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XML processing in DHT networks Serge Abiteboul, Ioana Manolescu, Neoklis Polyzotis, Nicoleta Preda, Chong Sun

INRIA-Saclay & UC Santa-Cruz

Date

Outline

• Topic• KadoP System

– Overview of DHT– Query evaluation

• Optimization techniquesa)DPP: Distributed postings partitioningb)Structural Bloom Filters

• Conclusion

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Topic

Querying large volume of content in a P2P network for a community of users– Focus on indexing– Content = XML– P2P network = structured - around DHT

XML indexing DHT networks

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Example: Edos distribution system

A system for managing Linux distribution (Mandriva)

System releases– about 10 000 software packages + metadata (XML)

Community of open-source developers: thousands

Functionalities– Publish/update releases– Query the metadata – Retrieve packages

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The KadoP system

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ID0=x mod 24

ID1=(x+20)mod24

ID2=(x+21)mod24

DHT: A P2P indexing infrastructure

[K, Object]

ID15

Pointer in the finger tableLook-up (K) from client ID0Look-up (K) from client ID1

ID4=(x+22)mod24

ID8=(x+23)mod24

Use a ring ─ each peer takes an ID in the space Modulo(2N)─ each peer stores (K, Object) pairs, for K satisfying:

─ID peer ≤ K < ID next peer

Which API?─ locate (K) → Peer IP─ get (K) → Object─ put (K, Object)

Pastry

Advantages and Disadvantage

Advantages• Availability and reliability– No centralization (bottleneck) and replication

• Scalability– Scalable solution for keyword queries

Disadvantage• Difficult to maintain the structure– Not suited for transient population of peers

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XML query processing in KadoP

Query evaluation:

Step 1.• Given a XQuery Q, decompose Q in tree pattern queries • Evaluate each tree pattern query using the DHT index to

identify a set candidates peers P that can provide answers

Step 2.• Ship Q to these peers P and evaluate it there

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Indexing XML documents

X ancestor of Y ↔start(X) < start(Y) ≤ end(X)

X parent of Y↔X ancestor of Y andlevel(X) = level(Y) - 1

Posting = peer, doc, start, end, level

A

B

D E

C

F

“John” G

Doc.xml

1

2

8

6

6 6

5 63 4

4 4

7 8

4 4

Publish them via a DHT – put (k,postings), where k is a

label or a keyword

Remark: all the postings for author accumulate at the same peer

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XML indexing in DHT

DHT

put(author;[p1,d2,start,end,lev])

p2Posting list for author put(author;[p2,d2,start,end,lev])

p1

p(author)

Some technical issuesGoal: manage millions of documents with thousands of

peers

First experiments were a disaster

First works• Replace the index storage of the DHT in a FS by

storage in a database (Berkeley DB)• Extend the API of the DHT with Append and not only

Read/Write• Extend the API of the DHT with a streaming exchange of

postingsWith this, KadoP scaled but was slow due to e.g. long

postings

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Optimization

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Main issue: long postings

Transfer of long posting is hurting performance

Bad response time– Parallelization: Distributed Posting Partitioning (DPP)

Communication load– Bloom filter: Structural Bloom Filter

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long posting for “Name”p(Name)

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DPP structure

long posting for “Name”p(Name)

DPP structure– Split and distribute postings

according to conditions– Each condition is an interval:

C1=[(p1,d1),(p2,d2)]– Each two conditions are over

disjoint intervals

Some kind of B-tree for postings

p(Name)

C1 C2 C3 C4

(p1,d1)

(p,d)

(p4,d4)(p2,d2) (p3,d3)

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Query processing (no DPP)

“Ullman” “database”

Pipeline transfers of postings to query processing peerHolistic twig-join algorithm to compute the result in parallel at QP peer

article

author abstract

article0 ∞

Ullman

author0 ∞

abstract0

0

∞

∞database

∞0

QP-peer

index-Q

Query processing with DPP

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abstract

“XML”

p( )

p( )

Fetch from p(abstract) and p(XML) the conditions C1-C5Prune intervals Transfer and compute in parallel the join for each sub-interval

Conditions sorted according (p,d) At p(client)

p( )

p( )

C1 C2

C3 C5C4

Experiments

• Platform– Grid5000: P2P platform for research in P2P systems– Distributed geographically across 6 sites in France

• KadoP tested on more than 100 machines– 1000 logical peers

• Conclusions in brief– Good performance– KadoP scales very nicely– Issue: does not support high churn of peers (index

copying)

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Query response time

Q=article//author//Ullman

Optimization

(b) Structural Bloom Filters Ancestor Bloom Filter Also in paper: Descendant BF

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Using an Ancestor Bloom Filters

Query: a//b

Compute the Bloom Filter of the a-postings and send to p(b)

Compute the b-postings that have an a-ancestor (and more)

Send it to the p(a) that can compute the answer

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DHT

p(b)

p(a)L(a)

L(b)

ABF(a)

F(b, ABF(a))

Dyadic covers– D(ap)={[1,4], [5,6], [7,7]}

ap is ancestor of bp if D(ap) (start(bp) )

– Here 3 [1,4], so answer is yes!

1 2 3 4 5 6 7

Technique: dyadic intervals

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[1 8]

ap

bp

start end[1,1] [2,2] [3,3] [4,4] [5,5] [6,6] [7,7] [8,8][1, 2] [3, 4] [5, 6] [7, 8]

[1, 4]

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Dyadic intervals

[5, 8]

Ancestor Bloom Filter (simplified)

• Publication: d, ap in d, D(ap)

– Insert a trace in the Bloom Filter• Say T[h(d,)] = 1 for some has function h

• Test: for bp in d,

– for each dyadic interval s.t. start(bp) ,

test if T[h(d,)] = 1

– If one test is positive, conclude bp in d is a solution

• Wrong positives because of Hash collisions

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Query evaluation strategies

p(a)

ABF(a)

ABF(b’)ABF(b’)

b’ = F(b, ABF(a))p(b)

p(c) p(d) d’ = F(d, ABF(b’))

Ancestor Bloom Reducer

p(a)

DBF(b’’)

DBF(c)DBF(c)

b’’ = F(b, DBF(c) ^ DBF(d))

p(b)

p(c) p(d)

Descendant Bloom Reducerc’ = F(c, ABF(b’))

Performances

PostingsDB FilterAB Filter

Conclusion

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Related works

• Very active area

• DHT-based platforms for XML data management– Locating data sources (Galanis & al. VLDB03)

– XPath lookup queries in P2P networks (Bonifati et al. WIDM04)

• Other DHT-based systems for data management– PIER query processor (Huebsch & al, CIDR05)– Indexing in P2P networks (Aberer & al, VLDB05)

• Dyadic Intervals– Maintenance of dynamic intervals (Gilbert & al, VLDB02)

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Contribution

• Two optimization techniques for index processing – Distributed Posting Partitioning– Structural Bloom Filters

• A full system for P2P XML indexing– As opposed to some simulation– Lots of engineering details that are important for

performance– Extensively tested for performance– Tested with a real application, EDOS

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On-going and future work

• New indexing techniques– Trading-off precision for performance

• Publish summarizations of documents• Index/transfer postings at a coarse level of detail

– Index views (query caching)

• Query optimizer for KadoP– This is standard distributed query processing– Use standard optimization techniques, e.g., use

OptiMax ActiveXML optimizer (demo in ICDE08) – Develop what is specific for KadoP: cost model

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Merci

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Indexing time