I have the same schema in two different databases. In "smalldb", the two tables of
interest have about 430,000 rows, in "bigdb", the two tables each contain about 5.5
million rows. I'm processing the data, and for various reasons it works out well to process it in
100,000 row chunks. However, it turns out for the big schema, selecting 100,000 rows is the
longest single step of the processing.
Below is the explain/analyze output of the query from each database. Since
both tables are indexed on the joined columns, I don't understand why the big
table should be so much slower -- I hoped this would scale well, or at least
O(log(N)), not O(N).
What's going on here? I don't know if I'm reading this right, but it looks
like the sort is taking all the time, but that doesn't make sense because in
both cases it's sorting 100,000 rows.
Thanks,
Craig
bigdb=> explain analyze
bigdb-> select r.row_num, m.molkeys from my_rownum r
bigdb-> join my_molkeys m on (r.version_id = m.version_id)
bigdb-> where r.row_num >= 100000 AND r.row_num < 200000
bigdb-> order by r.row_num;
Sort (cost=431000.85..431248.23 rows=98951 width=363) (actual
time=46306.748..46417.448 rows=100000 loops=1)
Sort Key: r.row_num
-> Hash Join (cost=2583.59..422790.68 rows=98951 width=363) (actual
time=469.010..45752.131 rows=100000 loops=1)
Hash Cond: ("outer".version_id = "inner".version_id)
-> Seq Scan on my_molkeys m (cost=0.00..323448.30 rows=5472530
width=363) (actual time=11.243..33299.933 rows=5472532 loops=1)
-> Hash (cost=2336.21..2336.21 rows=98951 width=8) (actual
time=442.260..442.260 rows=100000 loops=1)
-> Index Scan using i_chm_rownum_row_num on my_rownum r
(cost=0.00..2336.21 rows=98951 width=8) (actual time=47.551..278.736 rows=100000
loops=1)
Index Cond: ((row_num >= 100000) AND (row_num < 200000))
Total runtime: 46543.163 ms
smalldb=> explain analyze
smalldb-> select r.row_num, m.molkeys from my_rownum r
smalldb-> join my_molkeys m on (r.version_id = m.version_id)
smalldb-> where r.row_num >= 100000 AND r.row_num < 200000
smalldb-> order by r.row_num;
Sort (cost=43598.23..43853.38 rows=102059 width=295) (actual
time=4097.180..4207.733 rows=100000 loops=1)
Sort Key: r.row_num
-> Hash Join (cost=2665.09..35107.41 rows=102059 width=295) (actual
time=411.635..3629.756 rows=100000 loops=1)
Hash Cond: ("outer".version_id = "inner".version_id)
-> Seq Scan on my_molkeys m (cost=0.00..23378.90 rows=459590
width=295) (actual time=8.563..2011.455 rows=459590 loops=1)
-> Hash (cost=2409.95..2409.95 rows=102059 width=8) (actual
time=402.867..402.867 rows=100000 loops=1)
-> Index Scan using i_chm_rownum_row_num_8525 on my_rownum r
(cost=0.00..2409.95 rows=102059 width=8) (actual time=37.122..242.528 rows=100000
loops=1)
Index Cond: ((row_num >= 100000) AND (row_num < 200000))
Total runtime: 4333.501 ms
Table "bigdb.my_rownum"
Column | Type | Modifiers
------------+---------+-----------
version_id | integer |
parent_id | integer |
row_num | integer |
Indexes:
"i_chm_rownum_row_num" UNIQUE, btree (row_num)
"i_chm_rownum_version_id" UNIQUE, btree (version_id)
"i_chm_rownum_parent_id" btree (parent_id)
Table "bigdb.my_molkeys"
Column | Type | Modifiers
------------+---------+-----------
version_id | integer |
molkeys | text |
Indexes:
"i_chm_molkeys_version_id" UNIQUE, btree (version_id)
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