=0, when Nested Loop must be used (e.g for a CROSS JOIN) that the cost penalty that's added to reduce the chances of Nested Loop being used can dilute the costs so much that the query planner can then go on to make poor subsequent choices later in planning due to the costs for each method of implementing the subsequent operation being so relatively close to each other than the slightly cheaper one might not even be considered. See add_path() and STD_FUZZ_FACTOR. So, running enable_nestloop=0 in production is not without risk.

Yeah, nestloop with a cheap inner path (e.g. a lookup into a unique index) should be just fine, so I don't think nestloops as a whole should be banned. (Also, I believe Postgres is pretty much the only place I've seen the concept of a parameterized path described; it's not talked much about in academia, although it is probably really hard to make an index-aware System R planner without it.)

I wondered whether it would be possible just to add a fixed fuzz to every row estimate, say five rows. It would essentially mean you can never get this issue of a small undercount causing a plan disaster. Overestimating slightly is basically never a big issue as far as I know.

(I should perhaps have considered this when I was actually making a query planner in a previous life, but there were more than enough other things to worry about :-) )

It's a bit complex to explain here, but I describe an idea I've been considering in https://www.postgresql.org/message-id/CAApHDvo2sMPF9m=i+YPPU...

Yes, a lot of this seems interesting to go into. I really hope that at some point, someone would find the resources to just try a lot of dumb stuff and see what works in practice. I mean, what we have right now (multiply selectivities together as if they were independent) is also pretty dumb, and there's no good reason why it should be preferred over everything else.

Another avenue is of course trying to avoid the issue to begin with, e.g. through the recent “translation grids” of Müller and Moerkotte for better join selectivities. But I doubt anyone is going to be finding a silver bullet for this anytime soon, so reducing plan risk somehow seems very worthwhile.

> I mean, what we have right now (multiply selectivities together as if they were independent) is also pretty dumb

Yeah, I think it was probably a mistake to always assume there's zero correlation between columns, but what value is better to use as a default? At least extended statistics allows the correlations of multiple columns to be gathered now. That probably means we'd be less likely to reconsider changing the default assumption of zero correlation when multiplying selectivities.

Yeah, I don't have all the answers, my point is that it would be worthwhile to try similarly dumb stuff and see if it works just as well and could be more robust in some places :-)

Multi-selectivities are good, but IIRC they can't be specified across tables and thus across joins, right? Out of curiosity; how do you reconcile multiple selectivities? If you have a multi-column histogram on (a,b) and one on (b,c) and one on (c), and you need to figure out the selectivity of WHERE a=? AND b=? AND c=? from those three? I looked into this at some point, and academia presented me with a nightmare of second-order cone programming and stuff. :-) (I never implemented any of it before leaving the database world.)

> Multi-selectivities are good, but IIRC they can't be specified across tables and thus across joins, right?

Yeah, no extended statistics for join quals yet.

> how do you reconcile multiple selectivities?

Looking at https://doxygen.postgresql.org/extended__stats_8c.html#a3f10... it seems the aim is to find the stats that cover the largest number of clauses tiebreaking on the statistics with the least number of keys. For your example both of those are the same, so it seems that which stats are applied is down to the order the stats appear in the stats list. That list is ordered by OID, which does not seem ideal as a dump and restore could result in the stats getting a different OID. Seems sorting that list by statistics name might be better. That's what we do for triggers, which seems like a good idea as it gives the user some ability to control the trigger fire order.

OK, so basically ad-hoc/greedy. That can get you into, well, issues :-) There are methods where you first adjust away impossible statistics (if you have quals a,b, then P(a)P(b) >= P(a AND b), but if your estimates come from multiple places this might not hold[1] -- so you need to fudge one or more of those before proceeding), and then combine them optimally using Newton-Raphson. It's pretty neat. But it does require a matrix library and stuff :-)

[1] Similarly, you can sometimes guarantee stuff like P(a AND b) <= 1/n due to a unique multi-column index, which can also inform some of your sub-estimates.

