9/16/2023 0 Comments Postgres anti joinSolution: Don’t add in ORDER BY clause in your viewĭon’t add in ORDER BY clause in your view if you plan on sorting it by another field. This means Postgres has to do 2 sorts, the first one, the one in the view, is pointless as the result will have to be resorted. This is an issue if you plan on sorting the view afterwards. * from abc left join def on abc.abc_id =def.def_id where def.def_id is null The following query would likely be already optimized by Postgres as it will prefer to join with another_table and someone first, then use that result to join with the event table as it knows that order will result in the smallest intermediate tables.Įxplain ( analyze, costs off) select * from abc where not exists ( select def_id from def where def_id =abc_id) Įxplain ( analyze, costs off) select abc. Postgres already knows the distribution of existing tables and will try to rearrange JOINs in an optimal way, up to join_collapse_limit tables - it just has less information about potential aggregations. This all depends on the query and your data distribution. For example, joining 5 rows from the user table to the event table then aggregating would be much faster if the number of matches in the event table is small. Note there are MANY cases where the JOIN then AGGREGATE is the preferable solution (this is the “default” as aggregates happen after JOINs anyways). Index Cond: (someone_id = event.someone_id) Heap Fetches: 0 -> Index Scan using someone_pkey on someone (actual time = 0. > Index Only Scan using event_someone_id_idx on event (actual time = 0. Sort Key: ( count( *)) DESC Sort Method: top -N heapsort Memory: 25kB count from someone join ( select someone_id, count( *) from event group by someone_id order by count( *) desc limit 10) as aggregated using (someone_id) order by aggregated. Total_rows_scanned = 10,000,000 + 1,000,000 + 10 + 10 * log2(1,000,000)ġ1 million rows (Note I’m assuming a log base and branching factor, of 2, which is not accurate as the B-Tree used in Postgres’ index has a variable branching factor and is usually much larger than 2 but this is good enough for this estimation).Įxplain ( analyze, costs off) select someone.name, aggregated. total_rows_scanned = rows_from_event + rows_from_aggregate + rows_created_from_aggregated + rows_created_from_aggregated * log(num_rows_in_user) Because there are only 10 rows for the output and an index available in the someone table, Postgres will prefer to do a Nested Loop, with the 10 rows as the outer loop and an index scan over users in the inner loop - 10 times to logarithmically search the 1,000,000 users. Then it must join those 10 rows with the 1,000,000 users but at this point. How many rows will this scan through? 10,000,000 rows to aggregate resulting in 1,000,000 rows which are then sorted and limited, resulting in 10 rows. ORDER BY count( *) DESC LIMIT 10 ) AS aggregated USING (someone_id) We want to find the name of the top 10 users with the most events. For inner joins with aggregates, the order does not matter for the end result, but may cause different performance characteristics.įor this example, there are 10,000,000 click events in an event table, related to user events. This is a very situationally dependent issue that requires understanding your data distribution. You Join then Aggregate a result when you should do the opposite. Joining then aggregating (or vice versa) # Index Cond: (parent_id = child.parent_id) Index Cond: ((name >= '456'::text) AND (name Index Scan using parent_pkey on parent parent_1 (actual time = 0. Heap Blocks: exact = 1030 -> Bitmap Index Scan on child_name_idx (actual time = 0. > Bitmap Heap Scan on child (actual time = 0. Index Cond: ((name >= '456'::text) AND (name Nested Loop (actual time = 0. Heap Blocks: exact = 94 -> Bitmap Index Scan on parent_name_idx (actual time = 0. > Bitmap Heap Scan on parent (actual time = 0. * from parent left join child using (parent_id) where child.name like '456%' * from parent where parent.name like '456%' union distinct select parent. 047 rows = 1000000 loops = 1)Įxplain ( analyze, costs off) select parent. > Seq Scan on person prev_person (actual time = 0. Hash Cond: ((prev_person.id + 1) = person.id) > Seq Scan on person next_person (actual time = 0. Hash Cond: ((next_person.id - 1) = person.id) Explain ( analyze, costs off) select person.name, prev_person.name as prev_name, next_person.name as next_name from person left join person prev_person on person.id =prev_person.id + 1 left join person next_person on person.id =next_person.id - 1
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