Last Updated : 27 th April 2004 Center of Excellence Data Warehousing Group Teradata Physical Database Design Considerations.

Slides:



Advertisements
Similar presentations
Indexing Techniques CS 543 – Data Warehousing. CS Data Warehousing (Sp ) - Asim LUMS2 Indexing Goal: Increase efficiency of data.
Advertisements

9/26/2000SIMS 257: Database Management Physical Database Design University of California, Berkeley School of Information Management and Systems SIMS 257:
Database Systems: A Practical Approach to Design, Implementation and Management International Computer Science S. Carolyn Begg, Thomas Connolly Lecture.
Chapter Physical Database Design Methodology Software & Hardware Mapping Logical Design to DBMS Physical Implementation Security Implementation Monitoring.
IS 4420 Database Fundamentals Chapter 6: Physical Database Design and Performance Leon Chen.
ACS-4902 Ron McFadyen Chapter 15 Algorithms for Query Processing and Optimization.
E.G.M. PetrakisHashing1 Hashing on the Disk  Keys are stored in “disk pages” (“buckets”)  several records fit within one page  Retrieval:  find address.
Chapter 8 Physical Database Design. McGraw-Hill/Irwin © 2004 The McGraw-Hill Companies, Inc. All rights reserved. Outline Overview of Physical Database.
Chapter 17 Methodology – Physical Database Design for Relational Databases Transparencies © Pearson Education Limited 1995, 2005.
IT The Relational DBMS Section 06. Relational Database Theory Physical Database Design.
Lecture 8 Index Organized Tables Clusters Index compression
1 Physical Data Organization and Indexing Lecture 14.
1 © Prentice Hall, 2002 Physical Database Design Dr. Bijoy Bordoloi.
1 IT420: Database Management and Organization Storage and Indexing 14 April 2006 Adina Crăiniceanu
Lecture 9 Methodology – Physical Database Design for Relational Databases.
Physical Database Design & Performance. Optimizing for Query Performance For DBs with high retrieval traffic as compared to maintenance traffic, optimizing.
TM 7-1 Copyright © 1999 Addison Wesley Longman, Inc. Physical Database Design.
Physical Database Design Chapter 6. Physical Design and implementation 1.Translate global logical data model for target DBMS  1.1Design base relations.
September 2011Copyright 2011 Teradata Corporation1 Teradata Columnar.
CSCE Database Systems Chapter 15: Query Execution 1.
Chapter 16 Methodology – Physical Database Design for Relational Databases.
1 © Prentice Hall, 2002 Chapter 6: Physical Database Design and Performance Modern Database Management 6 th Edition Jeffrey A. Hoffer, Mary B. Prescott,
Chapter 6 1 © Prentice Hall, 2002 The Physical Design Stage of SDLC (figures 2.4, 2.5 revisited) Project Identification and Selection Project Initiation.
1 Index Structures. 2 Chapter : Objectives Types of Single-level Ordered Indexes Primary Indexes Clustering Indexes Secondary Indexes Multilevel Indexes.
Chapter 16 Practical Database Design and Tuning Copyright © 2004 Pearson Education, Inc.
1 CS 430 Database Theory Winter 2005 Lecture 16: Inside a DBMS.
© Pearson Education Limited, Chapter 13 Physical Database Design – Step 4 (Choose File Organizations and Indexes) Transparencies.
10/10/2012ISC239 Isabelle Bichindaritz1 Physical Database Design.
Indexing and hashing Azita Keshmiri CS 157B. Basic concept An index for a file in a database system works the same way as the index in text book. For.
Methodology – Physical Database Design for Relational Databases.
Indexes and Views Unit 7.
University of Sunderland COM 220 Lecture Ten Slide 1 Database Performance.
Physical Database Design Purpose- translate the logical description of data into the technical specifications for storing and retrieving data Goal - create.
Lec 7 Practical Database Design and Tuning Copyright © 2004 Pearson Education, Inc.
Chapter 8 Physical Database Design. Outline Overview of Physical Database Design Inputs of Physical Database Design File Structures Query Optimization.
Chapter 5 Index and Clustering
Indexes CSE2132 Database Systems Week 11 Lecture Indexes.
Advanced Database Concepts
Last Updated : 27 th April 2004 Center of Excellence Data Warehousing Group Teradata Performance Optimization.
Creating Indexes on Tables An index provides quick access to data in a table, based on the values in specified columns. A table can have more than one.
Last Updated : 12 th April 2004 Center of Excellence Data Warehousing Group Overview of Teradata Utilities.
1 Overview of Query Evaluation Chapter Outline  Query Optimization Overview  Algorithm for Relational Operations.
10/3/2017 Chapter 6 Index Structures.
Practical Database Design and Tuning
Indexes By Adrienne Watt.
Indexing Structures for Files and Physical Database Design
Record Storage, File Organization, and Indexes
Parallel Databases.
Physical Database Design and Performance
Choosing Access Path The basic methods.
Teradata Join Processing
COMP 430 Intro. to Database Systems
Methodology – Physical Database Design for Relational Databases
Database Management Systems (CS 564)
Accessing nearby copies of replicated objects
Teradata Physical Implementation – Case Study
CHAPTER 5: PHYSICAL DATABASE DESIGN AND PERFORMANCE
國立臺北科技大學 課程:資料庫系統 fall Chapter 18
Session #, Speaker Name Indexing Chapter 8 11/19/2018.
Physical Database Design
Chapter 6: Physical Database Design and Performance
Practical Database Design and Tuning
Indexing and Hashing Basic Concepts Ordered Indices
Introduction to Teradata
The Physical Design Stage of SDLC (figures 2.4, 2.5 revisited)
Indexing and Hashing B.Ramamurthy Chapter 11 2/5/2019 B.Ramamurthy.
File Storage and Indexing
INDEXING.
Physical Storage Structures
Database Administration
Presentation transcript:

