Business Analytics with IDS. Fred Ho, IDS Development

Transcription

1 Business Analytics with IDS Fred Ho, IDS Development

2 Disclaimer Copyright IBM Corporation All rights reserved. U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. THE INFORMATION CONTAINED IN THIS PRESENTATION IS PROVIDED FOR INFORMATIONAL PURPOSES ONLY. WHILE EFFORTS WERE MADE TO VERIFY THE COMPLETENESS AND ACCURACY OF THE INFORMATION CONTAINED IN THIS PRESENTATION, IT IS PROVIDED AS IS WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED. IN ADDITION, THIS INFORMATION IS BASED ON IBM S CURRENT PRODUCT PLANS AND STRATEGY, WHICH ARE SUBJECT TO CHANGE BY IBM WITHOUT NOTICE. IBM SHALL NOT BE RESPONSIBLE FOR ANY DAMAGES ARISING OUT OF THE USE OF, OR OTHERWISE RELATED TO, THIS PRESENTATION OR ANY OTHER DOCUMENTATION. NOTHING CONTAINED IN THIS PRESENTATION IS INTENDED TO, NOR SHALL HAVE THE EFFECT OF, CREATING ANY WARRANTIES OR REPRESENTATIONS FROM IBM (OR ITS SUPPLIERS OR LICENSORS), OR ALTERING THE TERMS AND CONDITIONS OF ANY AGREEMENT OR LICENSE GOVERNING THE USE OF IBM PRODUCTS AND/OR SOFTWARE. IBM, the IBM logo, ibm.com, Informix, solid, DataMirror, Optim, Cognos are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both. If these and other IBM trademarked terms are marked on their first occurrence in this information with a trademark symbol ( or ), these symbols indicate U.S. registered or common law trademarks owned by IBM at the time this information was published. Such trademarks may also be registered or common law trademarks in other countries. Other company, product, or service names may be trademarks or service marks of others.

3 Contents Definition of BI/DW/BA Types of IDS BI Users OLTP vs. Data Warehousing Informix Warehouse IDS Storage Optimization Your Feedback and Requirements

4 Business Intelligence A set of concepts and methodologies to improve decision making in business through use of facts and fact-based systems..howard Dresner, The Gartner Group The processes, technologies, and tools needed to turn data into information, information into knowledge, and knowledge into plans that drive profitable business actions.david Loshin, Business Intelligence: The Savvy Manager s Guide The foundation that enables BI is the enterprise architecture business, data, and technology. A well-implemented data warehousing program provides much of that foundation.

5 Data Warehousing A data warehouse is a subject-oriented, integrated, non-volatile, time variant collection of data organized to support management needs.w H Inmon The Data Warehouse is nothing more than the union of all the constituent data marts.ralph Kimball, et al, The Data Warehouse Life Cycle Toolkit The data warehousing process turns raw data into potentially valuable information usable by people and systems. Warehousing enhances data assets value by: Applying standards and consistency to the data Organizing the data into subject areas that cross business functional lines Integrating the data Enforcing data consistency over time to provide meaningful history Acting as a stable and reliable source Providing easy access to data

6 Business Analytics The process of using information to enhance knowledge and apply that knowledge to help a business achieve its objectives. Analytic applications provide tools to facilitate the business analytics process. Business Metrics and Business Management Business Process Management Business Performance Management Business Activity Monitoring Customer Relationship Management Supply Chain Management Performance Dashboards for Information Delivery Real-time (or near Real-time) Monitoring Scorecards for Information Delivery Monitoring history & trends Analytic Applications for Information Delivery Customer Analysis, Marketplace Analysis, Sales Channel Analysis,

7 Range of Business Analytics High Prediction Using Predictive Analysis tools Complexity Monitoring Analysis Using Dashboards & Scorecards Using OLAP & Virtualization tools Reporting Using Query, Reporting and search tools Low Business Value Source: TDWI High

8 IDS in BI/Warehousing Given the IDS Characteristics of Reliability, High Availability, Performance, Ease of Use, why isn t IDS in this space? IDS has traditionally been viewed as an OLTP solution However, there a lot more warehousing users on IDS than one realizes! Some customers have implemented IDS warehouses at Terabyte levels There are a lot of features already in IDS that make it suitable for BI/Warehousing BI tools have become very sophisticated over the years We recognize the need to provide better warehousing capabilities for IDS users

9 What s Available? IDS Warehousing Features Performance & Scalability Inherent SMP Multi-threading Parallel Data Query (PDQ) Light Scan for fast table scans Online Index build Efficient Hash Joins Auto Fragment Elimination Memory Grant Manager (MGM) High Performance Loader Optimistic Concurrency Easy of Management Time cyclic data management using Range Partitioning OPTCOMPIND optimization

