There is a table:
doc_id(integer)-value(integer)
Approximate 100.000 doc_id and 27.000.000 rows.
Majority query on this table – searching documents similar to current document:
select 10 documents with maximum of
(count common to current document value)/(count ov values in document).
Nowadays we use PostgreSQL. Table weight (with index) ~1,5 GB. Average query time ~0.5s – it is to hight. And, for my opinion this time will grow exponential with growing of database.
Should I transfer all this to NoSQL base, if so, what?
QUERY:
EXPLAIN ANALYZE
SELECT D.doc_id as doc_id,
(count(D.doc_crc32) *1.0 / testing.get_count_by_doc_id(D.doc_id))::real as avg_doc
FROM testing.text_attachment D
WHERE D.doc_id !=29758 -- 29758 - is random id
AND D.doc_crc32 IN (select testing.get_crc32_rows_by_doc_id(29758)) -- get_crc32... is IMMUTABLE
GROUP BY D.doc_id
ORDER BY avg_doc DESC
LIMIT 10
Limit (cost=95.23..95.26 rows=10 width=8) (actual time=1849.601..1849.641 rows=10 loops=1)
-> Sort (cost=95.23..95.28 rows=20 width=8) (actual time=1849.597..1849.609 rows=10 loops=1)
Sort Key: (((((count(d.doc_crc32))::numeric * 1.0) / (testing.get_count_by_doc_id(d.doc_id))::numeric))::real)
Sort Method: top-N heapsort Memory: 25kB
-> HashAggregate (cost=89.30..94.80 rows=20 width=8) (actual time=1211.835..1847.578 rows=876 loops=1)
-> Nested Loop (cost=0.27..89.20 rows=20 width=8) (actual time=7.826..928.234 rows=167771 loops=1)
-> HashAggregate (cost=0.27..0.28 rows=1 width=4) (actual time=7.789..11.141 rows=1863 loops=1)
-> Result (cost=0.00..0.26 rows=1 width=0) (actual time=0.130..4.502 rows=1869 loops=1)
-> Index Scan using crc32_idx on text_attachment d (cost=0.00..88.67 rows=20 width=8) (actual time=0.022..0.236 rows=90 loops=1863)
Index Cond: (d.doc_crc32 = (testing.get_crc32_rows_by_doc_id(29758)))
Filter: (d.doc_id <> 29758)
Total runtime: 1849.753 ms
(12 rows)
1.5 GByte is nothing. Serve from ram. Build a datastructure that helps you searching.