You can see that in the result of the EXPLAIN query from the question: table T1 ( pub_year) comes first. (As a side note, the MySQL query optimizer is smart enough to change the query and put the tables in the order that provides the best execution time. INNER JOIN citations T2 ON T2.paperkey1 = T1.paperkey
In this case, that table is pub_year: UPDATE pub_year T1 It will still take a couple of minutes (or even more if the tables are accessed by other processes during the query) but this OK when you update 5 million records.įor performance reasons, on INNER JOINs it's recommended to put first the table that produces the smallest number of rows in the final result set. Analytical queries run on online transaction-processing (OLTP) databases often take a long time to. You should get ref or eq_ref in the column type of the second row. Online analytical processing (OLAP) databases are purpose-built for handling analytical queries. If one of the two tables already contain an index on the aforementioned column (or it is the first column in a multi-column index) then skip that table having identical indexes doesn't help.Īfter the indexes are created (they will take some time to complete, especially if there is other activity on these tables in the same time), run the EXPLAIN again and check the result. By default, the execution mode is set to parallel so that refresh tasks finish as quickly as possible. When you run a schedule serially, it only runs on one backgrounder process. An index will fix that.Īdd an index on column Paperkey1 of table citations: ALTER TABLE `citations` ADD INDEX (`Paperkey1`) Īlso add an index on column Paperkey of table pub_year: ALTER TABLE `pub_year` ADD INDEX (`Paperkey`) When you run a schedule in parallel, it runs on all available backgrounder processes, even if the schedule contains only one refresh task.
As a result, many Apple Watch owners will either postpone software updates indefinitely or install software updates to watchOS overnight, or when they know they won’t need their watch anytime soon. For each of the 5 million rows from citations it needs to scan all the 3 million rows of table pub_year in order to find the matching rows for the JOIN clause. Some simple updates may install in a reasonable amount of time, but some of the larger watchOS updates can take an hour or much more. This is the cause of the very, very slow execution. The second row has value ALL in column type.