MonetDB/XQuery Technology Preview 1 Stefan Manegold CWI Amsterdam -

MonetDB/XQuery

Technology Preview 1

Stefan ManegoldCWI

Amsterdam

http://monetdb.cwi.nl/ - http://pathfinder-xquery.org/

European Pathfinder Team

• University of Konstanz (Germany)

– Torsten Grust, Jens Teubner, Jan Rittinger

• University of Twente (Netherlands)

– Maurice van Keulen, Jan Flokstra

• CWI, Amsterdam (Netherlands)

– Peter Boncz, Stefan Manegold, Sjoerd Mullender

Stefan Manegold HollandOpen, Amsterdam 31-5-2005MonetDB/XQuery

Xmark 11 MB 110 MB 1.1 GB 11 GB

Q Galax X-Hive MDB/XQ Galax X-Hive MDB/XQ X-Hive MDB/XQ MDB/XQ

1 0.06 0.37 0.05 0.72 1.29 0.41 9.9 1.2 13

2 0.03 0.45 0.07 0.31 1.75 0.30 33.0 2.4 25

3 0.14 0.65 0.28 1.76 5.66 1.51 25.1 12.5 126

4 0.22 0.10 0.08 2.91 1.00 0.45 18.1 3.8 36

5 0.05 0.13 0.05 0.63 0.90 0.16 20.7 1.2 11

6 1.30 1.07 0.02 13.29 10.17 0.05 178.1 0.3 3

7 2.68 1.57 0.03 30.01 24.84 0.07 278.4 0.4 4

8 0.16 0.85 0.14 2.12 3.51 0.75 49.1 10.4 208

9 113.23 32.25 0.20 DNF 12280.66 0.87 DNF 12.9 289

10 1.74 5.28 0.80 18.61 442.37 5.31 DNF 55.0 1882

11 2.62 98.91 0.18 DNF 19927.29 3.48 DNF 872.5 DNF

12 1.44 23.39 0.14 DNF 5100.19 1.66 DNF 150.7 DNF

13 0.03 0.10 0.07 0.66 1.03 0.22 12.9 1.3 13

14 1.92 0.72 0.17 99.53 11.16 1.40 110.2 13.7 959

15 0.02 0.03 0.09 0.20 0.49 0.28 10.6 1.7 16

16 0.03 0.03 0.11 0.46 0.52 0.26 10.9 1.8 18

17 0.06 0.09 0.07 0.82 0.85 0.30 11.8 2.8 26

18 0.07 0.08 0.04 0.73 0.64 0.13 14.8 0.9 9

19 1.17 0.67 0.11 14.73 12.15 0.55 254.5 5.3 88

20 0.28 0.11 0.24 2.98 1.40 0.62 24.6 4.9 50



Results: Performance (1)

did not finish


Results: Performance (2)

Story

• XQuery Example• Relational XQuery

– System Architecture– XML Encoding

• Science & Reseach• Scalability• Outlook

– Conclusions– Roadmaps– Release & References


• For each author, return number of books and receipts

for books published in the past 2 years, ordered by name

let $cat := fn:doc(“www.bn.com/catalog.xml”), (:Documents:) $sales := fn:doc(“www.publishersweekly.com/sales.xml”)

for $author in distinct-values($cat//author) (:Grouping:) let $books := $cat//book[@year >= 2003 and author = $a], (:Sel.:) $receipts := $sales/book[@isbn = $books/@isbn]/receipts (:Join:) order by $author (:Ordering:) return

<sales> (:XML Construction:) { $author }

<count> { fn:count($books) } </count> (:Aggregation:) <total> { fn:sum($receipts) } </total></sales>


XQuery Example

• For each author, return number of books and receipts

for books published in the past 2 years, ordered by name

let $cat := fn:doc(“www.bn.com/catalog.xml”), Documents $sales := fn:doc(“www.publishersweekly.com/sales.xml”)

for $author in distinct-values($cat//author) Grouping let $books := $cat//book[@year >= 2003 and author = $a], Sel. $receipts := $sales/book[@isbn = $books/@isbn]/receipts Join order by $author Ordering return

<sales> XML Construction { $author }

<count> { fn:count($books) } </count> Aggregation <total> { fn:sum($receipts) } </total></sales>


XQuery Example

XQuery Systems: 2 Approaches

• Existing “native” XML/XQuery systems are built from scratch– Galax, Saxon, …– X-Hive, Tamino, …– (Still have to) re-invent optimization technology