10 BI Users Classification 1. BI on Existing OLTP Schema (Operational BI) 2. BI on Star Schema (Data Mart) 3. BI in a Mix-Workload Environment 4. Enterprise BI

11 Type 1: BI/Analytics on OLTP Schema Majority of today s IDS customers have the need to do BI/ Analytics on their existing IDS (OLTP) database. They currently use a combination of 4GL programs, Excel, and BI tools (Business Objects, Cognos, Crystal Reports) Custom code and maintenance required by customer Performance may be acceptable even on an OLTP schema Allows for operational BI

12 OLTP vs. Data Warehousing Workload Short Transactions Relatively simple SQL Random Updates Few Rows accessed Sub-second response time ER Modeling Minimizes redundancy Normalized data (5NF) Minimizes duplicates Few indexes Avoids index maintenance Pre-compiled queries Repeated execution of queries Longer Transactions Complex SQL with analytics Sequential Updates Many Rows Accessed Secs to Mins response time Dimensional Modeling OK to have redundancy De-normalized data (3NF) Duplicates are OK OK to have more indexes Mostly read only Ad-hoc queries Unpredictable load

13 Type 2. BI/Analytics on IDS on Star Schema Transform OLTP database into Star Schema database Better performance for data warehousing and dimensional queries Star Schema database may be on a separate machine/domain Suitable for customers building separate data mart Use IDS as is against Star Schema

14 What s Available? BI Tools The Performance Management Framework Cognos identifies best-practice decision areas, or information sweet spots by business function: Cognos 8 provides a comprehensive set of BI tools for: Reporting Analysis Dashboards Scorecards Performance Management Framework for: Solutions for different areas of the organization

15 Cognos Business Intelligence and Performance Management One Platform, One Architecture Industry and Functional Solutions Complete Coverage of all capabilities Enterprise-Class SOA Platform

16 Data Warehouse Architecture

17 SQL Warehousing Tool Overview Warehousing Process Design Studio Admin Console Summary

18 SQL Warehousing Tools Overview Typical process Identify requirements Data Architect Define data transformation (ETL/ELT) process SQL/ETL developer Development of sql/shell scripts SQL/ETL developer Deployment in production system Application Architect, DBA Reporting Business user Refine requirements Challenges Dynamic requirements Constantly refinement Multiple roles, tools Each have different perspective Communication cost/ information loss Unreadable, hard-to-debug scripts Poor productivity SQW Solution Data Modeling Physical Data Model (Reverse engineering, New from scratch, generate DDL), compare & sync Data Flows Visual Design Optimized SQL code generation Control flow supports programming logic Admin Console Schedule, Monitor, Parameterized values Eclipse free reporting tool e.g. BIRT Reusable flows Easy refinement Copy & paste, refactor Values Easy to design & reuse Increased productivity Integrated tools Seamless integration inside Eclipse Auto generated code from visualized flows Optimized SQL code Impact analysis for any data model change

19 SQW Architecture Design Center Design Studio DESIGN DEPLOY (Eclipse) Data Flows + Control Flows Deployment preparation Databases IDS SQW Execution DB IDS Warehouse DB SQL Server Oracle DB2 Data Source Deployment package User scripts Code Units Build Profile IDS Admin Console RUNTIME Deploy HTTP service (WAS ) SQW Runtime Applications Deploy Execution Other Servers (DataStage) SQW Control DB

20 SQW: Design Studio Design Studio Eclipse based IDE Integrated tools, shell sharing Team development CVS, clearcase for checkin/checkout projects, flows Data Warehousing Project Data Models Data Flows Control Flows Warehouse Applications (deployment packages) Subflow & Subprocess (reusable flow module) Variables Data Source Explorer Database connections to multiple vendors, e.g. Informix, DB2 LUW, Oracle, SQL Server, MySQL, DB2 z/os DataStage Servers Integration with IBM DataStage

21 SQW: Data Modeling Physical Data Model Visualized data modeling Impact analysis Reverse engineering or new from scratch Compare & sync Generate DDL Overview diagram Shell Sharing with Rational Data Architect & other Data Studio products

22 SQW: Data Flows File source Table join Table target aggregation Table source Data Flow Operators: -- source & target operators (table, file) -- SQL Transformation operators -- Warehousing operators

23 SQW: Data Flows A simple flow Generated SQL code -- optimization across SQL statements. -- optimized staging strategy -- in-database transformation

24 SQW: Control Flows Control flow Common utility operators Control logic, parallel execution, loop iteration Error handling

