What is Teradata Query Rewriting? Top 6 Optimization Techniques Explained

What is Teradata Query Rewriting?

Teradata query rewriting is an integral component of the optimization process for Teradata.

The optimizer replaces your query with a more efficient and faster version.

Both queries must produce identical results. Teradata implements various optimization techniques to enhance performance.

We will demonstrate the commonly used optimization methods.

1. Removing unreferenced columns and expressions from the VIEW select list

Without enhancement, all VIEW columns must be spooled during query execution.

Let’s assume we have the following view definition:

CREATE VIEW MyView AS 
SELECT col1, col2, AVG(col3) myavg 
FROM table1 t01, table2 t02 
WHERE t01.pk=t02.pk GROUP BY 1,2;  

We are executing the following query:

SELECT MAX(myavg) FROM MyView;

In the example, col1 and col2 can be removed, and only myavg column will be spooled.

The following example shows another case where spool usage can be eliminated.

SELECT COUNT(*) FROM MyView;  

The query does not refer to any view column. The number of rows can be determined solely from the cylinder index.

The optimizer commonly reduces spool usage by eliminating view columns from the spool.

2. Conversion of outer joins to inner joins

If the optimizer recognizes that a query can be solved using an inner join, it will swap out the outer join to enhance its performance.

SELECT DISTINCT(col1) 
FROM table1 LEFT OUTER JOIN table2 ON table1.pk=table2.pk 
WHERE table2.col2 = 100 ;

In the example above, the outer join can be converted to an inner one because the WHERE condition filters non-matching rows.

3. Folding of Views

The optimizer removes any reference to views from the execution plan, known as view folding.

Assuming the given view definition:

CREATE VIEW MyView AS
 SELECT col1, col2, SUM(col3*col4) the_sum
 FROM table1, table2
 WHERE table1.pk=table2.pk GROUP BY 1,2;

We execute the query below:

SELECT col2 FROM MyView WHERE the_sum > 100;

The optimizer would rewrite the above example in the following form:

SELECT col2 FROM table1, table2
 WHERE table1.pk=table2.pk
 GROUP BY 1,2
 HAVING SUM(col3*col4) > 100;

The view has been removed from the execution plan.

4. Predicate Pushdown

The optimizer applies query predicates by pushing them down. For instance, in the t01 inner sub-query, the WHERE predicate is already implemented.

SELECT SUM(the_sum) the_overall_sum
 FROM (
 SELECT col1, col2, SUM(col3*col4) the_sum
 FROM table1, table2
 WHERE table1.col1=table2.col2
 GROUP BY 1, 2 ) t01
 WHERE col1 IN (1,2,3,4,5) ;

5. Elimination of SET operator branches

If a branch in SET has an unsatisfiable condition, it will be removed from the query.

Consider the following query as an example:

SELECT * FROM table1 WHERE month_id= 1
 UNION ALL
 SELECT * FROM table2 WHERE month_id = 1 ;

We assume a check constraint on month_id to ensure that table1 only has month_id = 1 and table2 only has month_id = 2.

The optimizer can rewrite the query above safely to:

SELECT * FROM table1 WHERE month_id = 1 ;

6. Elimination of Joins

Unnecessary joins have been removed from the execution plan.

SELECT table1.* FROM table1 LEFT JOIN table2 ON table1.pk=table2.pk ;

Only Table 1 needs to be spooled to fulfill the query above.

SELECT table1.* FROM table1;

The optimizer endeavors to enhance your queries. I trust you found this post enjoyable!