I need to optimize the performance of a commom WITH RECURSIVE query... We can limit the depth of the tree and decompose in many updates, and can also change representation (use array)... I try some options but perhaps there are a "classic optimization solution" that I'm not realizing.
All details
There are a t_up table, to be updated, with a composit primary key (pk1,pk2), one attribute attr and an array of references to primary keys... And a unnested representation t_scan, with the references; like this:
pk1 | pk2 | attr | ref_pk1 | ref_pk2
n | 123 | 1 | |
n | 456 | 2 | |
r | 123 | 1 | w | 123
w | 123 | 5 | n | 456
r | 456 | 2 | n | 123
r | 123 | 1 | n | 111
n | 111 | 4 | |
... | ...| ... | ... | ...
There are no loops.
UPDATE t_up SET x = pairs
FROM (
WITH RECURSIVE tree as (
SELECT pk1, pk2, attr, ref_pk1, ref_pk2,
array[array[0,0]]::bigint[] as all_refs
FROM t_scan
UNION ALL
SELECT c.pk1, c.pk2, c.attr, c.ref_pk1, c.ref_pk2
,p.all_refs || array[c.attr,c.pk2]
FROM t_scan c JOIN tree p
ON c.ref_pk1=p.pk1 AND c.ref_pk2=p.pk2 AND c.pk2!=p.pk2
AND array_length(p.all_refs,1)<5 -- 5 or 6 avoiding endless loops
)
SELECT pk1, pk2, array_agg_cat(all_refs) as pairs
FROM (
SELECT distinct pk1, pk2, all_refs
FROM tree
WHERE array_length(all_refs,1)>1 -- ignores initial array[0,0].
) t
GROUP BY 1,2
ORDER BY 1,2
) rec
WHERE rec.pk1=t_up.pk1 AND rec.pk2=t_up.pk2
;
To test:
CREATE TABLE t_scan(
pk1 char,pk2 bigint, attr bigint,
ref_pk1 char, ref_pk2 bigint
);
INSERT INTO t_scan VALUES
('n',123, 1 ,NULL,NULL),
('n',456, 2 ,NULL,NULL),
('r',123, 1 ,'w' ,123),
('w',123, 5 ,'n' ,456),
('r',456, 2 ,'n' ,123),
('r',123, 1 ,'n' ,111),
('n',111, 4 ,NULL,NULL);
Running only rec you will obtain:
pk1 | pk2 | pairs
-----+-----+-----------------
r | 123 | {{0,0},{1,123}}
r | 456 | {{0,0},{2,456}}
w | 123 | {{0,0},{5,123}}
But, unfortunately, to appreciate the "Big Data performance problem", you need to see it in a real database... I am preparing a public Github that run with OpenStreetMap Big Data.