Last Updated : 27 th April 2004 Center of Excellence Data Warehousing Group Teradata Physical Database Design Considerations

Primary Index Choice Criteria Access Demographics – Choose the column most frequently used for access to maximize the number of one AMP operations. Distribution Demographics – Better distribution optimizes parallel processing. Volatility – Changing PI may cause the row itself to be moved to another AMP. Stable PI reduces data movement overhead.

UPI and NUPI UPI  Best distribution due to unique value.  One AMP operation and uses only one I/O.  Best performance. NUPI  Good distribution for ‘near unique’ values.  Duplicate PI rows goes to same block. No extra I/O if all duplicate rows fit in single block.  Duplicate row check required if there is no USI defined.  Multiple I/Os required if rows do not fit in a single data block.

UPI and NUPI (cont.) Highly non-unique values cause skewed distribution. Highly non-unique values cause extra overhead in duplicate row check. Define a USI on the table whenever you have a NUPI. Note : FastLoad and Multiload can not load a table with USI defined.

UPI and NUPI (cont.) Multi-Column PI gives better distribution. But as the number of column increases the index becomes less usable. Partial values can not be used for PI access. Do not include a column for index selection that does not improve the selectivity of the index.

Secondary Indexes Secondary Index values are stored in sub tables. May be unique or non unique. Teradata implements USI and NUSI differently. SI ValueBT Row ID SI valueBT Row ID Sec. Index value Hash Algorithm Base Table Index Subtable

Secondary Indexes USI are hash distributed across all AMPs.  Sub table rows may reside in a AMP other than the base table row.  USI access involved two-AMP operation. NUSI are implemented on a AMP local basis.  Sub table rows located in the same AMP of base table rows.  NUSI access involved all-AMP operation.

Secondary Index Considerations Need additional storage to hold sub-table. Need additional I/O. Choose columns for NUSI candidate only those having frequent access. If “COLLECTed STATISTICS” are not available Teradata may not choose NUSI as the access path. Use EXPLAIN facility to see the plan chosen by the optimizer.

NUSI Bit Mapping Used when multiple NUSI are being used will AND condition. Identifies common Row Ids in the satisfied by the query before retrieving the base table rows. Indx1 Indx2 Multiple-column secondary indexes are less usable. Define multiple secondary indexes to allow bit mapping.

Row Access Methods PI Value Hashing Algorithm Base Table Sub Table NUSI Value Hashing Algorithm USI Value Hashing Algorithm Value Hashing Algorithm PI/NUPI USINUSIFTS

Questions ?