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Paper Index
Study on Index Selection Problem
A STUDY ON INDEX SELECTION PROBLEM
Abstract:
This paper is an attempt to understand the new methodology for collecting usage statistics at run time to develop the optimizer to estimate query execution costs for alternative index configurations that assists the database administrator in designing an index configuration for a relational database system and defining the workload specification required by an existing index design tools which may be very complex for a large integrated database system. However, one need to automatically derive the workload statistics and these are then used to efficiently compute an index configuration. This paper focuses on implementation of index recommendation, the user interface, and provides measurements on the quality of the recommended indexes.
1. Introduction
Relational database management systems (RDBMS) are by far the most popular database systems today on the other hand RDBMS is likely to dominate the commercial area for years to come, especially for business applications. Relational databases use indices to provide fast access to data. The presence of an index reduces the search time for indexed data items but also complicates update operations since the tuples as well as the indices must be updated.
The performance of queries in a relational database management system (RDBMS) has always been very sensitive to the indexes that exist on the tables in a database. The selection of indexes that would best serve a particular workload of queries. An index may have multiple columns as key columns, and the ordering of those columns is significant. In any real application can have tens of thousands of tables, each table can have hundreds of columns, and a typical workload can have thousands of queries, the number of possible indexes to consider is staggering. Finding the set of indexes that optimize a workload of complex, multi-table queries having varying importance and subject to resource constraints, is a daunting combinatorics challenge. Hence there is a tradeoff involved in selecting indices and indexing every column is rarely a good idea. This tradeoff decision will be referred to as the index selection problem (ISP).
A relational database consists of many stored relations and each stored relation can have many secondary indices. The index set of a (relational) database is the set of indices that are selected for the database. A cost function estimates the cost of processing a workload for a database with a given index set. Moreover, the costs of processing a workload depends on many factors, such as storage costs, number of page accesses, processor time, etc. We also assume that the cardinality of the relations remains constant. To be more precise, the frequency of tuple insertions and tuple deletions is such that the total number of tuples of each relation remains constant in two consecutive choices of index sets.
Workload on a relation:
Here, we distinguish four possible operations in the workload on a database; queries, updates, insertions and deletions. Each of these operations include one or more steps.
Query
1. Select the relevant tuples from the data pages
2. Output the relevant tuples to user
Update of tuples
1. Select the relevant tuples from the data pages
2. Update the specified attributes and rewrite the data pages
3. Update the relevant indices
Deletion of tuples
1. Select the relevant tuples from the data pages
2. Remove the tuples and rewrite the data pages
3. Update the relevant indices
Insertion of tuples
1. Select the location(s) where the tuples will be stored
2. Insert the tuples and rewrite the data pages
3. Update the relevant indices
We concentrate on steps that influence index selection. The first step of an operation of the workload is always the selection of the relevant tuple(s). The execution of this step clearly depends on the available set of indexes, so it has to be taken into account. The second step is never influenced by the availability of indexes, so can be ignored, while the third step, if present,
depends only on the presence of indexes.
Introduction of Indexex:
Index architecture
Index architectures can be classified clustered or unclustered.
UNCLUSTERED INDEX
SQL server creates a non-clustered index by default. The data is present in random order, but the logical ordering is specified by the index. The data rows may be randomly spread throughout the table. The non-clustered index tree contains the index keys in sorted order, with the leaf level of the index containing the pointer to the page and the row number in the data page. In non-clustered index:
- The physical order of the rows is not the same as the index order.
- Typically created on column used in JOIN, WHERE, and ORDER BY clauses.
- Good for tables whose values may be modified frequently.
CLUSTERED INDEX
Clustering alters the data block into a certain distinct order to match the index, hence it is also an operation on the data storage blocks as well as on the index. An address book ordered by first name resembles a clustered index in its structure and purpose. The exact operation of database systems vary, but because storing data is very redundant the row data can only be stored in one order. Therefore, only one clustered index can be created on a given database table. Clustered indexes can greatly increase overall speed of retrieval, but usually only where the data is accessed sequentially in the same or reverse order of the clustered index, or when a range of items is selected.
Formula for Number of Possible Indexes: Given a table with n columns, how many different indexes can exist containing k columns, where k <= n? There are n choices for the first column in the index. For the second column, there are n 1 remaining choices. As more columns are added, the total number becomes (n)(n - 1)(n - 2)……………(n - k +1) or n!=(n - k)!.
Therefore the total number of indexes that can be created on a table with n columns is
n
∑ n!/ (n - k)!
k=1
2. Types of Indices:
Types of indexes are
1.Primary key index vs Secondary index
2.Unique index vs Non unique index
3.Dense index vs Sparse index
4.Hash index
5.Function based index
6.B-tree index.
7.Virtual index
8. bitmap index
In general two types of indices can be distinguished, namely primary and secondary indices. In the case of a primary index, the tuples in the relation are ordered on the indexed attribute. This is not the case for a secondary index; from now on we concentrate on secondary indices.
Index Selection Problem (ISP):
The formalization of the index selection problem provides insight into its dfficulty, but the results are valid for special cases only and there is no methodology presented for finding an index configuration for the general case.
There are also some general problems with analytical approaches to the ISP.
First, substantial simplifications have to be made to derive an analytical solution.
Second, the model becomes obsolete if there are changes to the query processing strategy
or to other modeled aspects of the DBMS.
Index Selection Method and Database Relation
In single-index multiple-relation index selection method based on a set of join methods that is separable. This property reduces the index selection problem to finding a locally optimal index configuration for each relation. The set of join methods is reduced to two because these are the only ones adhering to separability. It is unclear if the advantage of a better index configuration outweighs the disadvantage of not using ecient join methods which would otherwise be available. The usage input consists of a weighted set of queries. The general problem with this form of usage input is that the "representative" query set might not be representative of the real workload because it has to be of moderate size for complexity reasons.
