Improve PostgresSQL aggregation query performance

Question

I am aggregating data from a Postgres table, the query is taking approx 2 seconds which I want to reduce to less than a second.

Please find below the execution details:

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Query

select
    a.search_keyword,
    hll_cardinality( hll_union_agg(a.users) ):: int as user_count,
    hll_cardinality( hll_union_agg(a.sessions) ):: int as session_count,
    sum(a.total) as keyword_count
from
    rollup_day a
where
    a.created_date between '2018-09-01' and '2019-09-30'
    and a.tenant_id = '62850a62-19ac-477d-9cd7-837f3d716885'
group by
    a.search_keyword
order by
    session_count desc
limit 100;

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Table metadata

1. Total number of rows - 506527
2. Composite Index on columns : tenant_id and created_date

---

Query plan

Custom Scan (cost=0.00..0.00 rows=0 width=0) (actual time=1722.685..1722.694 rows=100 loops=1)
  Task Count: 1
  Tasks Shown: All
  ->  Task
        Node: host=localhost port=5454 dbname=postgres
        ->  Limit  (cost=64250.24..64250.49 rows=100 width=42) (actual time=1783.087..1783.106 rows=100 loops=1)
              ->  Sort  (cost=64250.24..64558.81 rows=123430 width=42) (actual time=1783.085..1783.093 rows=100 loops=1)
                    Sort Key: ((hll_cardinality(hll_union_agg(sessions)))::integer) DESC
                    Sort Method: top-N heapsort  Memory: 33kB
                    ->  GroupAggregate  (cost=52933.89..59532.83 rows=123430 width=42) (actual time=905.502..1724.363 rows=212633 loops=1)
                          Group Key: search_keyword
                          ->  Sort  (cost=52933.89..53636.53 rows=281055 width=54) (actual time=905.483..1351.212 rows=280981 loops=1)
                                Sort Key: search_keyword
                                Sort Method: external merge  Disk: 18496kB
                                ->  Seq Scan on rollup_day a  (cost=0.00..17890.22 rows=281055 width=54) (actual time=29.720..112.161 rows=280981 loops=1)
                                      Filter: ((created_date >= '2018-09-01'::date) AND (created_date <= '2019-09-30'::date) AND (tenant_id = '62850a62-19ac-477d-9cd7-837f3d716885'::uuid))
                                      Rows Removed by Filter: 225546
            Planning Time: 0.129 ms
            Execution Time: 1786.222 ms
Planning Time: 0.103 ms
Execution Time: 1722.718 ms

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What I've tried

1. I've tried with indexes on tenant_id and created_date but as the data is huge so it's always doing sequence scan rather than an index scan for filters. I've read about it and found, the Postgres query engine switch to sequence scan if the data returned is > 5-10% of the total rows. Please follow the link for more reference.
2. I've increased the work_mem to 100MB but it only improved the performance a little bit.

Any help would be really appreciated.

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Update

Query plan after setting work_mem to 100MB

Custom Scan (cost=0.00..0.00 rows=0 width=0) (actual time=1375.926..1375.935 rows=100 loops=1)
  Task Count: 1
  Tasks Shown: All
  ->  Task
        Node: host=localhost port=5454 dbname=postgres
        ->  Limit  (cost=48348.85..48349.10 rows=100 width=42) (actual time=1307.072..1307.093 rows=100 loops=1)
              ->  Sort  (cost=48348.85..48633.55 rows=113880 width=42) (actual time=1307.071..1307.080 rows=100 loops=1)
                    Sort Key: (sum(total)) DESC
                    Sort Method: top-N heapsort  Memory: 35kB
                    ->  GroupAggregate  (cost=38285.79..43996.44 rows=113880 width=42) (actual time=941.504..1261.177 rows=172945 loops=1)
                          Group Key: search_keyword
                          ->  Sort  (cost=38285.79..38858.52 rows=229092 width=54) (actual time=941.484..963.061 rows=227261 loops=1)
                                Sort Key: search_keyword
                                Sort Method: quicksort  Memory: 32982kB
                                ->  Seq Scan on rollup_day_104290 a  (cost=0.00..17890.22 rows=229092 width=54) (actual time=38.803..104.350 rows=227261 loops=1)
                                      Filter: ((created_date >= '2019-01-01'::date) AND (created_date <= '2019-12-30'::date) AND (tenant_id = '62850a62-19ac-477d-9cd7-837f3d716885'::uuid))
                                      Rows Removed by Filter: 279266
            Planning Time: 0.131 ms
            Execution Time: 1308.814 ms
Planning Time: 0.112 ms
Execution Time: 1375.961 ms

