In our last blog we learned about Need and Usage of Hypothetical indexes in Postgresql . Now we can check easily in live environment also if some particular index will be helpful or not , but how we get to know which index to test . It requires in-depth knowledge of indexing and experience in Postgresql , But in PostgreSql we can get automatic recommendation of indexes for specific queries by using three extensions hypog,pg_stat_statements,pg_qualstats .
Now Let’s move directly to the practical part how we can use this feature in postgres :
i am doing experiments on Postgres10 installed on Centos7 .
Installation
- Install hypog from https://hello-worlds.in/2021/05/23/hypothetical-indexes-in-postgresql-need-and-usage/
- Install pgqualstats using following command :
yum install pg_qualstats10.x86_64- Change the following in postgresql.conf and restart postgresql
shared_preload_libraries = 'pg_stat_statements, pg_qualstats' - Create following extensions :
testdb=# CREATE EXTENSION hypopg ;
CREATE EXTENSION
testdb=# CREATE EXTENSION pg_stat_statements ;
CREATE EXTENSION
testdb=# CREATE EXTENSION pg_qualstats;
CREATE EXTENSION- Set sample rate of pgqual stats to 1 in postgresql.conf . This rate define how frequently monitor and analyze the queries . Value ‘1’ represents that keep track of all queries
pg_qualstats.sample_rate = '1'- Create the function which will be used to detect usable indexes :
CREATE OR REPLACE FUNCTION find_usable_indexes()
RETURNS VOID AS
$$
DECLARE
l_queries record;
l_querytext text;
l_idx_def text;
l_bef_exp text;
l_after_exp text;
hypo_idx record;
l_attr record;
/* l_err int; */
BEGIN
CREATE TABLE IF NOT EXISTS public.idx_recommendations (queryid bigint,
query text, current_plan jsonb, recmnded_index text, hypo_plan jsonb);
FOR l_queries IN
SELECT t.relid, t.relname, t.queryid, t.attnames, t.attnums,
pg_qualstats_example_query(t.queryid) as query
FROM
(
SELECT qs.relid::regclass AS relname, qs.relid AS relid, qs.queryid,
string_agg(DISTINCT attnames.attnames,',') AS attnames, qs.attnums
FROM pg_qualstats_all qs
JOIN pg_qualstats q ON q.queryid = qs.queryid
JOIN pg_stat_statements ps ON q.queryid = ps.queryid
JOIN pg_amop amop ON amop.amopopr = qs.opno
JOIN pg_am ON amop.amopmethod = pg_am.oid,
LATERAL
(
SELECT pg_attribute.attname AS attnames
FROM pg_attribute
JOIN unnest(qs.attnums) a(a) ON a.a = pg_attribute.attnum
AND pg_attribute.attrelid = qs.relid
ORDER BY pg_attribute.attnum) attnames,
LATERAL unnest(qs.attnums) attnum(attnum)
WHERE NOT
(
EXISTS
(
SELECT 1
FROM pg_index i
WHERE i.indrelid = qs.relid AND
(arraycontains((i.indkey::integer[])[0:array_length(qs.attnums, 1) - 1],
qs.attnums::integer[]) OR arraycontains(qs.attnums::integer[],
(i.indkey::integer[])[0:array_length(i.indkey, 1) + 1]) AND i.indisunique)))
GROUP BY qs.relid, qs.queryid, qs.qualnodeid, qs.attnums) t
GROUP BY t.relid, t.relname, t.queryid, t.attnames, t.attnums
LOOP
/* RAISE NOTICE '% : is queryid',l_queries.queryid; */
execute 'explain (FORMAT JSON) '||l_queries.query INTO l_bef_exp;
execute 'select hypopg_reset()';
execute 'SELECT indexrelid,indexname FROM hypopg_create_index(''CREATE INDEX on '||l_queries.relname||'('||l_queries.attnames||')'')' INTO hypo_idx;
execute 'explain (FORMAT JSON) '||l_queries.query INTO l_after_exp;
execute 'select hypopg_get_indexdef('||hypo_idx.indexrelid||')' INTO l_idx_def;
INSERT INTO public.idx_recommendations (queryid,query,current_plan,recmnded_index,hypo_plan)
VALUES (l_queries.queryid,l_querytext,l_bef_exp::jsonb,l_idx_def,l_after_exp::jsonb);
END LOOP;
execute 'select hypopg_reset()';
END;
$$ LANGUAGE plpgsql;Usage
- Now Let’s make a table with 10 Crores rows as following :
testdb=# create table orders as select s as orderno, md5(random()::text) as orderitem , now() as order_created from generate_Series(1,100000000) s;
SELECT 100000000- Now lets run a query on which we want to check if need to make any index
testdb=# select * from orders where orderno = 80000 ;
-[ RECORD 1 ]-+---------------------------------
orderno | 80000
orderitem | 03b41c2f32d99e9a597010608946c4c6
order_created | 2021-05-22 17:52:21.435936+05:30- Now run following queries to find out which indexes are recommend by this extension and what are the improvement percentage by applying these indexes hypothetically
testdb=# select find_usable_indexes();
find_usable_indexes
---------------------
(1 row)
testdb=# select b.query, a.recmnded_index,round((((a.current_plan->0->'Plan'->>'Total Cost')::numeric-(hypo_plan->0->'Plan'->>'Total Cost')::numeric)*100/(a.current_plan->0->'Plan'->>'Total Cost')::numeric),2) as percent_improvd FROM idx_recommendations a JOIN pg_stat_statements b ON a.queryid = b.queryid WHERE round((((current_plan->0->'Plan'->>'Total Cost')::numeric-(hypo_plan->0->'Plan'->>'Total Cost')::numeric)*100/(current_plan->0->'Plan'->>'Total Cost')::numeric),2) > 0 order by 3 desc ;
query | recmnded_index | percent_improvd
-----------------------------------------------------+------------------------------------------------------------------+-----------------
select * from orders where orderno = $1 | CREATE INDEX ON public.orders USING btree (orderno) | 100.00Above analysis was internally done by creating the indexes hypothetically not by making real indexes .
Please Note here that do not fully rely on automatic index recommendation . Yes we have no doubt it is very very useful feature , but please also check logically why these recommended indexes are useful and should you really create it or not.
You can read PostgreSQL Index Tutorial Series for basic in depth understanding of indexes in postgresql .
Now , You can try the explained feature with more complex queries and comment on this article explaining your result with your queries . So let’s experiment and comment .
helloworlds 