• Our approach:– Build XQuery system on top of an RDBMS– Leverage mature relational technology

to achieve efficient XQuery processing


Architecture


XML in an RDBMS: XPath Accelerator

Node-based relational encoding of XQuery's data model


1. f/descendant:SELECT * FROM pre_post WHERE pre > f.pre AND post < f.post

2. f/ancester: SELECT * FROM pre_post WHERE pre < f.pre AND post > f.post

3. f/preceeding: SELECT * FROM pre_post WHERE pre < f.pre AND post < f.post

4. f/following: SELECT * FROM pre_post WHERE pre > f.pre AND post > f.post

Science & Research

• More research lead to more optimization– Join Recognition– Embedded XPath processing– Order Awareness

• Various scientific publications


Results: Scalability (3)

Unsurpassed scalability • Standard Opteron PC, 8GB RAM, 64-bit Linux• Can process 11GB documents!

Mostly linear scaling with document size


Conclusions

• Relational approach Works Is fast Is scalable

• Crucial Optimizations– Join recognition– Embedded XPath processing– Order awareness


Roadmap

• 30-05-05: MonetDB/XQuery 4.8/0.8 “Mercurius”– Developers Release / Technology Preview 1

• 30-09-05: MonetDB/XQuery 4.10/0.10 “Venus”– Student Release / Technology Preview 2