What is the JOB paper? A quick google search didn’t find it, but I’d be interesting to see the recommendations

https://www.vldb.org/pvldb/vol9/p204-leis.pdf

Indeed, hints are super-useful (essential!) for applying quick fixes when something unexpected suddenly happens in the optimizer's magic. Or you just want to instruct/nudge the optimizer towards doing the right thing, if you know the shape of your data and optimizer can't see it or doesn't act on it correctly for some reason. The downside is that people who don't really know what exactly they want to achieve, will start applying incomplete sets of hints in random locations, based on Internet searches. And sometimes you'd even get lucky, that single index hint makes the problem go away - for a while!

And few people tend to remove hints after DB version upgrades, where the optimizer magic (or your table stats) have improved. Now you're limiting optimizer's choices. That's been a problem (by now) for decades in the Oracle world with lots of legacy SQL code full of random hints where some of them aren't even valid anymore, but others still are - and limit optimizer's choices.

At least Oracle folks had enough at some point and introduced an "optimizer_ignore_hints" parameter [1], so all legacy hints that were added 20 years ago just get ignored - and the modern optimizer does a much better job getting things right.

I do regularly use hints in SQL tuning and troubleshooting experiments, just to verify and prove that a better plan is theoretically and physically possible - and if yes, then go from there. When the (deliberately placed) hints make the query faster, the next step is to check the hinted, faster plan's optimizer cost estimate and drill down from there: Why did optimizer think that the other plan was cheaper or why did the optimizer think that the faster plan was more expensive. Then you end up with measuring row-count misestimates, etc...

But yes, plenty of people (including myself) have made legacy Oracle apps run much more efficiently and faster just by globally telling the DB to stop paying attention to all the old random hints lingering on and gathering object stats using the modern settings & defaults (doesn't always work though).

[1] https://docs.oracle.com/en/database/oracle/oracle-database/1...

> At least Oracle folks had enough at some point and introduced an "optimizer_ignore_hints" parameter [1], so all legacy hints that were added 20 years ago just get ignored - and the modern optimizer does a much better job getting things right.

I think the general attitude in the Postgres community is been from a purist point of view. When you have a codebase around 40 years old, you do have to think carefully about what you put into it, as it might not be that easy to take it out again. However, yes, I do think hints would be useful for Postgres, providing they're done well. It would be good to at least have something to assist with selectivity estimations. Those are at least not directly forcing the planner into a single choice. New planner/executor smarts, such as something like Memoize added in PG14 could still be considered after upgrading an older pre-PG14 application with such hints, but perhaps not if the hint told the planner that it must nested loop join these two tables.

If not hints and manual plans then we need a way to "freeze" automatic optimizer plans however optimal they are after each tested release.

The optimizer killing your heavily-loaded production because it randomly switched a frequent query to a bad plan is pretty annoying. People mostly put up with this because for a single-instance DB you can easily maintain 4x spare CPU capacity but it's impossible at scale.

It sounds like it would have to be an opt-in feature which could be applied per query, as otherwise wouldn't it be equally as annoying if the planner didn't adapt to the table data changing?

What may be better is if the executor provided feedback to the planner to tell it the new plan was worse than the old one. With that, you might be able to recover much more quickly and less likely to get a midnight phonecall. The tricky part would be when should the planner then retry the new plan again. Also, other factors that influence the plan's execution time such as locking would be a cause of noise for any sub-system that was monitoring this.

> It sounds like it would have to be an opt-in feature which could be applied per query

That's called a hint :)

Not exactly because a hint contains information that may become obsolete. A directive that freezes a known good auto-plan until the next release will not become obsolete as long as you keep releases rolling.

What I’d like is the ability to hand postgres an execution plan, whether that is a plan I retrieved from Postgres’s own planner, or one I wrote by hand (possibly a mix of the two).

Give the API a scary name if you want, but getting 3 Am calls because the planner suddenly decided to go off the rails or seeing pages of convoluted SQL because in (current version) it’s what it takes to get the plan you need is not fun.

It’s long past time for database developers to accept that the high level is not always the right solution, it should be that in 95 or 99% of cases, and the better it is the better my life is, but sometimes you got to write exactly what you need.

