Re: BUG #16104: Invalid DSA Memory Alloc Request in Parallel Hash

On Fri, Nov 8, 2019 at 8:12 PM Thomas Munro <thomas.munro@gmail.com> wrote:
>
> On Sat, Nov 9, 2019 at 1:23 PM James Coleman <jtc331@gmail.com> wrote:
> > On Fri, Nov 8, 2019 at 6:30 PM Tomas Vondra <tomas.vondra@2ndquadrant.com> wrote:
> > > On Fri, Nov 08, 2019 at 09:52:16PM +0000, PG Bug reporting form wrote:
> > > >ERROR:  invalid DSA memory alloc request size 1375731712
>
> > > >#3  0x000055a7c079bd17 in ExecParallelHashJoinSetUpBatches
> > > >(hashtable=hashtable@entry=0x55a7c1db2740, nbatch=nbatch@entry=2097152) at
>
> > > I've briefly looked at this today, and I think the root cause is
> > > somewhat similar to what is described in [1] where we simply increase
> > > the number of batches in an effort to keep batch contents in work_mem,
> > > but ignoring that each batch requires quite a bit of memory. So we end
> > > up with a lot of batches where each is small enough to fit into
> > > work_mem, but we need much more than work_mem to track the batches.
>
> Yeah.  So even when this is fixed, the query is going to perform
> *terribly*, opening and closing millions of files in random order to
> stream tuples into, if this is case where there really are tuples to
> go to all partitions (and not just a case of extreme skew that our
> extreme skew detector fails to detect because it only detects absolute
> extreme skew).

work_mem in the repro case is 500MB (the original failure was at
150MB). I realize that's too small for this query, though it's also
worth knowing that if I get rid of some other cluster-wide tunings
that shouldn't have been cluster-wide original (modifications to
cpu_*_cost), the seq scan on a TB+ table feeding the hash turns into
an index scan and no hash (and performs much better).

I think this also correlates with us seeing ~TB spike in disk usage,
so your explanation of the lots of "small" files would seem to be
consistent with that.

> > > This seems to be about the same problem, except that instead of
> > > forgeting about BufFile, the parallel hash join ignores this:
> > >
> > >    pstate->batches =
> > >      dsa_allocate0(hashtable->area,
> > >                    EstimateParallelHashJoinBatch(hashtable) * nbatch);
>
> Yeah, I failed to consider that possibility.  I suppose it could be
> avoided with something like this (not tested, I will find a repro for
> this on Monday to convince myself that it's right):
>
> @@ -1246,7 +1246,10 @@
> ExecParallelHashIncreaseNumBatches(HashJoinTable hashtable)
>                                 }
>
>                                 /* Don't keep growing if it's not
> helping or we'd overflow. */
> -                               if (extreme_skew_detected ||
> hashtable->nbatch >= INT_MAX / 2)
> +                               if (extreme_skew_detected ||
> +                                       hashtable->nbatch >= INT_MAX / 2 ||
> +
> !AllocSizeIsValid(EstimateParallelHashJoinBatch(hashtable) *
> +
>    hashtable->nbatch * 2))
>                                         pstate->growth = PHJ_GROWTH_DISABLED;
>                                 else if (space_exhausted)
>                                         pstate->growth =
> PHJ_GROWTH_NEED_MORE_BATCHES;
>
> But James's query is still going to be terrible.
>
> Do you know if it's extreme skew (many tuples with the same key, just
> a few scattered around in other keys), or simply too much data for
> your work_mem setting?

Given my description earlier (seq scan on a very large table), I
assume it's likely the latter? If you think that's sufficiently
likely, I'll leave it at that, or if not I could do calculation on
that key to see how distributed it is.

James