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Update 2

After creating an index on created_date and increased work_mem to 120MB

create index date_idx on rollup_day(created_date);

The total number of rows is: 12,124,608

Query Plan is:

Custom Scan (cost=0.00..0.00 rows=0 width=0) (actual time=2635.530..2635.540 rows=100 loops=1)
  Task Count: 1
  Tasks Shown: All
  ->  Task
        Node: host=localhost port=9702 dbname=postgres
        ->  Limit  (cost=73545.19..73545.44 rows=100 width=51) (actual time=2755.849..2755.873 rows=100 loops=1)
              ->  Sort  (cost=73545.19..73911.25 rows=146424 width=51) (actual time=2755.847..2755.858 rows=100 loops=1)
                    Sort Key: (sum(total)) DESC
                    Sort Method: top-N heapsort  Memory: 35kB
                    ->  GroupAggregate  (cost=59173.97..67948.97 rows=146424 width=51) (actual time=2014.260..2670.732 rows=296537 loops=1)
                          Group Key: search_keyword
                          ->  Sort  (cost=59173.97..60196.85 rows=409152 width=55) (actual time=2013.885..2064.775 rows=410618 loops=1)
                                Sort Key: search_keyword
                                Sort Method: quicksort  Memory: 61381kB
                                ->  Index Scan using date_idx_102913 on rollup_day_102913 a  (cost=0.42..21036.35 rows=409152 width=55) (actual time=0.026..183.370 rows=410618 loops=1)
                                      Index Cond: ((created_date >= '2018-01-01'::date) AND (created_date <= '2018-12-31'::date))
                                      Filter: (tenant_id = '12850a62-19ac-477d-9cd7-837f3d716885'::uuid)
            Planning Time: 0.135 ms
            Execution Time: 2760.667 ms
Planning Time: 0.090 ms
Execution Time: 2635.568 ms

Answer 1

Have you tried a Covering indexes, so the optimizer will use the index, and not do a sequential scan ?

create index covering on rollup_day(tenant_id, created_date, search_keyword, users, sessions, total);

If Postgres 11

create index covering on rollup_day(tenant_id, created_date) INCLUDE (search_keyword, users, sessions, total);

But since you also do a sort/group by on search_keyword maybe :

create index covering on rollup_day(tenant_id, created_date, search_keyword);
create index covering on rollup_day(tenant_id, search_keyword, created_date);

Or :

create index covering on rollup_day(tenant_id, created_date, search_keyword) INCLUDE (users, sessions, total);
create index covering on rollup_day(tenant_id, search_keyword, created_date) INCLUDE (users, sessions, total);

One of these indexes should make the query faster. You should only add one of these indexes.

Even if it makes this query faster, having big indexes will/might make your write operations slower (especially HOT updates are not available on indexed columns). And you will use more storage.

• Idea came from here , there is also an hint about size for the work_mem
• Another example where the index was not used

Answer 2

You should experiment with higher settings of work_mem until you get an in-memory sort. Of course you can only be generous with memory if your machine has enough of it.

What would make your query way faster is if you store pre-aggregated data, either using a materialized view or a second table and a trigger on your original table that keeps the sums in the other table updated. I don't know if that is possible with your data, as I don't know what hll_cardinality and hll_union_agg are.