25 SQW Overview Design Studio Eclipse Based Design Environment Application package (zip file) deployment profile (database connections, machine resources, variable definitions, DDL files etc..) Generated code create deploy Admin Console Production Environment in Websphere manage Manage warehouse applications Schedule Monitor

26 Admin Console Flex RIA based Warehouse Admin Console Admin Console manages common resources (e.g. databases connections, ftp servers, datastage servers) Schedule & monitor warehouse processes

27 XPS Customers Looking to Migrate to IDS External Tables XPS style loader for easy migration Partitioning Strategies Auto fragmentation Fragment Advisor Fragment stats Update Truncate Fragments Primary Storage Manager (PSM) For simpler, easier management of backups (replacing ISM) Merge UpSert capabilities * Features to be included in the next release(s)

28 Using Mach11 for OLTP/Warehousing in IDS Use Separate Boxes OLTP Apps OLTP Database (ETL) Data Warehouse Database SQW Use MACH 11 OR Blade Server OLTP Apps SQW User transparency Connection Manager OLTP Node Group SQW Node Group Primary SDS SDS SDS MACH 11 Single database view Shared Disk

29 IDS Storage Optimization Now Available as of 11.50xC4 Deep Compression + Storage Optimization

30 Row Compression Concepts Compression looks for repeating patterns across the entire table When pattern found, string replaced by a 12 bit symbol Symbols are stored in a dictionary for fast lookup Data resides compressed on pages (both on-disk and in bufferpool) Significant I/O bandwidth savings better performance Significant memory savings more efficient memory utilization Some CPU overhead costs Rows must be uncompressed before being processed for evaluation

31 Row Compression Using a Compression Dictionary Dictionary contains repeated information from the rows in the table Compression candidates can be across column boundaries or within columns Dictionary PartCode SPart Quantity LotNum BinLoc Aisle ANCPRPLT 220J 200 Z165-3 NE SNCPRPLT 580T 132 Z165-3 NE NCPRPLT 02 Z165-3NE ANCPRPLT 220J 200 Z165-3 NE SNCPRPLT 580T 132 Z165-3 NE Animated Slide A (01) 220J 200 (02) S (01) 580T 132 (02) A (01) 220 J 200 (02) S (01) 580 T 132 (02)

32 Storage savings Tables will often compress in the range of 60% - 80% Overall database storage savings will be between 40% and 50% That s 50% less disk space needed to support IDS 11 database! 78% Smaller 81% Smaller Sales Table Product Table

33 Performance Benefit Performance can be improved using compression Many queries will benefit from compression with fewer I/Os Consumes more CPU - most customers not 100% CPU bound Lab tests show I/O bound workloads improve by 30-40% Many utility (backup and recovery for example) will be faster 2x as fast in some cases as the database may now be ½ the size 40% Faster

34 IDS 11 Compression Operations estimate_compression Estimates compression ratio on a table create_dictionary Creates compression dictionary for a table compress Does implicit create_dictionary and compress all previous data uncompress Uncompress the table and deactivates compression uncompress_offline XLOCK table and uncompress it. Also deactivates compression purge_dictionary Delete old inactive dictionaries

35 Storage Optimization Operations repack Move rows within a table or fragment to consolidate free space repack_offline XLOCK the table and move rows within a table or fragment to consolidate free space shrink Return free space at end of table or fragment to the dbspace Normally done after a repack

36 Compression On Data Page With Multiple Rows compress Dictionary repack Multiple Compressed Pages shrink Uncompressed Compressed Compressed Animated Slide Empty Data Pages

37 Admin API Interface All compression and storage optimization operations are invoked via the IDS Admin API built-in UDRs execute function task( ); execute function admin( ); Example execute function task ( table compress repack shrink, table_name, database_name, owner_name );

38 Features That Cannot Be Compressed Out-of-row data (e.g. blobs) Indexes Temp tables Catalog tables (Data Dictionary) Partition tables (Tablespace Tablespace) Dictionary Partitions Tables in the following databases: Sysmaster Sysutils Sysuser Syscdr Syscdcv1

39 HDR, ER, CDC (DataMirror) and Compression All are supported on compressed tables HDR Tables will be compressed on secondary iff they are compressed on primary ER Compression status of tables is independent between source and target, specified by user CDC Compression of targets is a function of what the target database supports and what use specifies

40 Summary Storage optimization through IDS 11 compression can save 40-50% of your database storage requirements For IO-bound workloads Compression can also improve performance You not only see your online database shrink but often more importantly, your backup storage and disaster recovery storage is cut in half as well In real customer examples storage savings are realized and performance benefits are apparent Add in the time savings with utilities processing (particularly database backup and recover time is cut in half) and you can see the benefits of IDS 11 compression

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