I have created a GIN index using jsonb_path_ops over some JSONB
columns. Initially, while the index was small, the query planner would
select a Bitmap Index Scan strategy to execute queries leveraging the
appropriate JSONB operator (@>). Now that the table has grown to
almost 200k rows and the index has grown to 1.6M rows, the query
planner prefers running Seq Scans.
db=> SELECT relname, relkind, reltuples, relpages FROM pg_class WHERE
relname IN ('_object', 'idx_object') and relnamespace = 43315;
relname | relkind | reltuples | relpages
------------+---------+------------+----------
_object | r | 185618 | 39030
idx_object | i | 1.6583e+06 | 512
(2 rows)
db=> explain analyze ...;
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------
Subquery Scan on x (cost=41814.28..41814.30 rows=1 width=25) (actual
time=3954.742..3954.820 rows=5 loops=1)
Filter: ((x._data IS NOT NULL) OR (x.metadata IS NOT NULL))
Rows Removed by Filter: 3
-> Unique (cost=41814.28..41814.29 rows=1 width=1562) (actual
time=3954.740..3954.814 rows=8 loops=1)
-> Sort (cost=41814.28..41814.28 rows=1 width=1562) (actual
time=3954.738..3954.754 rows=77 loops=1)
Sort Key: ...
Sort Method: quicksort Memory: 63kB
-> Seq Scan on _object (cost=0.00..41814.27 rows=1
width=1562) (actual time=84.980..3954.330 rows=77 loops=1)
Filter: ((... @> ...::jsonb) AND (... @> ...::jsonb))
Rows Removed by Filter: 185529
Planning time: 0.261 ms
Execution time: 3954.860 ms
(12 rows)
Disabling seq scans shows that the execution time of the Bitmap Index
Scan is considerably lower, but the cost estimate is completely off.
db=> explain analyze ....;
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------
Subquery Scan on x (cost=75584.03..75584.05 rows=1 width=25) (actual
time=24.591..24.664 rows=5 loops=1)
Filter: ((x._data IS NOT NULL) OR (x.metadata IS NOT NULL))
Rows Removed by Filter: 3
-> Unique (cost=75584.03..75584.04 rows=1 width=1562) (actual
time=24.589..24.659 rows=8 loops=1)
-> Sort (cost=75584.03..75584.03 rows=1 width=1562) (actual
time=24.588..24.603 rows=77 loops=1)
Sort Key:...
Sort Method: quicksort Memory: 63kB
-> Bitmap Heap Scan on _object
(cost=75580.00..75584.02 rows=1 width=1562) (actual
time=24.120..24.284 rows=77 loops=1)
Recheck Cond: ((... @> ...::jsonb) AND (... @> ...::jsonb))
Heap Blocks: exact=73
-> Bitmap Index Scan on idx_object
(cost=0.00..75580.00 rows=1 width=0) (actual time=24.094..24.094
rows=77 loops=1)
Index Cond: ((... @> ...::jsonb) AND (...
@> ...::jsonb))
Planning time: 0.301 ms
Execution time: 24.723 ms
(14 rows)
It would seem that this miscalculation of the cost of the index scan
is due to the query planner lacking detailed statistics about the
relevant JSONB column. Here are the statistics collected by postgresql
on the relevant columns.
db=> \x
db=> select attname, null_frac, n_distinct,
array_length(most_common_vals,1) mcv,
array_length(most_common_freqs,1) mcf,
array_length(most_common_elems,1) mce,
array_length(most_common_elem_freqs,1) mcef, elem_count_histogram from
pg_stats where schemaname = 'zero2' and tablename = '_object';
-[ RECORD 1 ]--------+----------
attname | doctype
null_frac | 0
n_distinct | 56
mcv | 4
mcf | 4
mce |
mcef |
elem_count_histogram |
-[ RECORD 2 ]--------+----------
attname | app_id
null_frac | 0
n_distinct | 2374
mcv | 100
mcf | 100
mce |
mcef |
elem_count_histogram |
-[ RECORD 3 ]--------+----------
attname | status
null_frac | 0
n_distinct | 2
mcv | 2
mcf | 2
mce |
mcef |
elem_count_histogram |
-[ RECORD 4 ]--------+----------
attname | metadata
null_frac | 0.0924333
n_distinct | 985
mcv | 100
mcf | 100
mce |
mcef |
elem_count_histogram |
As you can see, most_common_elems and most_common_elems_freqs are
empty. This prevents the query planner from making any kind of
meaningful estimate of the number of index rows that it would need to
access as a function of the query terms.
The workaround I found so far is to set a low value of
random_page_cost, but this could result in the query planner using
index scans for other tables and other queries, where a seq scan would
actually be more appropriate.
The only real solution would seem to be for the postgresql engine to
support the most_common_elems and most_common_elems_freqs for the
JSONB data type. Are there any plans to implement this?
Thanks!
-- Alex
