1. On-line Index Selection for Physical Database Tuning
Karl Schnaitter
UCSC & Aster Data
Advisor: Neoklis Polyzotis
ISSDM Mentor: John Bent
SRL/ISSDM Symposium 2010

2. Index Tuning for Databases
SELECT b FROM R WHERE a = 10
Execution Plan 1 R a b 5
“blue” 10
“red” 97
“green”
· · ·
Reads whole table
Discards rows where a ≠ 10
Returns b from remaining rows
Table Scan
· · ·
Execution Plan 2 R a b 5
“blue” 10
“red” 97
“green”
· · ·
Only accesses rows where a = 10
May be much faster than a table scan
Index Scan
index(R(a))

3. Introduction The index selection problem for databases:
The goal is to choose the indexes that improve database performance
A crucial part of performance tuning
Index selection is very challenging
Query benefit must be weighed against the cost of creation, maintenance and storage
Dynamic query loads add more difficulty
In practice, index selection is typically handled by an experienced database administrator

4. Previous Work Auto-index tuning is an active research topic
Hammer and Chan, SIGMOD 1976
Finkelstein et al., TODS 1988
Chaudhuri and Narasaya, VLDB 1997
Bruno and Chaudhuri, ICDE 2007
…and numerous other references

5. Off-line Index Advisor
Off-line Tuning
Tuning
Workload
User
Actual
Workload
Off-line Index Advisor
Recommended
Indexes
DBA
Create/Drop
Indexes
DBA controls index maintenance
DBA must know the workload in advance
Limited support for interactive tuning
What-if Optimizer
Database Server

6. On-line Tuning Tuning is based on actual workload
On-line Index Advisor
DBA
User
Tuning is based on actual workload
DBA is out of the loop
Performance can become unpredictable
What-if Optimizer
Create/Drop
Indexes
Actual
Workload
Database Server

7. Our Proposal: Semi-automatic Tuning
Feedback
Semi-automatic Index Advisor
Recommended
Indexes
User
DBA
Create/Drop
Indexes
What-if Optimizer
Actual
Workload
Database Server

8. Semi-automatic Index Advisor
Example Session
I like a; I want d
I don’t like b
Semi-automatic Index Advisor
{a, b, c}
User
DBA
{a, d, e, c}
{a, f, c}
DBA makes decisions on index-maintenance
Workload is analyzed online
DBA expertise is coupled with index advisor
What-if Optimizer
Create/Drop
Indexes
Actual
Workload
Database Server

9. Our Proposal: Semi-automatic Tuning
Feedback
Semi-automatic Index Advisor
Recommended
Indexes
User
DBA
Create/Drop
Indexes
What-if Optimizer
Actual
Workload
Database Server

10. Generating Recommendations
workload
candidate selection
User
index candidates
Work Function Algorithm (WFA) of Borodin & El Yaniv 1998
+ performance guarantees
- exponential complexity
recommendation logic
Work Function
Algorithm
recommended index set
DBA

11. Example of Index Interaction
SELECT b FROM R WHERE a = 10
Execution Plan 1 R a b 5
“blue” 10
“red” 97
“green”
· · ·
Only accesses rows where a = 10
May be much faster than a table scan
Index Scan
index(R(a))
Execution Plan 2
++ Example of index interaction
Only accesses index
If plan 2 is cheaper, the index on R.a becomes obsolete
Index-Only Scan
index(R(a,b))

12. Interaction-Based Partitioning
Consider an undirected graph
Vertex for each candidate index
Edge between interacting indexes
++ Describe stable partitioning
We call this a stable partition of the indexes
Different components have independent benefits

13. Improvement: Candidate Partitioning
workload
candidate selection
partitioned index candidates
User
recommendation logic
Need to handle feedback
WFA
WFA
WFA
recommended index set
DBA

14. Feedback Model User provides votes for some indexes
Positive votes for indexes they want
Negative votes for indexes they don’t want
Votes may be explicit
E.g., user may reject the system’s recommendation to create an index
Votes may also be implicit
Creating an index casts a positive vote
Dropping an index casts a negative vote
++ What is the interface for feedback

15. Handling Feedback
Votes create a soft constraint on future recommendations
Include indexes with positive votes
Exclude indexes with negative votes
Reverse these decisions if sufficient evidence is seen after the votes were cast
++ High-level description of how feedback is taken into account

16. partitioned index candidates
Final Solution: WFIT
workload
candidate selection
partitioned index candidates
User
recommendation logic
feedback
WFA
WFA
WFA
recommended index set
DBA

17. Behavior of WFIT with Feedback
“Good” votes follow the best indexes chosen off-line
“Bad” votes are opposite of “Good” votes
Normalized against optimal schedule
++ Example of empirical results

18. Conclusions
Semi-automatic tuning is a promising new approach to physical database design
Combines best features of existing approaches
User feedback is the key ingredient
We recommend indexes with provable performance guarantees
Using work function algorithm as the foundation
Future work may include:
More efficient workload analysis
A GUI frontend to present recommendations