For the following query, the optimizer evaluates two different nestings. In both nestings, T1 must be processed in the outer loop because it is used in an outer join. T2 and T3 are used in an inner join, so that join must be processed in the inner loop. However, because the join is an inner join, T2 and T3 can be processed in either order.
One nesting evaluates T2 , then T3 :. The other nesting evaluates T3 , then T2 :. When discussing the nested-loop algorithm for inner joins, we omitted some details whose impact on the performance of query execution may be huge. In this case, MySQL actually uses the following nested-loop algorithm for the execution of the query with inner joins:. You see that each of the conjuncts C1 T1 , C2 T2 , C3 T3 are pushed out of the most inner loop to the most outer loop where it can be evaluated.
If C1 T1 is a very restrictive condition, this condition pushdown may greatly reduce the number of rows from table T1 passed to the inner loops. As a result, the execution time for the query may improve immensely. For a query with outer joins, the WHERE condition is to be checked only after it has been found that the current row from the outer table has a match in the inner tables.
Thus, the optimization of pushing conditions out of the inner nested loops cannot be applied directly to queries with outer joins. Here we must introduce conditional pushed-down predicates guarded by the flags that are turned on when a match has been encountered. For that example, the nested-loop algorithm using guarded pushed-down conditions looks like this:. In this case, a pushed-down predicate is guarded also by a flag that prevents checking the predicate for the NULL -complemented row generated by the corresponding outer join operation.
Access by key from one inner table to another in the same nested join is prohibited if it is induced by a predicate from the WHERE condition. Optimizing SQL Statements. Index Merge Optimization. Engine Condition Pushdown Optimization. Index Condition Pushdown Optimization. Nested Join Optimization. Outer Join Optimization. Outer Join Simplification.
Multi-Range Read Optimization. Constant-Folding Optimization. Function Call Optimization. Window Function Optimization. Note that the join is an integral part of the select statement. It can not stand on its own as a subquery can. You can actually copy and run the in their own query window.
These are correlated subqueries. The subquery is evaluated once for each outer query row. Despite their differences, joins and subqueries are used to solve similar problems. Suppose the Sales Manager for Adventure Works wants a detailed listing of all sales orders and the number of order details lines for each order. Here is the query plan for a subquery.
The subquery uses the same set of operations to return a result as you see with the join! You can get more detail about his in my article what is a query plan.
I have put together a really great series of videos explaining subqueries and their mysteries. Click the button below to see more! Kris Wenzel has been working with databases over the past 30 years as a developer, analyst, and DBA. Kris has written hundreds of blog articles and many online courses.
He loves helping others learn SQL. Usually joins will work faster than inner queries, but in reality it will depend on the execution plan generated by SQL Server. No matter how you write your query, SQL Server will always transform it on an execution plan. If it is "smart" enough to generate the same plan from both queries, you will get the same result. A subquery can be used with JOIN operation. The temporary table from the subquery is given an alias so that we can refer to it in the outer select statement.
Note that the left and right table of the join keyword must both return a common key that can be used for the join. Why do we use subquery? A subquery is used to return data that will be used in the main query as a condition to further restrict the data to be retrieved. A subquery cannot be immediately enclosed in a set function. Is having faster than where? Why left outer join is faster than inner join?
For example, between a pair of tables:. Note that using a normal value operator on the results of a sub-query requires that only one field must be returned.
If you're interested in checking for the existence of a single value within a set of other values, use IN:. If you're just worried about speed you'll have to check with your database and write a good query and see if there's any significant difference in performance.
Some people say "some RDBMS can rewrite a subquery to a join or a join to a subquery when it thinks one is faster than the other. Here is an example where sub queries work better than JOINs.
A comparison of the rows column tells the difference and the query with JOINs is using Using temporary; Using filesort. Of course when I run both the queries, the first one is done in 0. Subqueries have ability to calculate aggregation functions on a fly. Find minimal price of the book and get all books which are sold with this price.
The difference is only seen when the second joining table has significantly more data than the primary table. I had an experience like below We had a users table of one hundred thousand entries and their membership data friendship about 3 hundred thousand entries.
It was a join statement in order to take friends and their data, but with a great delay. But it was working fine where there was only a small amount of data in the membership table. Once we changed it to use a sub-query it worked fine. But in the mean time the join queries are working with other tables that have fewer entries than the primary table. So I think the join and sub query statements are working fine and it depends on the data and the situation.
These days, many dbs can optimize subqueries and joins. Thus, you just gotto examine your query using explain and see which one is faster. If there is not much difference in performance, I prefer to use subquery as they are simple and easier to understand.
I need connect and query from large tables, the "slave" table have 28 million record but the result is only so small result big data! I am using MAX function on it. Just in the chokito adv. The subselect looks terrible but the result It depends on several factors, including the specific query you're running, the amount of data in your database. Subquery runs the internal queries first and then from the result set again filter out the actual results.
Whereas in join runs the and produces the result in one go. The best strategy is that you should test both the join solution and the subquery solution to get the optimized solution.
The general idea about sub queries vs joins is the path the evaluation of the larger query takes. In order to perform the larger query, every individual subquery has to be executed first, and then the resultset is stored as a temporary table that the larger query interacts with.
In contrast, when you use a join, all indexes are in use and so, comparison require traversing index trees or hash tables , which is way less expensive in terms of speed. Now, what I don't know if newer versions of the most popular relational engines execute the evaluation on reverse, and just load the necessary elements in the temporary table, as an optimization method. So, No use of using it in "On". So, Try to use "Where" condition. Stack Overflow for Teams — Collaborate and share knowledge with a private group.
Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. Join vs. Asked 11 years, 7 months ago. Active 2 months ago. Viewed k times. Improve this question. Peter Mortensen 29k 21 21 gold badges 97 97 silver badges bronze badges. Your Common Sense Your Common Sense k 35 35 gold badges silver badges bronze badges.
Subqueries are great sometimes. They suck performance-wise in MySQL. Don't use them. I was always under the impression that sub-queries implicitly were executed as joins where available in certain DB technologies.
Sub queries don't always suck, when joining with pretty large tables, the preferred way is to do a sub-select from that large table limiting the number of rows and then joining. Potentially related although much more specific : stackoverflow.
Show 6 more comments. Active Oldest Votes. Improve this answer. Marcelo Cantos Marcelo Cantos k 37 37 gold badges silver badges bronze badges. Great answer. I'd also add that developers esp.
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