– XUpdate, Algebraic Query Optimization

• 30-12-05: MonetDB/XQuery 4.12/1.12 “Mars”– Final Release

– Application Programming Interfaces

– End-User Front-Ends


Open Source Release

• MonetDB + Pathfinder on SourceForge– Mozilla-like License

• MonetDB homepage– http://monetdb.cwi.nl/

• Pathfinder homepage– http://pathfinder-xquery.org/

• Developers website– http://sf.net/projects/monetdb/



Xmark 11 MB

Q X-Hive

1 0.06 0.37

2 0.03 0.45

3 0.14 0.65

4 0.22 0.10

5 0.05 0.13

6 1.30 1.07

7 2.68 1.57

8 0.16 0.85

9 113.23 32.25

10 1.74 5.28

11 2.62 98.91

12 1.44 23.39

13 0.03 0.10

14 1.92 0.72

15 0.02 0.03

16 0.03 0.03

17 0.06 0.09

18 0.07 0.08

19 1.17 0.67

20 0.28 0.11

Galax

Xmark 11 MB

Q X-Hive MDB/XQ

1 0.06 0.37 0.05

2 0.03 0.45 0.07

3 0.14 0.65 0.28

4 0.22 0.10 0.08

5 0.05 0.13 0.05

6 1.30 1.07 0.02

7 2.68 1.57 0.03

8 0.16 0.85 0.14

9 113.23 32.25 0.20

10 1.74 5.28 0.80

11 2.62 98.91 0.18

12 1.44 23.39 0.14

13 0.03 0.10 0.07

14 1.92 0.72 0.17

15 0.02 0.03 0.09

16 0.03 0.03 0.11

17 0.06 0.09 0.07

18 0.07 0.08 0.04

19 1.17 0.67 0.11

20 0.28 0.11 0.24

Galax


Xmark 11 MB 110 MB

Q X-Hive MDB/XQ X-Hive

1 0.06 0.37 0.05 0.72 1.29

2 0.03 0.45 0.07 0.31 1.75

3 0.14 0.65 0.28 1.76 5.66

4 0.22 0.10 0.08 2.91 1.00

5 0.05 0.13 0.05 0.63 0.90

6 1.30 1.07 0.02 13.29 10.17

7 2.68 1.57 0.03 30.01 24.84

8 0.16 0.85 0.14 2.12 3.51

9 113.23 32.25 0.20 DNF 12280.66

10 1.74 5.28 0.80 18.61 442.37

11 2.62 98.91 0.18 DNF 19927.29

12 1.44 23.39 0.14 DNF 5100.19

13 0.03 0.10 0.07 0.66 1.03

14 1.92 0.72 0.17 99.53 11.16

15 0.02 0.03 0.09 0.20 0.49

16 0.03 0.03 0.11 0.46 0.52

17 0.06 0.09 0.07 0.82 0.85

18 0.07 0.08 0.04 0.73 0.64

19 1.17 0.67 0.11 14.73 12.15

20 0.28 0.11 0.24 2.98 1.40

Galax Galax


Xmark 11 MB 110 MB

Q X-Hive MDB/XQ X-Hive MDB/XQ

1 0.06 0.37 0.05 0.72 1.29 0.41

2 0.03 0.45 0.07 0.31 1.75 0.30

3 0.14 0.65 0.28 1.76 5.66 1.51

4 0.22 0.10 0.08 2.91 1.00 0.45

5 0.05 0.13 0.05 0.63 0.90 0.16

6 1.30 1.07 0.02 13.29 10.17 0.05

7 2.68 1.57 0.03 30.01 24.84 0.07

8 0.16 0.85 0.14 2.12 3.51 0.75

9 113.23 32.25 0.20 DNF 12280.66 0.87

10 1.74 5.28 0.80 18.61 442.37 5.31

11 2.62 98.91 0.18 DNF 19927.29 3.48

12 1.44 23.39 0.14 DNF 5100.19 1.66

13 0.03 0.10 0.07 0.66 1.03 0.22

14 1.92 0.72 0.17 99.53 11.16 1.40

15 0.02 0.03 0.09 0.20 0.49 0.28

16 0.03 0.03 0.11 0.46 0.52 0.26

17 0.06 0.09 0.07 0.82 0.85 0.30

18 0.07 0.08 0.04 0.73 0.64 0.13

19 1.17 0.67 0.11 14.73 12.15 0.55

20 0.28 0.11 0.24 2.98 1.40 0.62

Galax Galax


Xmark 11 MB 110 MB 1.1 GB

Q X-Hive MDB/XQ X-Hive MDB/XQ X-Hive MDB/XQ

1 0.06 0.37 0.05 0.72 1.29 0.41 9.9 1.2

2 0.03 0.45 0.07 0.31 1.75 0.30 33.0 2.4

3 0.14 0.65 0.28 1.76 5.66 1.51 25.1 12.5

4 0.22 0.10 0.08 2.91 1.00 0.45 18.1 3.8

5 0.05 0.13 0.05 0.63 0.90 0.16 20.7 1.2

6 1.30 1.07 0.02 13.29 10.17 0.05 178.1 0.3

7 2.68 1.57 0.03 30.01 24.84 0.07 278.4 0.4

8 0.16 0.85 0.14 2.12 3.51 0.75 49.1 10.4

9 113.23 32.25 0.20 DNF 12280.66 0.87 DNF 12.9

10 1.74 5.28 0.80 18.61 442.37 5.31 DNF 55.0

11 2.62 98.91 0.18 DNF 19927.29 3.48 DNF 872.5

12 1.44 23.39 0.14 DNF 5100.19 1.66 DNF 150.7

13 0.03 0.10 0.07 0.66 1.03 0.22 12.9 1.3

14 1.92 0.72 0.17 99.53 11.16 1.40 110.2 13.7

15 0.02 0.03 0.09 0.20 0.49 0.28 10.6 1.7

16 0.03 0.03 0.11 0.46 0.52 0.26 10.9 1.8

17 0.06 0.09 0.07 0.82 0.85 0.30 11.8 2.8

18 0.07 0.08 0.04 0.73 0.64 0.13 14.8 0.9

19 1.17 0.67 0.11 14.73 12.15 0.55 254.5 5.3

20 0.28 0.11 0.24 2.98 1.40 0.62 24.6 4.9

Galax Galax


Xmark 11 MB 110 MB 1.1 GB 11 GB

Q Galax X-Hive MDB/XQ Galax X-Hive MDB/XQ X-Hive MDB/XQ MDB/XQ

1 0.06 0.37 0.05 0.72 1.29 0.41 9.9 1.2 13

2 0.03 0.45 0.07 0.31 1.75 0.30 33.0 2.4 25

3 0.14 0.65 0.28 1.76 5.66 1.51 25.1 12.5 126

4 0.22 0.10 0.08 2.91 1.00 0.45 18.1 3.8 36

5 0.05 0.13 0.05 0.63 0.90 0.16 20.7 1.2 11

6 1.30 1.07 0.02 13.29 10.17 0.05 178.1 0.3 3

7 2.68 1.57 0.03 30.01 24.84 0.07 278.4 0.4 4

8 0.16 0.85 0.14 2.12 3.51 0.75 49.1 10.4 208

9 113.23 32.25 0.20 DNF 12280.66 0.87 DNF 12.9 289

10 1.74 5.28 0.80 18.61 442.37 5.31 DNF 55.0 1882

11 2.62 98.91 0.18 DNF 19927.29 3.48 DNF 872.5 DNF

12 1.44 23.39 0.14 DNF 5100.19 1.66 DNF 150.7 DNF

13 0.03 0.10 0.07 0.66 1.03 0.22 12.9 1.3 13

14 1.92 0.72 0.17 99.53 11.16 1.40 110.2 13.7 959

15 0.02 0.03 0.09 0.20 0.49 0.28 10.6 1.7 16

16 0.03 0.03 0.11 0.46 0.52 0.26 10.9 1.8 18

17 0.06 0.09 0.07 0.82 0.85 0.30 11.8 2.8 26

18 0.07 0.08 0.04 0.73 0.64 0.13 14.8 0.9 9

19 1.17 0.67 0.11 14.73 12.15 0.55 254.5 5.3 88

20 0.28 0.11 0.24 2.98 1.40 0.62 24.6 4.9 50


Outline

• Basic XML / XQuery• Introduction of Pathfinder and MonetDB projects• Relational XQuery

