On 10/21/20 5:35 PM, Sebastian Dressler wrote:
Hi Mats,
Happy to help.
On 21. Oct 2020, at 16:42, Mats Olsen <m...@duneanalytics.com
<mailto:m...@duneanalytics.com>> wrote:
On 10/21/20 2:38 PM, Sebastian Dressler wrote:
Hi Mats,
On 20. Oct 2020, at 11:37, Mats Julian Olsen
<m...@duneanalytics.com <mailto:m...@duneanalytics.com>> wrote:
[...]
1) Vanilla plan (16 min) : https://explain.depesz.com/s/NvDR
<https://explain.depesz.com/s/NvDR>
2) enable_nestloop=off (4 min): https://explain.depesz.com/s/buKK
<https://explain.depesz.com/s/buKK>
3) enable_nestloop=off; enable_seqscan=off (2 min):
https://explain.depesz.com/s/0WXx <https://explain.depesz.com/s/0WXx>
How can I get Postgres not to loop over 12M rows?
I looked at the plans and your config and there are some thoughts
I'm having:
- The row estimate is off, as you possibly noticed. This can be
possibly solved by raising `default_statistics_target` to e.g. 2500
(we typically use that) and run ANALYZE
I've `set default_statistics_target=2500` and ran analyze on both
tables involved, unfortunately the plan is the same. The columns we
use for joining here are hashes and we expect very few duplicates in
the tables. Hence I think extended statistics (storing most common
values and histogram bounds) aren't useful for this kind of data.
Would you say the same thing?
Yes, that looks like a given in this case.
- I however think that the misestimate might be caused by the
evt_tx_hash being of type bytea. I believe that PG cannot estimate
this very well for JOINs and will rather pick row numbers too low.
Hence the nested loop is picked and there might be no way around
this. I have experienced similar things when applying JOINs on
VARCHAR with e.g. more than 3 fields for comparison.
This is very interesting, and I have never heard of issues with using
`bytea` for joins. Our entire database is filled with them, as we
deal with hashes of different lengths. In fact I would estimate that
60% of columns are bytea's. My intuition would say that it's better
to store the hashes as byte arrays, rather than `text` fields as you
can compare the raw bytes directly without encoding first? Do you
have any references for this?
Unfortunately, I have not dealt yet with `bytea` that much. It just
rang a bell when I saw these kind of off-estimates in combination with
nested loops. In the case I referenced it was, that the tables had 3
VARCHAR columns to be joined on and the estimate was very much off. As
a result, PG chose nested loops in the upper layers of processing. Due
to another JOIN the estimate went down to 1 row whereas it was 1
million rows in reality. Now, yours is "only" a factor 5 away, i.e.
this might be a totally different reason.
However, I looked into the plan once more and realized, that the
source of the problem could also be the scan on "Pair_evt_Mint" along
the date dimension. Although you have a stats target of 10k there. If
the timestamp is (roughly) sorted, you could try adding a BRIN index
and by that maybe get a better estimate & scan-time.
Hi again, after around 48 hours a CREATE INDEX CONCURRENTLY ran
successfully. The new plan still uses a nested loop, but the scan on
"Pair_evt_Mint" is now a Parallel index scan. See
https://explain.depesz.com/s/8ZzT
Alternatively, since I know the length of the hashes in advance, I
could've used `varchar(n)`, but I don't think there's any gains to be
had in postgres by doing that? Something like `bytea(n)` would also
have been interesting, had postgres been able to exploit that
information.
I think giving VARCHAR a shot makes sense, maybe on an experimental
basis to see whether the estimates get better. Maybe PG can then
estimate that there are (almost) no dupes within the table but that
there are N-many across tables. Another option to explore is maybe to
use UUID as a type. As said above, it more looks like the timestamp
causing the mis-estimate.
Maybe try querying this table by itself with that timestamp to see
what kind of estimate you get?
- Other things to look into:
- work_mem seems too low to me with 56MB, consider raising this
to the GB range to avoid disk-based operations
- min_parallel_table_scan_size - try 0
- parallel_setup_cost (default 1000, maybe try 500)
- parallel_tuple_cost (default 1.0, maybe try 0.1)
- random_page_cost (as mentioned consider raising this maybe
much higher, factor 10 or sth like this) or (typically)
seq_page_cost can be possibly much lower (0.1, 0.01) depending on
your storage
I've tried various settings of these parameters now, and
unfortunately the only parameter that alters the query plan is the
last one (random_page_cost), which also has the side effect of
(almost) forcing sequential scans for most queries as far as I
understand? Our storage is Google Cloud pd-ssd.
I think a combination of random_page_cost with parallel_tuple_cost and
min_parallel_table_scan_size might make sense. By that you possibly
get at least parallel sequential scans. But I understand that this is
possibly having the same effect as using `enable_nestloop = off`.
I'll have a closer look at these parameters.
Again, thank you.
Mats
