VAMANA (Talk 2) ( vǎ - mǎ - nǎ )

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VAMANA (Talk 2)

(vǎ - mǎ - nǎ)

Venkatesh Raghavan & Prof. Elke Rundensteiner

DSRG Talk

1ST May 2003

An Efficient XPath Query Engine Exploiting the MASS Index

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Introduction Purpose of the talk.

Generation of Execution Tree Execution

Running Example 1. Running Example 2.

XPath Expression Execution. Cost Estimation. Heuristics and Transformation.

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Running Examples

E.g. 1: //name/parent::person/descendant::watch

E.g. 2: //name [ text() = “Klemens Pelz” ]/parent::person

<people>

<person id="person1">

<name> Klemens Pelz </name>

<people>

<person id="person1">

<name> Hayato Cappelletti </name>

<watches>

<watch open_auction="open_auction82" />

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Bigger Picture

MASS(A Multi-Axis Storage Structure

for Large XML Documents)

VAMANA(XPath Query Engine)

XQuery Engine(future development)

Execution Tree

Mass Interface Node Set

Node Set

XPath Expression

XPath Processor

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How many “ROOT(s)” are there? Root of the Document

We call it “Document Root”

Root of the expression //name/parent::person/descendant::watch

We call it “First Location Step”

Root of Execution Tree We call it “ROOT”

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XPath Processor

Execution Tree

XPath Expression

XPath ProcessorE.g. 2: //name [ text() = “Klemens Pelz” ]/parent::person

name//

CONTEXT

personParent

ROOT

BIPRED=

PRED

textchild

OPERAND

“Klemens Plez”LITERALOPERAND

Phase 1: Parse Tree

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Contd..

name//

CONTEXT

personParent

ROOT

BIPRED=

PRED

textchild

OPERAND


Phase I: Parse Tree

BIPRED=

PRED

textchild

OPERAND


Phase II: Transformed Parse Tree

Execution Tree

XPath Expression

XPath Processor

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Phase III: Execution Tree Generation

Execution Tree

XPath Expression

XPath Processor

name//

CONTEXT

personParent

ROOT

BIPRED=

PRED

textchild

OPERAND


Phase II: Transformed Parse Tree

“person”X: Parent

“name”X: //

“”X: child

“Klemens Plez”

BI_PREDICATE“EQ”

Phase III: VAMANA Execution Tree

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VAMANA Nodes (VNode)

Node Base

VRootNode

MassNode

VBinaryPredicateNode

VExistPredicateNode

VJoinNode

VLiteralNode


Execution Tree


MASS

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VNode Structure

Context Side

Expression Side

Root Node

child


Execution Tree


MASS

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VNode Flow Structure Data-Flow style of querying.

Most of commercial relational database system. Each node is arranged in a fashion such that data “flow”

from one node to another in a procedure-consumer fashion. Correctness. Each node performs some operation on the data that flows

through it. The result is produced by the last node on the dataflow chain.

IN SHORT: Data Flows upwards. Control Flows downwards.

Iterative.


Execution Tree


MASS

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Contd. Iterative.

Currently VAMANA executes nodes iteratively. So no copies of the data is made.

IS IT A PROBLEM?

MASS produces nodes in document order so not a problem.

But there are some expression that in sibling order.

Work in progress.


Execution Tree


MASS

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Execution Tree

“name”X: //

“watch”X: AXIS_DESCENDANT

“person”X: AXIS_PARENT

E.g. 1: //name/parent::person/descendant::watch

Context Side

Root Node


Execution Tree


MASS

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How Do We EXECUTE ?

Step 1: Set Context Node of the root of the expression.

In this example the root of the expression is the root of the document.

Step 2: Ask the VAMANA Root Node for nodes.

//name/parent::person/descendant::watch


Execution Tree


MASS

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Step1: Setting Context for the “First Location Step”



“name”X: //


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OUT OF NODE

FETCHING

INTIAL



“name”X: //

b.i.c.c

b.i.c

b.i.c

b.i.c.m.c

b.i.c.c

b.i.c.m.c


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b.i.c


b.i.c.c

“name”X: //

b.i.c

b.i.c.m.c

b.i.c.m.c

b.i.c.c

b.i.c.m.e

b.i.c.m.e//name/parent::person/descendant::watch

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b.i.c

b.i.c.m.e

b.i.c.m.e


b.i.c.c

b.i.i

“name”X: //

b.i.i.c

b.i.i.c

b.i.i

b.i.i.m.c


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IO Operation

a.a , a.b , a.c

a.a.a , a.b.a, a.b.b , a.c.a , a.c.a, a.c.b/z

//y

** Please see handout

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Example 2

“name”X: //


“ ”X: AXIS_CHILD

“Klemens Pelz”

BI_PREDICATEEQ

Context Side

Expression Side

//name [ text() = “Klemens Pelz” ]/parent::person

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BI_PREDICATEEQ

“name”X: //


“Klemens Pelz”

b.i.e.c

b.i.e.c

b.i.e.c.b

Klemens Pelz

b.i.e.c

b.i.e

//name [ text() = “Klemens Pelz” ]/parent::person

b.i.e.c

b.i.e.c

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Determining Selectivity

Count.

