This means that we can read only some of the rows in the table, and since we want a small set of rows, Postgres can directly ask the index. Starting from the bottom, we can see that we are doing an index scan using the index that we created when we created the tables. This is probably the slowest sort method since there is a lot of back and forth to the disk.Īfter all that, we read from the CTE and apply our filter mbt.rank < 3 that removes 3_000_003 rows. This happens when the data does not fit in memory, and we use the disk to process it. Then we apply the sort, and we see that we are using the external merge instead of the quicksort from the first explain analyze. Unless we need a large portion of the table, this is generally inefficient. This kind of scan always reads everything in the table. This means that we scan through every page of data sequentially, read the data, apply the filter (if exists) and then discard the rows that don’t fit. Starting from the bottom, first we do a sequential scan on the movies table. Taking a closer look into the output, we can see that the WindowAgg and the Subquery Scan on the CTE are the two most expensive actions in the query. Sort Method: external merge Disk: 96624kB Sort Key: movies.tag_id, movies.created_at DESC That way, when we fetch the entries, we can limit the number of movies from each tag. It will number the rows according to the partition config we specify in the over clause. One way that we can achieve the desired result is to use the row_number() window function combined with an WITH query (common table expressions). Genres INSERT INTO "tags" ( "name" ) VALUES ( 'Action' ) INSERT INTO "tags" ( "name" ) VALUES ( 'Animation' ) INSERT INTO "tags" ( "name" ) VALUES ( 'Sci-Fi' ) - Movies INSERT INTO "movies" ( "name", "tag_id", "created_at" ) VALUES ( 'The Matrix', ( SELECT id FROM "tags" where "name" = 'Action' ), '' ) INSERT INTO "movies" ( "name", "tag_id", "created_at" ) VALUES ( 'Tenet', ( SELECT id FROM "tags" where "name" = 'Action' ), '' ) INSERT INTO "movies" ( "name", "tag_id", "created_at" ) VALUES ( 'Wonder Woman 1984', ( SELECT id FROM "tags" where "name" = 'Action' ), '' ) INSERT INTO "movies" ( "name", "tag_id", "created_at" ) VALUES ( 'Toy Story', ( SELECT id FROM "tags" where "name" = 'Animation' ), '' ) INSERT INTO "movies" ( "name", "tag_id", "created_at" ) VALUES ( 'Monsters Inc.', ( SELECT id FROM "tags" where "name" = 'Animation' ), '' ) INSERT INTO "movies" ( "name", "tag_id", "created_at" ) VALUES ( 'Finding Nemo', ( SELECT id FROM "tags" where "name" = 'Animation' ), '' ) INSERT INTO "movies" ( "name", "tag_id", "created_at" ) VALUES ( 'Arrival', ( SELECT id FROM "tags" where "name" = 'Sci-Fi' ), '' ) INSERT INTO "movies" ( "name", "tag_id", "created_at" ) VALUES ( 'Minority Report', ( SELECT id FROM "tags" where "name" = 'Sci-Fi' ), '' ) INSERT INTO "movies" ( "name", "tag_id", "created_at" ) VALUES ( 'The Midnight Sky', ( SELECT id FROM "tags" where "name" = 'Sci-Fi' ), '' ) Using row_number to solve it Let’s create our tables and populate them with some data, to help us visualize. I want to fetch the most recent videos of each tag. To make things easier, a movie can only have one tag. Imagine that we have a table tags and a table movies, with a one-to-many relationship. We need to fetch the most recent items of each tag in a list. Quite similar to a for loop that iterates through the rows returned by a SQL query. (Without LATERAL, each sub-SELECT is evaluated independently and so cannot cross-reference any other FROM item.)īasically, what it does is that for each row in the main select, it evaluates the sub-select using the main select row as a parameter. This allows the sub-SELECT to refer to columns of FROM items that appear before it in the FROM list. The LATERAL keyword can precede a sub-SELECT FROM item. Since PostgreSQL 9.3 we have a new LATERAL option for FROM-clause subqueries and function calls.Īccording to the documentation what it does is:
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