Most language designers are fine with it and will allow either embedding or calling into lower-level language (down to hand-crafted assembly), it would be great if database devs could get on with that program.

Store hints and index definitions in same place and read from it for migrations. Problem solved in most practical scenarios.

> wouldn't it be equally as annoying if the planner didn't adapt to the table data changing?

Not really, because at scale tables don't change suddenly unless you release something -- and that will re-generate the plans after the perf testing.

Whatever changes slowly accumulate over time will be covered in the next release which normally should be very frequent, daily or more.

The one I'd love to tell the planner is that a table holds transactions in time, and that it should not expect that today's data is empty because it was empty 10 hours ago. It's an extremely common pattern, it makes any statistics gathering based on percentage of data changed dubious pretty quickly, and harms a whole lot of real queries, because in data like this, people care the most about the recent data.

There are ways to organize data to minimize the issue, but it'd be so much nicer if we could just teach the optimizer that this is the way the data is shaped.

> The one I'd love to tell the planner is that a table holds transactions in time, and that it should not expect that today's data is empty because it was empty 10 hours ago. It's an extremely common pattern, it makes any statistics gathering based on percentage of data changed dubious pretty quickly, and harms a whole lot of real queries, because in data like this, people care the most about the recent data.

It's not a hint, but PostgreSQL does have something that can help with cases like that.

In some cases, to obtain selectivity estimates, the planner will probe a btree index to find the actual lower and/or upper bound. For this to apply, a btree index must exist and you have to be using indexes >, >=, < or <= operator. The planner will probe the index if the query is comparing the indexed column to a value that's known the planner if that value falls on the first or last histogram bucket. This can help when your statistics are slightly out of date and you're querying for some column which stores a monotonically increasing or decreasing value.

I feel the best abstraction for hints would be to declare on tables how large you expect them to be -- and even throw errors if query plans with a good scaling cannot be found.

Say I could declare "assume this table will grow very large", "assume this table will be a small enum table".

And then it would use that information instead of actual table size to guide planning AND throw an error for any query doing a full table scan on a declared-to-be-large table -- so that missing indices can be detected instantly, not after running in prod for some days/weeks.

Google Data Store has this property and it is a joy to work with for a backend developer.

What I am usually after is NOT the fastest plan, but the most consistent and robust plan across test and prod environments.

Oh, that sounds really cool. I like declaring expected size, but "throw on certain behaviors" would be something I’d love in MS SQL.

Table row count is a small part of it, what matters is cardinality and fanout from joins after predicates have been pushed down as far as they can. (Assuming there are sane indexes.)

If the database can't see that your predicates will restrict the set of rows at a certain point in the join graph, it is likely to decide to join too much too early with huge table scans.

Bad join order and join strategy is at the heart of most bad plans once you already have indexes in place that cover the expected joins and lookups.

IMO hints need to be provided out of band, that is, not in the SQL query itself. To do this it is necessary to have a way to address every table source, in every sub-query, then one can have hints as a pile of {table source, hint}. Not that this solves the problem of hints rotting, but being able to separate them from the text of the query at least keeps the query clean, and makes is possible to have different sets of hints for different contexts and different RDBMS versions.

I guess that the solution to this problem can be automated. The DB or an extension to the DB or application code can run the query without hints sometimes and compare the result with the version with hints. If the hinted version is still faster, good. If it is slower, it's time to tell the DBA. Or switch to the unhinted query automatically if it's faster for a large enough number of times.

I suspect the ideological problem with hints is that if the planner is producing a poor query, then the correct place to fix that is in the planner.

While I agree with this viewpoint, The problem is that most people don't want to be a Postgress dev, To actually enable people to fix the planner it would have to be exposed as a runtime service. And unless there was a lot of diligence the planner script would quickly degrade into an unmaintainable mess(low blow: just like most schemas.)

I don't think anybody disagrees that the correct place to fix that is the planner, people just want an escape hatch so that when poor queries happen they can do _something_ instead of waiting for a fix to be written, a release to include it, and upgrading their database. It's a pretty reasonable ask, I think!

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