– XPath steps in the pre/post plane– Translating for-loops, and beyond

• Optimizations– Order prevention– Loop-Lifted Staircase join – Join recognition

• Outlook– Conclusions– Roadmaps


Outline






XML

• Standard, flexible syntax for data exchange

– Regular, structured data

Database content of all kinds: Inventory, billing, orders, …

“Small” typed values

– Irregular, unstructured text

Documents of all kinds: Transcripts, books, legal briefs, …

“Large” untyped values

• Lingua franca of B2B Applications…

– Increase access to products & services

– Integrate disparate data sources

– Automate business processes

• … and numerous other application domains

– Bio-informatics, library science, …Stefan Manegold HollandOpen, Amsterdam 31-5-2005MonetDB/XQuery

XML : A First Look

• XML document describing catalog of books

<?xml version="1.0" encoding="ISO-8859-1" ?><catalog> <book isbn="ISBN 1565114302"> <title>No Such Thing as a Bad Day</title> <author>Hamilton Jordan</author> <publisher>Longstreet Press, Inc.</publisher> <price currency="USD">17.60</price> <review> <reviewer>Publisher</reviewer>: This book is the moving

account of one man's successful battles against three cancers ... <title>No Such Thing as a Bad Day</title> is warmly recommended.

</review> </book>



</catalog>


XQuery 1.0

• Functional, strongly-typed query language• XQuery 1.0 =

XPath 2.0 for navigation, selection, extraction

+ A few more expressions For-Let-Where-Order By-Return (FLWOR)

XML construction

Operators on types

+ User-defined functions & modules

+ Strong typing


XSLT vs. XQuery

• XSLT 1.0: XML XML, HTML, Text– Loosely-typed scripting language– Format XML in HTML for display in browser– Must be highly tolerant of variability/errors in data

• XQuery 1.0: XML XML– Strongly-typed query language– Large-scale database access– Must guarantee safety/correctness of operations on data

• Over time, XSLT & XQuery may both serve needs of many application domains

• XQuery will become a hidden, commodity language


XQuery Example

• For each author, return number of books and receipts books published in past 2

years, ordered by name

let $cat := fn:doc(“www.bn.com/catalog.xml“), Join $sales := fn:doc(“www.publishersweekly.com/sales.xml“)

for $author in distinct-values($cat//author) Groupinglet $books := $cat//book[@year >= 2000 and author = $a], S.J.

$receipts := $sales/book[@isbn = $books/@isbn]/receipts

order by $author Orderingreturn

<sales> XML Construction { $author }

<count> { fn:count($books) } </count> Aggregation <total> { fn:sum($receipts) } </total></sales>


Outline






XQuery Systems: 2 Approaches

• Tree-based– Tree is basic data structure

• Also on disk (if an XQuery DBMS)– Navigational Approach

• Galax [Simeon..], Flux [Koch..], X-Hive– Tree Algebra Approach

• TIMBER [Jagadish..]

• Relational– Data shredded in relational tables– XQuery translated into database query (e.g. SQL)


The Pathfinder Project

• Challenge / Goal:– Turn RDBMSs into efficient XQuery engines

• People:– Torsten Grust, Jens Teubner

• University of Konstanz (June 2005: Technical University of Munich)

– Maurice van Keulen

• University of Twente

– Jan Rittinger

• University of Konstanz & CWI

• Task: generate code for MonetDB


The Pathfinder Project

• Challenge / Goal:– Turn RDBMSs into efficient XQuery engines

• People:– Torsten Grust, Jens Teubner, ...

• University of Konstanz (June 2005: Technical University of Munich)

– Maurice van Keulen, Jan Flokstra, ...

• University of Twente

– Jan Rittinger

• University of Konstanz & CWI

• Task: generate code for MonetDB

– Peter Boncz, Stefan Manegold, Sjoerd Mullender, ...