Where as single-index single relation approach is unrealistic for real-life databases. Here index selection means that the system adopts the current index configuration based on automatically gathered statistics so that users do not even have to know about the concept of indices. An example for the database usage statistics used are the restrictive clauses for every query.
2. Why is Index Selection hard?
Despite a long history of work in the area of index selection, there are no significant
commercial products that do automatic index selection and are widely deployed. Several factors make the task of automating physical design extremely hard.
First, when viewed as a search problem, the space of alternatives for indexes is very large. A database may have many tables and each table may have many columns that need to be considered for indexing. An index may be clustered or non-clustered. Indexes may have different physical structures, e.g. B+-tree, hash, bitmap. When multi-column indexes are considered the search space increases even more dramatically, since for a given set of k columns, k! multi-column indexes are possible.
Second, index selection tools of the past have frequently followed the "textbook solution" of taking semantic information such as uniqueness, reference constraints and rudimentary statistics to produce a database design. Such designs may perform
poorly because they ignore valuable workload information.
Third, even when index selection tools have taken the workload into account, they suffer
from being disconnected from the query optimizer. These tools adopt an expert
system like approach, where the knowledge of "good" designs are encoded as rules and are used to come up with a design. This has adverse ramifications for two reasons. First, a selection of indexes is only as good as the optimizer that uses it. In other words, if the optimizer does not consider a particular index for a query, then its presence in the database does not benefit that query. Second, these tools operate on their private model of the query optimizer's index usage.
While making an accurate model of the optimizer is itself hard, ensuring consistency between the assumptions made by the tool and the query optimizer is a software-engineering nightmare
3. Index selection
The index selection problem has been identified as a variation of the Knapsack Problem, and there are several designs for index recommendations based on optimization rules.
Solution for Index Selection
The overall goal of this work is to develop a flexible index selection framework that can be tuned to achieve effective static index selection and online index selection for
high-dimensional data under different analysis constraints.
For the static index selection, when no constraints are specified, the goal is to recommend the set of indexes that yields the lowest estimated cost for every query in a workload for any query that can benefit from an index. In cases where a constraint is specified either as the minimum number of indexes or a time constraint, we want to
recommend a set of indexes within the constraint, from which the queries can benefit the most. When there is a time constraint, we need to automatically adjust the analysis parameters to increase the speed of analysis.
For the online index selection, the goal is to develop a system that can recommend an evolving set of indexes for incoming queries over time such that the benefit of index set changes outweighs the cost of making those changes. Therefore, an online index selection system that differentiates between low-cost index set changes and higher cost index set changes and can also make decisions about index set changes based on different cost-benefit thresholds is desirable.
While maintaining the original query information for later use to determine the estimated query cost, we apply one abstraction to the query workload to convert each query into the set of attributes referenced in the query. We perform frequent item set mining over this abstraction and only consider those sets of attributes that meet a certain
support to be potential indexes. By varying the support, we affect the speed of index selection and the ratio of queries that are covered by potential indexes. We further prune the analysis space using association rule mining by eliminating those subsets above a certain confidence threshold. Lowering the confidence threshold improves the analysis time by eliminating some lower dimensional indexes from consideration but can result in recommending indexes that cover a strict superset of the queried attributes.
Our technique differs from existing tools in the method that we use to determine the potential set of indexes to evaluate and in the quantization-based technique that we use to estimate query costs. All of the commercial index wizards work in design time. The DBA has to decide when to run this wizard and over which workload. The assumption is that the workload is going to remain static over time, and in case it changes, the DBA would collect the new workload and run the wizard again.
Static Index Selection Approach
The goal of the index selection is to minimize the cost of the queries in the workload, given certain constraints. Given a query workload, a data set, the indexing constraints, and several analysis parameters, our framework produces a set of suggested indexes as an output.
Online Index Selection Approach
The online index selection is motivated by the fact that query patterns can change over time. By monitoring the query workload and detecting when there is a change on the query pattern that generated the existing set of indexes, we are able to maintain good performance as query patterns evolve. In our approach, we use control feedback to monitor the performance of the current set of indexes for incoming queries and determine when adjustments should be made to the index set. In a typical control feedback system, the output of a system is monitored, and based on some functions involving the input and output, the input to the system is readjusted through a control feedback loop.
Conclusion
A flexible technique for index selection is introduced, which can be tuned to achieve different levels of constraints and analysis complexity.Index creation is quite time consuming. It is not feasible to perform real-time analysis of incoming queries and
generate new indexes when the patterns change. Potential indexes could be generated prior to receiving new queries and, when indicated by the online analysis, moved to the
active status.
References
- Ramakrishnan and Gehrke: Database Management Systems.
- Data Mining: Concepts and Techniques : Micheline Kamber, Jiawei Han
- http://citeseerx.ist.psu.edu/
- Article by K. Whang, "Index Selection in Relational Databases,"
About the Author
Lecturer, Dept of Informatics Alluri Institute of management sciences, warangal.
What is the name of that folded paper game?
There's a game that kids play with folded paper and writing numbers and words inside. It kinda looks like a fortune cookie.
You put your index finger and thumb inside the paper and open it one way, then another way and count...
I am trying to prepare a game for a class I'm teaching and I want to find instructions on how to fold the paper. I had a book of activities that showed it but I gave it away a long time ago and I just cannot remember the name of the game to find more info.
Thanks.
It's the oragami fortune teller, they have online directions ( http://www.funorama.com/fortune.html )or you can buy kits ( http://www.origamigameskit.com/index2.html )
Hope this helps
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