The exact count of the number of nodes in MASS storage structure of that particular nodetest.

IN. The number of tuples that are fetched by the child VNode.

OUT. The number of tuples produced by the VNode.

I_Tuples. Total number of tuples processed till that VNode. This includes the cutrrent node also.

NodeType:NodeTest:X:Count:IN:OUT:I_Tuples:

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Example 1: //name/parent::person/emailaddress

NodeType: MASSNodeTest: nameX: //Count: 482IN: 482OUT: 482

NodeType: MASSNodeTest: personX: AXIS_PARENTCount: 255IN: 482OUT: ?

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Worst Case – Costing Categorize the axis into three division Division 1:

child | descendant | descendant-or-self

NodeType: NodeTest: X: Count:IN: OUT:


X

Y

Cases:

1. #X > #Y

2. #Y > #X#X

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Contd. Division 2:

parent, ancestor, ancestor-or-self, following, following-sibling, preceding, preceding-sibling



X

Y

Cases:

1. #X > #Y

2. #Y > #X#Y

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Contd. Division 3:

Self

For Example: //*/self::X Y/self::*



X

Y

Cases:

1. #X > #Y #Y

2. #Y > #X #X

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NodeType: MASSNodeTest: nameX: //Count: 482IN: 482OUT: 482I_Tuple: 482

NodeType: MASSNodeTest: personX: AXIS_PARENTCount: 255IN: 482OUT: 482I_Tuple: 737

NodeType: MASSNodeTest: watchX: AXIS_DESCENDANTCount: 488IN: 482OUT: 488I_Tuple: 1225

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What about Binary Operator Cost expression sides w.r.t. to child. Operator = AND | OR | EQ.

ALL go out.

Arithmetic Operators. ALL go out. Because cannot predict before execution.

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Contd.

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Heuristics

Higher the ratio, better the selectivity.

Generate a multimap <scaled(IN/OUT),VNode>. Each optimize-able node can then applied the

rules that apply to it.

Ratio = IN/OUT

Scaled Ratio = scale0..1 (IN/OUT)

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Transformation Rule 1:

“name”X: //


BI_PREDICATEEQ


“Klemens Pelz”

Binary Predicate with text comparison Value Index

“name”X: //

“Klemens Pelz”X: AXIS_VALUE

“Klemens Pelz”

“name”X:AXIS_PARENT

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Transformation Rule 2 Mass Node to Join

“name”X: //



Root Node

“name”X: //


“watch”

X: AXIS_DESCENDANT

JOINX: AXIS_DESCENDANT


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* RemovalRule:

p/descendant :: */child::n ≡ p/descendant::nWhere,

p : path expression

Need for this rule: with nodes "*" as node test, during the cost

estimation this might be the spoilsport.

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“Axis::self” RemovalRule:

p/descendant::*/self::m ≡ p/descendent::m

Rule:

p/descendant-or-self::*/self::m ≡ p/descendent-or-self::m

Need for the node: “self” node in combination with * or a node test not

necessary.

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Reverse Axes rules Rule : p/descendant::n/parent::m

≡ //descendant-or-self::m[child::n]

Rule: p/descendant::n/m ≡ p/descendant::m[parent::n]

Rule: /descendant::m/preceding::n ≡ /descendant::n [ following::m]

From Paper: Symmetry in XPath by Dan Olteanu, Holger Meuss, Tim Furche, Francois Br

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Predicate Axis Rules Rule:

p/descendant::* [child::n] ≡ p [descendant::n] / descendant:: *

Predicate Node to Join.

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Conclusion Work in progress in THREE main areas.

Frame work for XPath expression execution. Selectivity Determination. Transformation Rules.

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References1. James Clark and Steve DeRose. XML Path Language (XPATH),

http://www.w3.org/TR/xpath, 2002.

2. S.Boag, D.Chamberlin, Mary F. Fernandez, D.Florescu, J.Robie and J.Siméon,

XQuery 1.0: An XML Query Language. W3C Working Draft, http://www.w3.org/TR/xquery/, 2002.

3. Kurt W. Deschler and Elke Rundensteiner. MASS- Multi Axis Storage Structure, 2002, Technical Report in progress\.

4. T. Milo and D. Suciu. Index structure for path expression, In Proceedings of 7th International Conference on Database Theory, 1999, pages 277-295.

5. Flavio Rizzolo, Alberto Mendelzon. Indexing XML Data with ToXin},WebDB, pages 49-54, Santa Barbara, USA, 2001.

6. Q. Li and B. Moon. Indexing and Querying XML Data for Regular Path Expressions, Proceedings of 27th International Conference on Very Large Database (VLDB'2001), Rome, Italy, September 2001, pages 361-370.

7. XMark - The XML Benchmark project. http://monetdb.cwi.nl/xml/.