Nested loop (loop over loop)
In this algorithm, an outer loop is formed which consists of few entries and then for each entry, and inner loop is processed.
Select tab1.*, tab2.* from tabl, tab2 where tabl.col1=tab2.col2;
For i in (select * from tab1) loop
For j in (select * from tab2 where col2=i.col1) loop
The Steps involved in doing nested loop are:
a) Identify outer (driving) table
b) Assign inner (driven) table to outer table.
c) For every row of outer table, access the rows of inner table.
In execution plan it is seen like this:
When optimizer uses nested loops?
Optimizer uses nested loop when we are joining tables containing small number of rows with an efficient driving condition. It is important to have an index on column of inner join table as this table is probed every time for a new value from outer table.
- No of rows of both the table is quite high
- Inner query always results in same set of records
- The access path of inner table is independent of data coming from outer table.
Note: You will see more use of nested loop when using FIRST_ROWS optimizer mode as it works on model of showing instantaneous results to user as they are fetched. There is no need for selecting caching any data before it is returned to user. In case of hash join it is needed and is explained below.
Hash joins are used when the joining large tables. The optimizer uses smaller of the 2 tables to build a hash table in memory and the scans the large tables and compares the hash value (of rows from large table) with this hash table to find the joined rows.
The algorithm of hash join is divided in two parts
- Build a in-memory hash table on smaller of the two tables.
- Probe this hash table with hash value for each row second table
In simpler terms it works like
For each row RW1 in small (left/build) table loop
Calculate hash value on RW1 join key
Insert RW1 in appropriate hash bucket.
For each row RW2 in big (right/probe) table loop
Calculate the hash value on RW2 join key
For each row RW1 in hash table loop
If RW1 joins with RW2
Return RW1, RW2
When optimizer uses hash join?
Optimizer uses has join while joining big tables or big fraction of small tables.
Unlike nested loop, the output of hash join result is not instantaneous as hash joining is blocked on building up hash table.
Note: You may see more hash joins used with ALL_ROWS optimizer mode, because it works on model of showing results after all the rows of at least one of the tables are hashed in hash table.
Sort merge join
Sort merge join is used to join two independent data sources. They perform better than nested loop when the volume of data is big in tables but not as good as hash joins in general.
They perform better than hash join when the join condition columns are already sorted or there is no sorting required.
The full operation is done in two parts:
- Sort join operation
get first row RW1 from input1
get first row RW2 from input2.
- Merge join operation
while not at the end of either input loop
if RW1 joins with RW2
get next row R2 from input 2
return (RW1, RW2)
else if RW1 < style=""> get next row RW1 from input 1
get next row RW2 from input 2
Note: If the data is already sorted, first step is avoided.
Important point to understand is, unlike nested loop where driven (inner) table is read as many number of times as the input from outer table, in sort merge join each of the tables involved are accessed at most once. So they prove to be better than nested loop when the data set is large.
When optimizer uses Sort merge join?
a) When the join condition is an inequality condition (like <, <=, >=). This is because hash join cannot be used for inequality conditions and if the data set is large, nested loop is definitely not an option.
b) If sorting is anyways required due to some other attribute (other than join) like “order by”, optimizer prefers sort merge join over hash join as it is cheaper.
Note: Sort merge join can be seen with both ALL_ROWS and FIRST_ROWS optimizer hint because it works on a model of first sorting both the data sources and then start returning the results. So if the data set is large and you have FIRST_ROWS as optimizer goal, optimizer may prefer sort merge join over nested loop because of large data. And if you have ALL_ROWS as optimizer goal and if any inequality condition is used the SQL, optimizer may use sort-merge join over hash join