在 R 中进行 mysql 更新查询的任何更快方法？ 在蟒蛇？

Question

I tried to run this query:

update table1 A 
set number = (select count(distinct(id)) from table2 B where B.col1 = A.col1 or B.col2 = A.col2);

but it takes forever bc table1 has 1,100,000 rows and table2 has 350,000,000 rows.

Is there any faster way to do this query in R? or in python?

Answer 1

I rewrote your query with three subqueries instead of one - with UNION and two INNER JOIN statements:

UPDATE table1 as A
SET number = (SELECT COUNT(DISTINCT(id)) 
              FROM
                  (SELECT A.id as id
                   FROM table1 as A
                   INNER JOIN table2 as B
                   ON A.col1 = B.col1) -- condition for col1

                   UNION DISTINCT

                  (SELECT A.id as id
                   FROM table1 as A
                   INNER JOIN table2 as B
                   ON A.col2 = B.col2) -- condition for col2
              )

My notes:

• Updating all of the rows in table1 doesn't look like a good idea, because we have to touch 1.1M rows. Probably, another data structure for storing number would have better performance
• Try to run part of the query without update of table1 (only part of the query in parenthesis
• Take a look into EXPLAIN , if you need more general approach for optimization of SQL queries: https://dev.mysql.com/doc/refman/5.7/en/using-explain.html