1. Chapter 6: Database Evolution Title: AutoAdmin “What-if” Index Analysis Utility Authors: Surajit Chaudhuri, Vivek Narasayya ACM SIGMOD 1998

2. AutoAdmin “What-if” Index Analysis Utility Problem –Problem Statement –Why is this problem important? –Why is this problem hard? Approaches –Approach description, key concepts –Contributions (novelty, improved) –Assumptions

3. Problem Statement – Index Selection Given –Database, i.e. tables –A Database Management System, i.e. SQL Server Find –Index Selection Tool to compare alternate indexes by estimated processing cost of workload queries –Tools to define workload queries, alternative indices, … Objectives –Assist database administrators, i.e. reduce manual effort Constraints –Avoid physical alternation to Databases –Avoid scan of large tables to create statistics for query optimizer

4. Why is this problem important? Total cost of ownership –Database administration is a significant component –Annual salaries compare with cost of software and hardware Thus, DBA time is valuable –Tools to assist DBA produce significant savings DBA perform many services –Performance tuning via Index selection –Other, e.g. recovery, upgrade, …

5. Why is this problem Hard? Index selection is intrinsically a hard search problem. –A large # of possible single and multi-column indexes –Various types of indexes –Variety of usage by query optimizer e.g., indexed-only access Trade-off between read and update queries –Cost of update, insert, delete, bulk load may go up! –Impact analysis is required

6. Novelty of Contribution Limitations of Related Work –Lack of impact analysis –Reliance of large physical changes to databases Ex. ‘views’ in simulating hypothetical database [Stonebraker] Very computation intensive Contributions –Built ‘index selection’ & ‘index analysis’ utility. (This paper more focuses on index analysis utility.) –Hypothetical index structures –Enables a large class of analysis at low cost. Query optimizer to evaluate indexes Sampling to collect statistics

7. Basic Concepts Ideas for modeling –‘Workload’ = a set of SQL statements –‘Configuration’ = a set of indexes –Hypothetical Configuration Analysis (HCA) Summary analysis on simulation results Ideas for efficient implementation –Simulate a hypothetical configuration estimate the cost of queries in the workload –Sampling to estimate statistics needed by optimizer

8. Key Concepts - Simulating Configurations Simulating Hypothetical Configurations to Estimate –(a) the cost of queries in the workload: is relative to the total cost of the workload in the current configuration. –(b) usage of indexes: represents the indexes that are expected to be used by the server to answer the query if the hypothetical configuration exists

9. Key Concepts - Sampling Strategy Used for creating hypothetical indexes. –Accuracy – Figure 6 –Cost – Figure 5 Details - adaptive page-level sampling algorithm –Psuedo-code (fig.. 3)

10. Key Concepts - Architecture

11. Validation Methodology Prototyped index analysis utility –for Microsoft SQL Server 7.0 Case Study –Workload – TPC-D – Decision Support benchmark –Usage analysis Figure 10. Usage of each index in configuration for the workload Figure 11. – Distribution of selection conditions on a given table –Comparison of two configuration by cost Figure 13 – For 10 most expensive queries Figure 14 – By SQL Statement types

12. Examples of Summary Analysis Distribution of workload by SQL type Distribution of conditions over tables

13. Summary Paper’s focus –Index Analysis Utility Ideas –Simulating hypothetical indexes can estimate the cost of queries and usage of indexes. Contributions –The first of its kind, index analysis utility with low cost –Presented efficient mechanism for implementation Analytical Validation –Case study with Microsoft SQL Server 7.0

14. Assumptions, Rewrite today Assumptions –The simulation represents the real behavior of database system. Rewrite today –Compare with newer methods DB2 Design Advisor –Experimental evaluation To measure the efficiency of the index analysis utility