Re: Statistics and selectivity estimation for ranges
| От | Alexander Korotkov |
|---|---|
| Тема | Re: Statistics and selectivity estimation for ranges |
| Дата | |
| Msg-id | CAPpHfdudkYhJkGbhQtbjt6GG5Owp_v7_kpQKA=1Dz5Fb8iGteA@mail.gmail.com обсуждение исходный текст |
| Ответ на | Re: Statistics and selectivity estimation for ranges (Alexander Korotkov <aekorotkov@gmail.com>) |
| Ответы |
Re: Statistics and selectivity estimation for ranges
|
| Список | pgsql-hackers |
On Mon, Aug 13, 2012 at 1:11 AM, Alexander Korotkov <aekorotkov@gmail.com> wrote:
Empty result sets
------
With best regards,
Alexander Korotkov.
On Thu, Aug 9, 2012 at 12:44 AM, Alexander Korotkov <aekorotkov@gmail.com> wrote:My conclusion is so, that current errors are probably ok for selectivity estimation. But taking into attention that generated datasets ideally fits assumptions of estimation, there could be room for improvement. Especially, it's unclear why estimate for "<@" and "@>" have much greater error than estimate for "&&". Possibly, it's caused by some bugs.ITSM, I found reason of inaccuracy. Implementation of linear interpolation was wrong. Fixed version is attached. Now, need to rerun tests, possible refactoring and comments rework.
After fixing few more bugs, I've a version with much more reasonable accuracy.
Statistics target = 100.
Relatively large result sets (>= 10)
test=# select operator, avg(estimate_count::float8/actual_count::float8) as avg_ratio, avg(exp(abs(ln(estimate_count::float8/actual_count::float8)))) - 1.0 as avg_error from datasets d join test_results tr on tr.test_id = d.id where d.stat_target = 100 and actual_count >= 10 group by operator;
operator | avg_ratio | avg_error
----------+------------------+--------------------
<@ | 1.00404179116863 | 0.0504415454560903
@> | 1.06364108531688 | 0.105646077989812
&& | 1.00757984721409 | 0.0420984234933233
(3 rows)
Small result sets (1 - 9)
test=# select operator, avg(estimate_count::float8/actual_count::float8) as avg_ratio, avg(exp(abs(ln(estimate_count::float8/actual_count::float8)))) - 1.0 as avg_error from datasets d join test_results tr on tr.test_id = d.id where d.stat_target = 100 and actual_count between 1 and 9 group by operator;
operator | avg_ratio | avg_error
----------+------------------+-------------------
<@ | 1.31530838062865 | 0.654886592410495
@> | 2.78708078320147 | 1.94124123003433
&& | 1.93268112525538 | 1.09904919063335
(3 rows)
test=# select operator, avg(estimate_count) as avg_estimate, count(*) as tests_count from datasets d join test_results tr on tr.test_id = d.id where d.stat_target = 100 and actual_count = 0 group by operator;
operator | avg_estimate | tests_count
----------+--------------------+-------------
<@ | 1.1437670609645132 | 1099
@> | 1.0479430126460701 | 87458
(2 rows)
Statistics target = 1000.
Relatively large result sets (>= 10)
test=# select operator, avg(estimate_count::float8/actual_count::float8) as avg_ratio, avg(exp(abs(ln(estimate_count::float8/actual_count::float8)))) - 1.0 as avg_error from datasets d join test_results tr on tr.test_id = d.id where d.stat_target = 1000 and actual_count >= 10 group by operator;
operator | avg_ratio | avg_error
----------+------------------+--------------------
<@ | 1.00073999445381 | 0.045099762607524
@> | 1.05296320350853 | 0.0907489633452971
&& | 1.00217602359039 | 0.0353421159150165
(3 rows)
Small result sets (1 - 9)
test=# select operator, avg(estimate_count::float8/actual_count::float8) as avg_ratio, avg(exp(abs(ln(estimate_count::float8/actual_count::float8)))) - 1.0 as avg_error from datasets d join test_results tr on tr.test_id = d.id where d.stat_target = 1000 and actual_count between 1 and 9 group by operator;
operator | avg_ratio | avg_error
----------+------------------+-------------------
<@ | 1.26946358795998 | 0.577803898836364
@> | 2.69000633430211 | 1.83165424646645
&& | 1.48715184186882 | 0.577998652291105
(3 rows)
Empty result sets
test=# select operator, avg(estimate_count) as avg_estimate, count(*) as tests_count from datasets d join test_results tr on tr.test_id = d.id where d.stat_target = 1000 and actual_count = 0 group by operator;
operator | avg_estimate | tests_count
----------+--------------------+-------------
<@ | 1.0887096774193548 | 1364
@> | 1.0423876983771183 | 89224
&& | 5.0000000000000000 | 1
(3 rows)
With best regards,
Alexander Korotkov.
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