![]() ![]() If you look back up at the first SQL statement that we were executing, we're basically doing the same thing: we're generating a derived table "for each row" in the outer query. Consider the use-case problem that the LATERAL derived table is solving for. This has some performance implications.īut, that doesn't mean that the performance implications are bad. A LATERAL derived table, on the other hand, can't be cached since it is executed once per row of the outer query. Which means, MySQL can run it once and then cache it for the rest of the query. The nice thing about a traditional derived table is that it has to be "constant" within the query execution plan. The more complex the query gets - and the more filtering it uses - the more savings we'll see with this new type of per-row cross-product. Since we are able to remove the non-aggregate column ( c.member_id) from the derived table query, we are also able to remove the GROUP BY and just run the aggregates ( COUNT, MIN, MAX) on the entirety of the derived recordset.Įven though this query is relatively low in complexity - we're filtering based on a single ID, using the LATERAL derived table is already simplifying the structure of the SQL. Since we no longer need to use it within the JOIN condition's ON clause, we no longer need to select the column. Remove the c.member_id column from the derived table query. Since we know that our LATERAL derived table query is running once per row, there's no need to limit the relationship within the ON clause - the derived table SQL is already applying the JOIN condition, essentially. Remove the INNER JOIN condition, mber_id = m.id. ![]() Remove the duplicated filtering reference, in the derived table query - we can now limit the query based on the condition, c.member_id = m.id. This means that our derived table query can reference the outer query which allows us to simplify: This changes the derived table from one that must be constant within the query execution plan to one that will execute once per row in the outer query. reference the row in the outer-query in order to perform theĪs you can see, I included the LATERAL keyword just prior to my derived table calculation. Since we are using a LATERAL derived table, it means that this inner Here's the same query, refactored to use a LATERAL derived table: The new LATERAL derived table removes this point-of-friction and allows our derived query to reference rows in the outer query! All we have to do is include the LATERAL keyword prior to the SELECT. Since the derived query cannot reference rows in the outer query - derived queries have to be "constant" in the query execution plan - I have to reproduce all of the desired filtering within the derived query in order to prevent a catastrophic full-table scan. M.id = - Filter OUTER QUERY to given member.Īs you can see, I'm using the to limit rows in both the outer query as well as in the derived stats query. more conditions would need to be duplicated. it's only one column but, the more complex the filtering gets, the ![]() the derived query in order to avoid a FULL TABLE SCAN. has to be a constant evaluation - I have to repeat the filtering in NOTE: Since the derived query CANNOT reference the outer query - it "once for the member" instead of "once for each aggregate". Generate a DERIVED TABLE with multiple aggregations relating to the Gather the aggregate data from the derived stats table. ![]() If I want to gather aggregate information about the comments for a given user, I would have to filter records using the member_id in both the outer query and the derived query: This is a 1-to-many relationship in which every member can leave N-number of comments (related by blog_mber_id). blog_comment - Contains all of the comments left by the members.member - Contains all of the author information for the comments.In my database schema, I have two tables: To see what I mean, let's look at an example using data from this blog. The downside to this approach is that - because the derived query can't reference the outer query - I need to duplicate all of the filtering inside of the derived query in order to limit the number of records that it generates. In the past, if I needed to pull back a number of related aggregations for a given row, I would JOIN to a derived "stats" table that contained the aggregations for the predicted records in the outer query. This feature, added in MySQL 8.0.14, will make it easier to gather relevant aggregate data for each row in the results. Not only that, the "lateral rows" are based on other rows in recordset. This is a derived table in which the resultant rows are calculated based on other tables in the query. One fun feature that caught my eye was this idea of a LATERAL derived table. After my database was accidentally upgraded to MySQL 8.0.28, I started to look at the features released in version 8. ![]()
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