Kick-off

Hi all!
Here is a new blog; I'll post here some thoughts, tutorials... all related to my job - software developer at Oracle Corporation, working on MySQL. It will soon be nine years since I joined MySQL AB. After working on interesting topics: Replication, Online Backup, the Maria storage engine, each of them having taught me something, I'm now full-time on the Optimizer, and guess what - it is an interesting topic and it teaches me something!

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WITH RECURSIVE and MySQL

[2017 update: MySQL 8.0.1 now features SQL-standard CTE syntax; more information is here ; the entry below, from 2013, shows how to work around the absence of CTEs in older MySQL versions.] If you have been using certain DBMSs, or reading recent versions of the SQL standard, you are probably aware of the so-called "WITH clause" of SQL. Some call it Subquery Factoring . Others call it  Common Table Expression (CTE). In its simplest form, this feature is a kind of "boosted derived table". Assume that a table T1 has three columns: CREATE TABLE T1( YEAR INT, # 2000, 2001, 2002 ... MONTH INT, # January, February, ... SALES INT # how much we sold on that month of that year ); Now I want to know the sales trend (increase/decrease), year after year: SELECT D1.YEAR, (CASE WHEN D1.S>D2.S THEN 'INCREASE' ELSE 'DECREASE' END) AS TREND FROM (SELECT YEAR, SUM(SALES) AS S FROM T1 GROUP BY YEAR) AS D1, (SELECT YEAR, SUM(SALES) AS S FROM T1 GR
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Faster subqueries with materialization

In a previous post , I analyzed how a query of the famous DBT3 benchmark was optimized by MySQL. It was this query, named "Q16" in the DBT3 jargon: select p_brand, p_type, p_size, count(distinct ps_suppkey) as supplier_cnt from partsupp, part where p_partkey = ps_partkey and p_brand <> 'Brand#23' and p_type not like 'LARGE PLATED%' and p_size in (43, 1, 25, 5, 35, 12, 42, 40) and ps_suppkey not in ( select s_suppkey from supplier where s_comment like '%Customer%Complaints%' ) group by p_brand, p_type, p_size order by supplier_cnt desc, p_brand, p_type, p_size; Here is a brief recap of conclusions I had drawn: for this query, MySQL tranforms the IN condition to EXISTS and then evaluates it with the "unique_subquery" technique, which does an index lookup into the subquery's table. IN is evaluated 120,000 times (once per combined row of the outer tables). The total execution time of query Q16
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