Continuing my blog series on reading and interpreting Oracle execution plans, this week’s post covers the different Join Methods and types available to the Optimizer.
What is an Oracle Join Method?
Join Methods are the techniques used by the Oracle Optimizer to join data coming from two data producing operators in an execution plan. You can find the individual Join Methods chosen by the Optimizer in the Operations column of an Execution table.
How many Join Methods does the Optimizer have to choose from?
The Oracles Optimizer supports three join methods; Nested Loops, Hash Join and Sort Merge Join.
Nested Loops Join
Nested loops joins are useful when small subsets
of data are being joined and if there is an efficient way of accessing the second table (for example an index lookup).
For every row in the first table (the outer table), Oracle accesses all the rows in the second table (the inner table) looking for a match. You can think of it as a set of embedded FOR loops.
NOTE: In Oracle Database 11g the internal implementation for nested loop joins changed to reduce overall latency for physical I/O. You may see two NESTED LOOPS operators in the plan’s operations column, where you previously only saw one on earlier versions of Oracle. You can find more details on why there are two operators in the video below.
Hash joins are used for joining large data sets. The Optimizer uses the smaller of the two tables or data sources to build a hash table, based on the join key, in memory. It then scans the larger table and performs the same hashing algorithm on the join column(s). It then probes the previously built hash table for each value and if they match, it returns a row.
Sort Merge Joins
Sort Merge joins are useful when the join condition between two tables is an in-equality condition such as, <, <=, >, or >=. Sort merge joins can perform better than nested loop joins for large data sets. The join consists of two steps:
- Sort join operation: Both the inputs are sorted on the join key.
- Merge join operation: The sorted lists are merged together.
A Sort Merge join is more likely to be chosen if there is an index on one of the tables that will eliminate one of the sorts.
When will the Optimizer choose each of these methods, and what can I do to influence that decision?
To clearly explain how each of the Join Methods works and when they will be chosen, I’ve created the short video below.
What if I don’t get the Join Method I want?
The leading cause of getting the wrong Join Method is typically a cardinality misestimate on the table on the left-hand side of the join. That’s why Oracle introduced Adaptive Plans and more specifically Adaptive Join Methods in Oracle Database 12c to help automatically correct itself if the wrong Join Method is chosen.
How Adaptive Joins work
During the initial execution of a plan, if Oracle detects that the Optimizer’s cardinality estimates were wrong, the join method can be changed “on the fly” to a better option.
It’s possible to change an adaptive plan “on the fly” because it consists of a default plan, which is the plan that the Optimizer picks based on the current statistics and an alternative method for various portions of the plan. For example, the default plan could be a Nested Loops plan, and the alternative(subplan) would be a Hash join.
A new operated called a Statistics Collector is inserted into the plan, right above the table on the left-hand side of the join, which will buffer the rows coming out of the table until we can get a sense of how many rows will be returned. Once we know the number of rows returned or the number is above a certain threshold, the Optimizer will choose the final join method. After the initial execution, the Statistics Collector and the subplan components not chosen become no-ops, and the impact on execution plan performance is nill.
Don’t forget this post is part of a series of posts on interpreting execution plans, which also covers, how to generate plans, cardinality estimates, and access methods.
The next instalment will be all about join orders.