Re: Too few rows expected by Planner on partitioned tables

On Tue, Jul 21, 2020 at 01:09:22PM +0000, Julian Wolf wrote:
> Hello,
>
> A description of what you are trying to achieve and what results you expect:
> Our database is growing on a daily basis by about 2.5million rows per table (2 at the moment). Because of that, we decided to partition the data, especially, as we are pre-aggregating the data for weeks, months, quarters and years. Every aggregation is stored in a separate partition:
>
...
> Our problem is, that the planner always predicts one row to be returned, although only a part of the primary key is queried. This problem exceeds feasibility of performance rapidly - a query only involving a few days already takes dozens of seconds. All tables are analyzed and pg_stats looks reasonable IMHO.
...
> PRIMARY KEY ( daterange, spatial_feature_id, visitor_profile_id, activity_type_combination_id,
> activity_chain_id),
...
> ) PARTITION BY LIST (daterange);

> schemaname relname n_live_tup
> mobility_insights location_statistics_y2019m03d 23569853
> mobility_insights location_statistics_y2019m03w 19264373
> mobility_insights location_statistics_y2019m03 18105295

> Aggregate (cost=2.79..2.80 rows=1 width=8) (actual time=143.073..143.073 rows=1 loops=1)
> Buffers: shared hit=67334
> -> Index Scan using location_statistics_y2019m03w_pkey on location_statistics_y2019m03w st (cost=0.56..2.78 rows=1 width=8) (actual time=0.026..117.284 rows=516277 loops=1)
> Index Cond: ((daterange = '[2019-03-04,2019-03-11)'::daterange) AND (spatial_feature_id = 12675))
> Buffers: shared hit=67334

I guess this isn't actually the problem query, since it takes 143ms and not
dozens of seconds. I don't know what is the problem query, but maybe it might
help to create an new index on spatial_feature_id, which could be scanned
rather than scanning the unique index.

Also, if daterange *and* spatial_feature_id are always *both* included, then
this might work:

postgres=# CREATE STATISTICS t_stats (mcv) ON daterange,spatial_feature_id FROM t ; ANALYZE t;

--
Justin