Data warehousing with MySQL By Anand Pandey

1. Data warehousing with MySQLBy Anand Pandey MySQL MS-SQL Oracle DB2 Flat Files MySQL

2. Agenda • Introduction • Free and Open Source Software • Data Warehousing application • Extraction, Transformation and Loading • Partitioning and Storage Engine • Configuration Parameters • Business Intelligence • Summary • Q & A

3. Introduction MySQL AB develops and markets a family of high performance, affordable database servers and tools. MySQL is a key part of LAMP (Linux, Apache, MySQL, PHP / Perl / Python), a fast growing open source enterprise software stack. Anand Pandey, Senior Consultant, MySQL Inc. Josh Chamas, Senior Consultant, MySQL Inc.

4. Freeand Open Source Software MySQL is licensed under GPL. The GPL is a Free and Open Source Software (FOSS) license that grants licensees many rights to the software under the condition that, if they choose to share the software, or software built with GPL-licensed software, they share it under the same liberal terms.

5. Free and Open Source Software Quid Pro Quo MySQL has a dual license that works on a quid pro quo basis—i.e., if you're free, MySQL is free. If you're closed, you need a license.

6. Freeand Open Source Software Advantages of Open Source MySQL has 5 million plus active installation base. New releases immediately downloaded by users providing early feedback on bugs and features. Access to source code Write your own features/proprietary Storage Engine Freedom !

7. DataWarehousing application Data Warehouse is a relational database. It is designed for query and analysis rather than for transaction processing. It enables an organization to consolidate data from several resources.

8. DataWarehousing application Why DWH? • How to measure and manage your company's intangible assets? • How to leverage its data for competitive advantage ? • How to measure sales performance of previous year? • Which department produced the maximum profits in the current financial year? SOLUTION: Create and Manage Data Warehouse.

9. DataWarehousing application

10. A Typical Data Warehouse Data Source Staging Area DWH BI / OLTP MySQL Mining Oracle Meta Data Staging Database AWH SWH SWH Analysis MS-SQL Flat File Reporting DataWarehousing application

11. DataWarehousing application DWH Design • Identification of important things (Entities), their properties (Attributes) and relationship among them (ER modeling ). • Summary data is more important than individual transactions (Physical and Logical Design). • Use tools for modeling like ERWin and many others.

12. DataWarehousing application DWH Design • Most common schemas • Third Normal Form schema • Star schema • Snowflake schema • Most popular table structure • Fact Table • Dimensional tables

13. Transform Extraction ,Transformation and Loading Data Source Extract Load MERGE & BULK INSERT MERGE Tables Storage Indexes, Memory Views, Summary Staging Tables Users AWH SWH HEAP Perfor- mance OLTP/ BI

14. Extraction ,Transformation and Loading • Staging database • “LOAD DATA INFILE ….” Command. • Merging of SQLs • Segregating Informations • View enhancements • Index Enhancement • Memory Manipulation

15. Extraction, Transformation and Loading Staging Area and its benefits Relational Table structures are flattened to support extract processes in Staging Area. • First data is loaded into the temporary table and then to the main DB tables. • Reduces the required space during ETL. • Data can be distributed to any number of data marts

16. Partitioning and Storage Engine The MERGE Table • A collection of identical MyISAM tables used as one • You can use SELECT, DELETE, UPDATE, and INSERT on the collection of tables. • Use it when having large tables • DROP the MERGE table, you drop only the MERGE spec. • Advantage : manageability and performance MERGESALES Table Sales for Yr’04 Aug’04 Oct’04 Sep’04

17. Partitioning and Storage Engine MERGING based on month as Range JUN2004 JUN2004 - OCT2004 JUL2004 AUG2004 SEP2004 OCT2004

18. Partitioning and Storage Engine MERGE Table Example mysql> CREATE TABLE jan04 ( -> a INT NOT NULL AUTO_INCREMENT PRIMARY KEY, -> message CHAR(20)); mysql> CREATE TABLE feb04 ( -> a INT NOT NULL AUTO_INCREMENT PRIMARY KEY, -> message CHAR(20)); mysql> CREATE TABLE year04 ( -> a INT NOT NULL AUTO_INCREMENT, -> message CHAR(20), INDEX(a)) -> TYPE=MERGE UNION=(jan04,feb04) INSERT_METHOD=LAST;

19. Partitioning and Storage Engine MyISAM Storage Engine • Supports MERGE table. • Support fulltext indexing • “INSERT DELAYED ...” option very useful when clients can't wait for the INSERT to complete. Many client bundled together and written in one block • Compress MyISAM tables with “myisampack” to take up much less space. • Benefit from higher performance on SELECT statements

20. Partitioning and Storage Engine Restrictions on MERGE tables • You can use only identical MyISAM tables for a MERGE table. • MERGE tables use more file descriptors. If 10 clients are using a MERGE table that maps to 10 tables, the server uses (10*10) + 10 file descriptors. • Key reads are slower. When you read a key, the MERGE storage engine needs to issue a read on all underlying tables to check which one most closely matches the given key.

21. Partitioning and Storage Engine my.cnf parameters for DWH (example) • key_buffer = 1G • myisam_sort_buffer_size = 256M • sort_buffer = 5M • query_cache_type = 1 • query_cache_size = 100Mkey_buffer is the important one, this tells mysql how much memory to cap itself

22. Business Intelligence Using MySQL database server • Drastically reduce information retrieval by distributing data into replicated clusters. This enables parallel processing. • Tighter storage format (3 TB squeezed to 1TB) • Aggregate huge amount of data and deliver reports for OLAP • Relieve overloaded OLTP databases • Availability, scalability and throughput for the most demanding applications, and of course affordability

23. Summary • Free and Open Source under GPL • MyISAM Storage Engine • No Transactional Overhead • MERGE Table • Tighter storage format • Highly efficient

24. Any Questions? Anand and Josh