Originally posted at Andres’s blog. Traditional query optimizing is mostly about two things: first, in which order and from where to access data, and then how to then combine it. You have probably seen the tree shapes execution plans that are produced from query planning. I’ll use an example from the MySQL docs, using FORMAT=TREE which was introduced in MySQL 8.0: mysql> EXPLAIN FORMAT=TREE -> SELECT * -> FROM t1 -> JOIN t2 -> ON (t1.

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MySQL introduced optimizer hints in version 5.7 and greatly extended the feature in MySQL 8. One thing that has been missing though is the ability to specify index hints using the syntax of optimizer hints. This has been improved of in MySQL 8.0.20 with the introduction of index-level optimizer hints for the FORCE and IGNORE versions of the index hints. This blog will look at the new index hint syntax.

Warning

Do not add index hints – neither using the old or new style – unless you really need them. When you add index hints, you limit the options of the optimizer which can prevent the optimizer obtaining the optimal query plan as new optimizer improvements are implemented or the data changes.

On …

I will try to make it short and clear: if you are writing SQL queries with “NOT IN” like
SELECT … WHERE x NOT IN (SELECT y FROM …)
you have to be sure to first understand what happens when “x” or “y” are NULL: it might not be what you want!…

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In MySQL 8.0.1, we introduced support for recursive common table expressions (CTE). There are quite a few blog entries showcasing the feature, starting from this one, and there is also a complete documentation. Today, I would like to present a solution to a problem which nearly everybody meets when writing queries with recursive CTE’s: when infinite recursion happens, how to debug ?…

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In MySQL 8.0.17, we made an observation in the well-known TPC-H benchmark for one particular query. The query was executing 20% faster than in MySQL 8.0.16. This improvement is because of the “antijoin” optimization which I implemented. Here is its short mention in the release notes:

“The optimizer now transforms a WHERE condition having NOT IN (subquery), NOT EXISTS (subquery), IN (subquery) IS NOT TRUE, or EXISTS (subquery) IS NOT TRUE internally into an antijoin, thus removing the subquery.”…

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For a long time, the only algorithm for executing a join in MySQL has been variations of the nested loop algorithm. With the release of MySQL 8.0.18, the server can now execute joins using hash join. This blog post will have a look at how it works, when it is used, and how it compares to the old join algorithms in MySQL in terms of performance.…

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MySQL 8.0.18 was just released, and it contains a brand new feature to analyze and understand how queries are executed: EXPLAIN ANALYZE.

What is it?

EXPLAIN ANALYZE is a profiling tool for your queries that will show you where MySQL spends time on your query and why.…

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Sometimes, the MySQL Optimizer chooses a wrong plan, and a query that should execute in less than 0.1 second ends-up running for 12 minutes !  This is not a new problem: bugs about this can be traced back to 2014, and a blog post on this subject was published in 2015.  But even if this is old news, because this problem recently came yet again to my attention, and because this is still not fixed in MySQL 5.7 and 8.0, this is a subject worth writing about.

The MySQL Optimizer

Before looking at the problematic query, we have to say a few words about the optimizer.  The Query Optimizer is the part of query execution that chooses the query plan.  A Query Execution Plan is the way MySQL chooses to execute a specific query.  It …

(Image credit: Pixabay).

In my earlier post, I showed how MySQL, since version 8.0.14, has support for LATERAL derived tables. With LATERAL, a JOIN can have a second table – a subquery-based derived table – be defined based on values from columns of the first table, and thus be re-calculated for each row of the first table.…

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In the just-released MySQL 8.0.14 I added a feature called LATERAL derived tables.

The manual describes the syntax and has examples of how the feature can be used to find greatest values in tables. In the present post I’m going to consider a different problem solved by LATERAL: let’s say that we have a bunch of nodes, and want to make a “random graph”, by connecting every node to other nodes.…

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