• CWI, Amsterdam


MonetDB: Applied CS Research at CWI

• a decade of “query-intensive” application experience

• image retrieval: Peter Bosch ImageSpotter

• audio/video retrieval: Alex van Ballegooij RAM

• XML text retrieval: de Vries / Hiemstra TIJAH

• biological sequences: Arno Siebes BRICKS

• XML databases: Albrecht Schmidt XMark

Grust / vKeulen Pathfinder

• GIS: Wilco Quak MAGNUM

• data warehousing / OLAP / data mining

SPSS DataDistilleries

Univ. Massachussetts PROXIMITY

CWI research group successfully spun off DataDistilleries (now SPSS)


Pathfinder — MonetDB

Pathfinder

MonetDB

Parser

Sem. Analysis

Core Translation

Typechecking

Relational Algebra

Database

MIL

SQL

Parser

Sem. Analysis

Core Translation

Typechecking

Database

MIL


Pathfinder — MonetDB

Pathfinder

MonetDB

Parser

Sem. Analysis

Core Translation

Typechecking

Relational Algebra

Database

MIL

SQL

Core to MILTranslation

Parser

Sem. Analysis

Core Translation

Typechecking

Database

MIL


Open Source

• MonetDB + Pathfinder on Sourceforge– Mozilla License

• MonetDB Homepage– http://monetdb.cwi.nl/

• Pathfinder Homepage

– http://pathfinder-xquery.org/• Developers website:

– http://sf.net/projects/monetdb/

RoadMap• 14-apr-04: initial Beta release MonetDB/SQL• 30-sep-04: first official release MonetDB/SQL• 30-may-05: Developer release of MonetDB/XQuery (i.e. Pathfinder)• 30-sep-05: Student release of MonetDB/XQuery (incl. XUpdate)• 30-dec-05: Users release of MonetDB/XQuery (?)


MonetDB: extensible architecture

Front-end/back-end:

• support multiple data models

• support multiple end-user languages

• support diverse application domains


Front-end/back-end:

• support multiple data models

• support multiple end-user languages

• support diverse application domains

PathfinderXQuery Frontend

MonetDB: extensible architecture


Architecture


The Architecture


Outline






XPath on an RDBMS

Node-based relational encoding of XQuery's data model


Pre/Post Pre/Level/Size

done for better skipping and updates


Outline






Order Prevention

[VLDB03 Wang&Cherniack] define:

• Order properties of relations

• Order propagation rules for relational operators

Decoration of physical plans with order properties eliminate sort


– For loop map with all combinations O(N*N)– If `simple’ condition exist on two loop variables join

– Only make a map with the matching combinations– E.g. with Hash-Table O(N)

Performed on the XCore tree

Recognize if-then expressions

Open question:

where to optimize best??

Join Recognition

for $p in $auction/site/people/person for $t in $auction/site/closed_auctions/closed_auction where $t/buyer/@person = $p/@id return $t


Loop-lifted staircase join

• Staircase join [VLDB03]: – Single-pass for a *set* of context nodes

Loop-lifting multiple iters multiple sets of context nodes

– elaborate skipping!

– Loop-Lifted Staircase Join

In a single pass: process multiple input context node lists

– Use a stack

– Exploit axis properties for pruning


Scalability

Test platform• Opteron 1.6GHz, 8GB RAM, 64-bit Linux (Fedora Core 3)

• Can process 11GB document!



Scalability




• Some swapping in the join queries


Scalability




• Some swapping in the join-queries

• Q11 + Q12 generate quadratic result


XMark 10MB : Pathfinder vs XHive & Galax


XMark 1GB: Pathfinder vs X-Hive

did not finish


Outline






Conclusions

• Relational approach can be scalable & fast• Crucial Optimizations

– Join recognition– Loop-lifted XPath steps– Order awareness


Conclusions

• Relational approach can be scalable & fast• Crucial Optimizations

– Join recognition– Loop-lifted XPath steps– Order awareness

Future Roadmap (Scientific/Research):• Algebraic Query Optimization• Updates


Product Roadmap

• 30-05-05: MonetDB/XQuery 4.8/0.8 “Mercurius”– Developers Release / Technology Preview 1

• 30-09-05: MonetDB/XQuery 4.10/0.10 “Venus”– Student Release / Technology Preview 2

– XUpdate, Algebraic Query Optimization

• 30-12-05: MonetDB/XQuery 4.12/1.12 “Mars”– Final Release

– Application Programming Interfaces

– End-User Front-Ends


Loop-lifted staircase join

document document

List of context nodes Active stack

Multiple lists of context nodes


Staircase join

document

List of context nodes

Date post:	29-Dec-2015
Category:	Documents
Upload:	virgil-floyd
View:	213 times
Download:	0 times