Update: This answer covers the general error classification. For a more specific answer about how to best handle the OP's exact query, please see other answers to this question
In MySQL, you can't modify the same table which you use in the SELECT part.
This behaviour is documented at:
http://dev.mysql.com/doc/refman/5.6/en/update.html
Maybe you can just join the table to itself
If the logic is simple enough to re-shape the query, lose the subquery and join the table to itself, employing appropriate selection criteria. This will cause MySQL to see the table as two different things, allowing destructive changes to go ahead.
UPDATE tbl AS a
INNER JOIN tbl AS b ON ....
SET a.col = b.col
Alternatively, try nesting the subquery deeper into a from clause ...
If you absolutely need the subquery, there's a workaround, but it's
ugly for several reasons, including performance:
UPDATE tbl SET col = (
SELECT ... FROM (SELECT.... FROM) AS x);
The nested subquery in the FROM clause creates an implicit temporary
table, so it doesn't count as the same table you're updating.
... but watch out for the query optimiser
However, beware that from MySQL 5.7.6 and onward, the optimiser may optimise out the subquery, and still give you the error. Luckily, the optimizer_switch
variable can be used to switch off this behaviour; although I couldn't recommend doing this as anything more than a short term fix, or for small one-off tasks.
SET optimizer_switch = 'derived_merge=off';
Thanks to Peter V. Mørch for this advice in the comments.
Example technique was from Baron Schwartz, originally published at Nabble, paraphrased and extended here.
My favorite answer is as what the first sentence in this thread suggested. Use an Adjacency List to maintain the hierarchy and use Nested Sets to query the hierarchy.
The problem up until now has been that the coversion method from an Adjacecy List to Nested Sets has been frightfully slow because most people use the extreme RBAR method known as a "Push Stack" to do the conversion and has been considered to be way to expensive to reach the Nirvana of the simplicity of maintenance by the Adjacency List and the awesome performance of Nested Sets. As a result, most people end up having to settle for one or the other especially if there are more than, say, a lousy 100,000 nodes or so. Using the push stack method can take a whole day to do the conversion on what MLM'ers would consider to be a small million node hierarchy.
I thought I'd give Celko a bit of competition by coming up with a method to convert an Adjacency List to Nested sets at speeds that just seem impossible. Here's the performance of the push stack method on my i5 laptop.
Duration for 1,000 Nodes = 00:00:00:870
Duration for 10,000 Nodes = 00:01:01:783 (70 times slower instead of just 10)
Duration for 100,000 Nodes = 00:49:59:730 (3,446 times slower instead of just 100)
Duration for 1,000,000 Nodes = 'Didn't even try this'
And here's the duration for the new method (with the push stack method in parenthesis).
Duration for 1,000 Nodes = 00:00:00:053 (compared to 00:00:00:870)
Duration for 10,000 Nodes = 00:00:00:323 (compared to 00:01:01:783)
Duration for 100,000 Nodes = 00:00:03:867 (compared to 00:49:59:730)
Duration for 1,000,000 Nodes = 00:00:54:283 (compared to something like 2 days!!!)
Yes, that's correct. 1 million nodes converted in less than a minute and 100,000 nodes in under 4 seconds.
You can read about the new method and get a copy of the code at the following URL.
http://www.sqlservercentral.com/articles/Hierarchy/94040/
I also developed a "pre-aggregated" hierarchy using similar methods. MLM'ers and people making bills of materials will be particularly interested in this article.
http://www.sqlservercentral.com/articles/T-SQL/94570/
If you do stop by to take a look at either article, jump into the "Join the discussion" link and let me know what you think.
Best Answer
You can wrap it in a subquery like so. The issue is that MySQL can't update rows that it's also querying. This will make MySQL use a temporary table implicitly to store the ids you want to delete.