Best Practices for SAP BI using DB2 9 for z/OS - IBM Redbooks

10.2 SQL efficiency for InfoCube
Now let us take a look at SQL efficiency for InfoCube. We recommend a method
to simplify the analysis of a query against an InfoCube. An example of using this
method follows a general description.
1. Analyze the joins and build a join graph.
a. Ignore the SELECT list. We assume that all the columns that are selected
are required. Note that we do not have much choice on this if the query is
generated SQL. If it is user-written SQL, then double-check the SELECT
list.
b. Ignore the FROM clause. We assume that all the tables that are listed are
required. Note that we do not have much choice on this if the query is
generated SQL. If it is user-written SQL, then double-check the FROM list.
c. Ignore the GROUP BY clause. The columns in the GROUP BY clause
must be equal to or a superset of the columns in the SELECT list. Any
column in the SELECT list that is not contained within a column function
must be in the GROUP BY clause. Note that all the columns in the
GROUP BY clause do not need to be in the SELECT list.
d. Focus on the WHERE clause.
i. For each join predicate, map the table join relationship.
ii. Ignore the local predicates for now.
iii. Check that all the tables in the FROM clause are accounted for in the
join graph. This verifies that each table has at least one join predicate.
2. Associate local predicates to their table by mapping each local predicate to
the appropriate table on the join graph.
3. Determine the filtering.
Count the number of rows retrieved for each master data table, then each
master data joined to its corresponding dimension, and finally joined to the
facttable. Include the local predicates on the tables. This shows the effect of
filtering of the dimension/snowflake against the facttable.
4. Compare the filtering of the each of dimensions/snowflakes joined to the
facttable. This data should show where the access path should start for the
query and provide a method to compare with the actual access path seen for
the query.
Let us look at an example of using this process to analyze a real-life customer
query.
Chapter 10. Tips for SQL efficiency 211