Twig2Stack: Bottomup Processing of GeneralizedTreePattern Queries over XML Documents

1
Twig2Stack Bottom-up Processing of
Generalized-Tree-Pattern Queries over XML
Documents

• Songting Chen, Hua-Gang Li , Junichi Tatemura
• Wang-Pin Hsiung, Divykant Agrawal and K. Selcuk
  Candan
• NEC Laboratories America
• University of California, Santa Barbara

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Background

• XML
• Hierarchical (tree) structured data
• Provide flexibility to model semi-structured data
• Widely accepted as universal data exchange format
• Query over XML
• XPath, XQuery W3C
• Extensively used by many applications
• Adopted by a number of commercial systems

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State-of-the-art XML Query Processing
Algebraic Approach
Binary Structure Joins Timber Large
intermediate results
Optimize multiple path expressions of XQuery
Chen, et. al Expensive post-processing
Holistic Approach
?
PathStack Bruno, et. al
TwigStack Bruno, et. al
Twig2Stack
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Processing Generalized Tree Pattern (GTP) Queries
Structural Joins
Structural Outer Joins

Grouping
Duplication Elimination
a1
A
//A//B
a2
B
b1
Our goal Avoid ALL these!
D
C
Sort
a1
XQuery FOR b in //AE/B, d in
b/D LET c b/C RETURN b, c, d
//A/B
a2
b2
b1
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Motivation PathStack Bruno et.al
a1

• Query //A//B Data
• Key observation minimize intermediate results
  through compact representation of path matches,
  by
• Inter-node record AD relationship between
  elements in different query nodes, e.g., b1?a2,
  b2?a2
• Intra-node record AD relationship between
  elements within the same query nodes, e.g., b1,
  b2
• TwigStack Bruno et.al minimizes intermediate
  results through
• Output only those path matches that are in final
  twig results
• However, such optimality cannot be guaranteed
  Choi, et.al
• Not helpful for processing GTP queries
• Question can we minimize intermediate results
  for twig queries through compact result encoding
  (similar to PathStack)?
• Useful for processing GTP queries as well?

b2
a2
a2
a1
b1
b1
SA
SB
b2
?
?
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Hierarchical Stack Encoding
a1
a1

• Inter-node //A//B
• Can still use explicit edges
• Intra-node A
• Matching elements forms a tree structure as well
• Associate each query node with a hierarchical
  stack
• Push element e into hierarchical stack HSE iff
  e satisfies the sub-twig query rooted at E
• Matching can be determined when entire sub-tree
  of e seen
• Require post-order document traversal

a2
a2
a3
a4
a3
a4
HSA
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Twig2Stack Running Example
1,20, 1
a1
A
2,15, 2
16,19, 2
a2
b3
B
a2
17,18, 3
3,14, 3
C
D
d3
HSA
b1
12,13, 4
4,11, 4
c2
d1
5,10, 5
b2
b1
8, 9, 6
b2
6,7, 6
c1
d2
HSB
Merging Stacks
TwigStack needs to enumerate 3 matches for
//A/B//D and 2 for //A/B//C then join them
together. Twig2Stack requires neither path
joins nor path enumeration!
d1
c1
d2
c2
d3
HSC
HSD
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GTP Result Enumeration
a4

• Bottom-up Computation .vs. Top-down Enumeration
• Visit Only those that are in the twig matches
• Handling grouping results
• Automatic grouping through Inter-node edges
• Handling duplicates and out-of-order results
• Problems coming from non-return nodes
• If D is return node while B is not
• b1 ? d1, d2, d3 and b2 ?d2, d3 (duplicates)
• Observation Intra-node hierarchy provides hints

b1
b2
d2
c2
c1
d3
d1
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Experiment Setup

• Implementation
• Twig2Stack Java 1.4.2
• TwigStack, TJFast Java 1.4.2
• Kindly provided by Jiaheng Lu from National
  University of Singapore (NUS)
• Datasets
• XMark, DBLP, TreeBank
• Metrics
• Query processing time
• IO time

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Processing Full Twig Queries
Optimization of Query Processing TwigStack
Twig2Stack Optimization of IO TJFast
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Not yet done Memory Usage

• Hierarchical Stack Encoding could hold entire
  document in memory in the worst case
• Unlike DOM approach, only matches need to be
  stored
• Tag match
• (Partial) twig match
• Predicate evaluation
• Early result enumeration dramatically reduces the
  memory usage
• Enumerate query results before the end of
  document and release buffer
• Main idea hybrid of top-down (PathStack) and
  bottom-up (Twig2Stack) approaches

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Early Result Enumeration (ERM)

• Enumerate results and release buffer when
  elements in top-branch node are popped from
  PathStack

A
1,20, 1
a1
a2
a1
B
2,15, 2
16,19, 2
a2
b3
C
D
17,18, 3
3,14, 3
d3
b1
12,13, 4
4,11, 4
c2
d1
5,10, 5
b2
8, 9, 6
6,7, 6
c1
d2
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Memory Usage
dblp
Small sub-tree ?
article
title
year
site
open_auctions
Huge sub-tree ?
bid
reserve
bidder
increase
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Conclusions and Future Work

• Proposed a bottom-up GTP processing solution
• A twig encoding scheme
• A GTP enumeration algorithm that avoids any
  post-processing operations
• A hybrid scheme to reduce memory usage
• Future directions
• Handling worst case memory issues
• Optimizing IO cost by exploiting indexes
• Handling other axes, full XQuery, graph input
• Handling XML streams

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Processing GTP
Optimization of non-return nodes
Automatic grouping