How is inner join used in MySQL Query?
Since you are using INNER JOIN this way you first filter out a with b joins and only then use the remaining join to filter out thus saving a lot of comparison actions. Imagine each table has 1 thousand rows. When you add c this becomes 1 million comparisons.
Why is MySQL very simple join taking too long?
This query takes too long, and before executing it I was trying to test how long takes this simplier query: It is still taking too long, and I don’t understand why.
Can you apply update by inner join after one left join?
You can apply the UPDATE by INNER JOIN after one LEFT JOIN to determine the needed ones to be updated. Does it have sense? I’m not any expert, but it can be useful to think about.
Why does count ( * ) query take so long?
A join involving half a million rows — as your query plan shows — is bound to take some time. The count (*) query is quicker because it doesn’t need to read the tabla_maestra table itself, but it still needs to scan all the rows of index cruce_empresa_apellido1.
Why does MySQL Query take so long to execute?
I am supposed to write a query which requires joining 3 tables. The query designed by me works fine, but it takes a lot of time to execute.
How to improve SQL Server efficient join technique?
However this performs quite badly due to the 5 usages of “exists” i.e. for 1 million rows in the main table, the query runs for 5 seconds and the STATISTICS IO output shows a looooot of reads, even if the query only returns a small subset of data. Can you give me any hint how we could improve this query dramatically?
How to avoid joins on a large table?
Tl;dr: Avoid joins on large tables and evaluate parts of queries beforehand to get 100–10,000x performance gains! As mentioned in a previous post, because of some of our tables growing in size, our queries started performing poorly which resulted in a performance hit to our most used APIs.
When to use huge gains to avoid joins?
Here is the EXPLAIN ANALYSE output for this query: https://explain.depesz.com/s/9dE Huge gains only when the join predicate matches 100+ rows, otherwise performance will be more or less the same in both the cases.