SAS®, Sun, Oracle: On Mashups, Enterprise 2.0 and Ideation Charlie Garry, Director, Product Manager, Oracle Corporation Charlie Garry, Director, Product Manager, Oracle Corporation Paul Kent, Vice President, Platform R&D, SAS Maureen Chew, Principal Software Engineer, Oracle Corporation
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SAS , Sun, Oracle: On Mashups, Enterprise 2.0 and Ideation · SAS ®, Sun, Oracle: On Mashups, Enterprise 2.0 and Ideation Charlie Garry, Director, Product Manager, Oracle Corporation
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SAS®, Sun, Oracle: On Mashups, Enterprise 2.0 and Ideation
“Our customers are very interested in getting maximum value out of our business analytic solutions and that means putting less effort into provisioning and managing infrastructure. Our ability to partner with and leverage the fusion of Sun into Oracle to simplify infrastructure will be a benefit to our mutual
On Collaboration ...
infrastructure will be a benefit to our mutual customers.”
Keith Collins, SAS Senior Vice President and Chief Technology Officer
Sun & Oracle – A Better Platform for SAS
� SAS uses many Oracle and Sun technologies
� Solaris is a leading UNIX deployment platform for SAS
� Sun HW / Storage
� WebLogic
� Java
� LDAP� LDAP
� ACCESS / Oracle / Exadata
� MySQL
Oracle & SAS Collaboration
� Partnership & Collaboration
� High end performance testing
» SAS Enterprise BI, Sun Enterprise M9000
» JMP Genomics
» SAS Grid
» Sun Blade 6000» Sun Blade 6000
» Sun ZFS Storage 7420
� Broad Engineering collaboration
� http://oracle.com/sas
SQL Optimization – the “Obvious”
� UNION, MINUS, INTERSECT : sort to elminate duplicate rows; UNION ALL : no sort, includes dups
� IN vs EXISTS
� Queries using IN or NOT IN could convert to EXISTS / NOT EXISTS (or vice versa) - bit.ly/gZvzeM
� Wildcard search against an index� Wildcard search against an index
� Indexes (ie: COL) usable only from beginning of column
» “COL like 'abc123%'” uses index, “COL like '%abc123%'” does not
� Functions cannot use index, create “functional” index
� UPPER(COL)='ABC123' → create index idx on tablename(UPPER(COL));
SQL Optimization – the “less Obvious”
� Collect good statistics using DBMS_STATS
� Poor query performance can result from stale stats, data skew
� Partition large tables
� ie: Partition data by week - retrieves 1/52 of table
� CTAS instead of UPDATE/DELETE (DML)
� If deleting large number of rows, often better to CREATE TABLE xyz AS SELECT … from abc”
� INSERT with APPEND hint bypasses buffer cache and typically faster than conventional inserts
� Use parallelism – ie: query, dml, data load, replication, ... (bit.ly/eLFRQy)
SQL Optimization – the “unObvious”� Maria Colgan – Top Tips for Getting Optimal SQL
Execution All the Time
� Cardinality
» How to combat common causes for incorrect cardinality
� Access path
» What causes the wrong access path
� Join type
» Common causes for why the wrong join type was selected
� Join order
» Common causes for why the wrong join order was selected
� Implicit SQL – SAS code converted to SQL passthrough (Use Tactics for Pushing SQL to the Relational Database)
» 9.2M2 -significant improvements (inline views, SQL views, tables, expressions using CALCULATED keyword, SELECT, WHERE, HAVING, ON, GROUP BY, ORDER BY clauses)