Query Optimization Techniques and Performance Issues in XML - PowerPoint Presentation

Slide1

Query Optimization Techniques and Performance Issues in XML and Parallel databases

CSE 8330

Instructor:

Dr.Margaret

H. Dunham

Presenter:

Akshaya

AradhyaSlide2

IntroductionQuery optimization in XML databasesQuery optimization in Parallel databases

Comparison

Conclusion and Future workBibliography

Topics to be coveredSlide3

XML is an emerging standard for exchanging, storing and representing the data

The data encoded in XML conforms to a DTD (Document Type Definition

)XML structure is intuitive and it is easier to interpret it using its tree like structure.

IntroductionSlide4

XML data model is very complex when compared to other relational models, which renders a larger search space for optimizing XML queries

In order to optimize XML queries, we need to study the equivalence issue related to the data and the query in order to find out the query equivalence before transforming the query

IntroductionSlide5

The techniques used to classify the XML query optimization techniques can be divided into groups based on the

content

and structureContent based query optimization – Based on

statistics or

classification

Query execution can be improved by classifying the elements, which transform the query based on constraints which are obtained from the data

IntroductionSlide6

The application of parallel database systems can be observed in decision support systems and a wide range of modern database

applications.

The machine architecture in parallel database systems are based on parallel dataflow architecture system, which make use of conventional, shared nothing hardware

design.

For each relation in the database, the tuples are de-clustered (partitioned) across disk storage units, which are attached to individual processors

.

IntroductionSlide7

There are two properties demonstrated by parallelism, which makes it very desirable.

The first one is called as linear

scale-up, where the system can perform a task ‘k’ times the size in a particular span of time, after the number of processors are increased by ‘k’.

The

second one is called as linear speedup

where

the response time is reduced by ‘k’ times if we increase the number of processors by ‘k’ times

IntroductionSlide8

During the query processing stage in parallel databases, parallelism can be exploited in three different

ways.

In the independent parallelism technique, different processors can execute different queries in parallel if the query operators do not depend on each other.

By

pipelining or by making use of inter-operator parallelism, the output of the producer to the consumer can be passed on in parallel by two or more operators in a producer consumer relationship.

Finally

, in intra-operator or partitioned parallelism technique, copies of the same query operator can be run on multiple processors simultaneously, where each of them can be operated on a partition of the data.

IntroductionSlide9

ToXin

indexing scheme was developed to overcome the limitation of applying optimization for path query processing.

This

scheme was developed with the primary goal of exploiting the path structure of the XML databases in all the stages of query processing.

There

are two types of index structures in

Toxin called Value index and Path index

Optimization

mechanism using

ToXin

treeSlide10

Algorithm: ConstructIndexTree

Output: Tree T

ConstructIndexTree()

1. Perform a depth first traversal of the tree.

2. For each visited edge

2.1 Check whether the corresponding index edge has been added

2.1.1 For the current index edge of the XML element

2.1.1.1 Update the instance function in two redundant hash tables representing forward and backward navigation tables

2.1.1.2 Add the parent node and child node

2.2 If it has been added already, skip to the next index edge

3. Stop

Optimization mechanism using

ToXin

treeSlide11

An input query is divided into a set of sub queries where each operation is evaluated separately, as a part of the query.

An

effective execution order for these operations is obtained by creating evaluations for all the set of operations, which in turn helps in executing the queries faster.

The

final result can be obtained by joining all the aggregation of the results together.

Optimization mechanism in LoreSlide12

Algorithm: PlanSelectionAlgorithm

Input: Input list (for the query)

Output: Plan PPlanSelectionAlgorithm (input list)

1. Create a structure in order to track the binding variables

2. while input list is not empty

2.1 For each element in the input list

2.1.1 Based on the current bound variables, find the cheapest access method for the remaining steps

2.1.2 If the step has the least cost, mark the variables as bound and add it to the plan P

2.1.3 Remove the chosen step

3. Return the final plan P obtained from the previous steps

Optimization mechanism in LoreSlide13

Using a set oriented algebraic technique named PAT

algebra, a

series of set related operations and rules are defined. PAT

expressions are obtained by transforming input queries, after checking for the correctness of their syntax.

Based

on the relationship of elements in the DTD, the PAT expressions can be normalized with the help of the PAT algebra in order to get a new query.

Optimizing queries in XML structured document

databasesSlide14

Query optimization based on SchemaSlide15

Query optimization by pruning and rewriting queries Slide16

Query optimization by classification of

elementsSlide17

Join Strategy SelectionSlide18

Optimal Serial Plan (

in identical processors

)Slide19

Comparison between

Relational Database Management System vs. XML Database System Slide20
Slide21

Comparison of algorithmsSlide22

The tree generation algorithm and some of the optimal plan selection and generation algorithms run in polynomial time and hence, they need to be optimized to run in linear time.

PAT algebra is being extended to make it more suitable for query

optimization.

Frequency search operations heavily make use of the indexing techniques in

PAT.

The future research will also be focused more towards generation and use of partially correlated

sub-plans,

which depend on bindings passed between portions of query

plan.

When a significant number of paths pass through a small number of objects, a transformation which introduces a group-by clause can be useful.

Further examination is being conducted in order to implement the Toxin Graph and to check if the Toxin Tree can be extended to be used as an alternative to DOM for querying, updating and storing XML documents

Conclusion and Future workSlide23

Value based grouping and join techniques are being investigated along with multi-way structural joins, new access methods for merged operators and several structural pattern

techniques.

In addition to this, new optimization algorithms have to be implemented to improve caching in Web Service Management

Systems, XQuery

language constructs are to be

optimized.

Cost

based decisions are to be integrated in earlier stages of the query evaluation process

and

the cost model has to be refined in order model the CPU cost in a precise manner.

Conclusion and Future workSlide24

[1] Dunren

Che

, Karl Aberer, and Tamer. 2006. Query optimization in XML structured-document databases.

The VLDB Journal

15, 3 (September 2006), 263-289.

[2] Jason McHugh and Jennifer

Widom

. 1999. Query Optimization for XML. In

Proceedings of the 25th International Conference on Very Large Data Bases

(VLDB '99), Malcolm P. Atkinson, Maria E.

Orlowska

, Patrick

Valduriez

, Stanley B.

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(Eds.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 315-326.

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, M. F. (2007), 'XML Path Language (

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) 2.0' , Technical report, W3C , http://www.w3.org/TR/2007/REC-xpath20-20070123/ .

[4] Haw, S.C and

Rao

, G.S.V.R.K., 2005. Query Optimization Techniques for XML Databases. International Journal of Information Technology, 2(1): 97 – 104.

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