Tree-Pattern Queries on a Lightweight XML Processor

Transcription

1 Tree-Pattern Queries on a Lightweight XML Processor MIRELLA M. MORO Zografoula Vagena Vassilis J. Tsotras Research partially supported by CAPES, NSF grant IIS , UC Micro, and Lotus Interworks

2 Outline Motivation and Contributions Background Method Categorization Experimental Evaluation Conclusions UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 2

3 Motivation XML query languages: selection on both value and structure Tree-pattern queries (TPQ) very common in XML Many promising holistic solutions None in lightweight XML engines Without optimization module (e.g. exist, Galax) Effective, robust processing method Reasons: No systematic comparison of query methods under a common storage model No integration of all methods under such storage model Context: XPath semantics, stored data (indexed at will) UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 3

4 Contributions TPQ methods over unified environment Method Categorization: data access patterns and matching algorithm Common storage model + integration of all methods Capture the access features Permit clustering data with off-the-shelf access methods (e.g. B + tree) Novel variations of methods using index structures + Handle TPQ Extensive comparative study Synthetic, benchmark and real datasets Decision in the applicability, robustness and efficiency UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 4

5 Background TPQ article Bib (1,20) author last procs conf article (2,19) title (3,5) author (6,13) procs (14,18) article (2,19) t1 (4) last (7,9) first (10,12) conf (15,17) last (7,9) 2<7<9<19 DeWitt (8) David J. (11) VLDB (16) XML database = forest of unranked, ordered, node-labeled trees, one tree per document UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 5

6 Common Storage Model name (4,5) bib (1,26) book (2,9) paper (18,25) author (3,8) author (19,24) address (6,7) book (10,17) name (20,21) address (22,23) author (3,8) (11,16) (19,24) bib (1,16) address (6,7) (14,15) (22,23) name (4,5) (12,13) (20,21) paper (18,25) book (2,9) (10,17) B + Tree on ( tag, initial ) name (12,13) author(11,16) address (14,15) Input = sequence (list) of elements One list per document tag = element list Node clustering by index structures Numbering scheme UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 6

7 Method Categorization Parameters: access pattern and matching algorithm (1) set based techniques (2) query driven (3) input driven (4) structural summaries UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 7

8 Cat 1: Set-based Techniques Access Pattern Sorted/indexed Matching Process Join sets, merge individual paths Input: sequences of elements, one list per query node element, possibly indexed (set-based) Major representative: TwigStack Optimal XML pattern matching algorithm (ancestor/descendant) Stack-based processing Set of stacks = compact encoding of partial and total results in linear space (possibly exponential number of answers) UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 8

9 TwigStack + Indexes B + tree, built on the left attribute From ancestor: probe descendants: skip initial nodes Ancestor skipping not effective (up to 1st element that follows) XB-tree: on (left,right) bounding segment XR-tree: on (left,right), B+tree with complex index key + stab lists A comparative study* shows that Skipping ancestors: XBTree better (XBTree size is smaller) Recursive level of ancestors: XBTree better again Searching on stab lists of XR-tree is less efficient Plain B+tree: skips descendants, BUT not ancestors XBTwigStack is our choice * H.Li et al. An Evaluation of XML Indexes for Structural Joins. Sigmod Record, 33(3), Sept 04 UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 9

10 Cat 2: Query Driven Techniques Access Pattern Indexed/random Matching Process Incremental construction of each result instance Processing: the query defines the way input is probed Major representatives: ViST and PRIX Specific details: significantly different Same strategy Convert both document and query to sequences Processing query = subsequence matching UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 10

11 ViST and PRIX Recursively identify matches = quadratic time Optimize the naïve solution: Identify candidate nodes for each matching step Index structures to cluster those candidates Subsequence matching process = a plan consisting of INLJ among relations, each of which groups document nodes with the same label For a given query, joins sequence statically defined by the sequencing of the query INLJ plans are a superset of the static plans that PRIX and VIST use UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 11

12 ViST x PRIX x INLJ Dataset #nodes VIST PRIX INLJ 100% LEAVES: 80% LEAVES: 1% ROOT: 80% ROOT: 1% INTERNAL: 80% INTERNAL: 1% Percentage of nodes processed by each algorithm INLJ: best plan UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 12

13 INLJ : improved B + tree a 1,52 Consider b//c Starting from c b 2,31 b 32,41 b 42,51 b elem. list a 33,40 b c 38,39 34,37 c 35, ,31 32,41 34,41 42,51 TPQ evaluation of relational plan Independence of the ordered XML model Total avoidance of false positives UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 13

14 Cat 3: Input Driven Techniques Access Pattern Sequential Matching Process Input drives computation, merge individual paths Processing: at each point, the flow of computation is guided entirely by the input through a Finite State Machine (DFA/NFA) Advantages Each node processed only once Simplicity, sequential access pattern Problem: skipping elements UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 14

15 SingleDFA and IdxDFA SingleDFA <element> triggers the DFA, choosing next state </element>: execution backtracks to when start processed TPQ matching: intermediate results compacted on stacks Experiments show reading whole input = not enough Speeding up navigation: IdxDFA Instead of reading sequentially: use indexes and skip descendants UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 15

16 IdxDFA: example c a b d c 1 b 2 a 3 d 11 a 12 c 22 c 4 d 6 b 9 b 13 c 16 d 6 b 21 c 5 d 7 d 9 c 10 d 14 c 15 UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 16

17 IdxDFA: example c a b d c 1 b 2 a 3 d 11 a 12 c 22 c 4 d 6 b 9 b 13 c 16 d 6 b 21 c 5 d 7 d 9 c 10 d 14 c 15 UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 17

18 Cat 4: Graph Summary Evaluation Access Pattern Indexed/Random Matching Process Merge-join partitioned input, merge individual paths Structural summary: index node identifies a group of nodes in the document Processing: identify index nodes that satisfy the query + post processing filtering Beneficial: when there is a reasonable structural index, much smaller than document Problem: graph size comparable/larger than original document UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 18

19 Categories Summary Access Pattern Matching Process Methods Set Based Sorted/ Indexed Join sets, merge individual paths Twigstack /XB, B + tree, XR-tree Query Driven Indexed/ random Incremental construction of each result instance (ViST, PRIX) INLJ Input Driven Sequential Input drives computation, merge individual paths SingleDFA, IdxDFA Structural Summary Indexed/ random Merge-join partitioned input, merge individual paths Structural indexes UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 19

20 Experimental Evaluation 1. Experiments with real datasets 2. Experiments with synthetic datasets Further analyze each method Characterize the methods according to specific features available in each custom dataset 3. More sets of experiments Closely verify XBTWIGSTACK versus INLJ UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 20

21 Setup Algorithms using the same API Analysis varying structure and selectivity Performance measure = total time required to compute a query Number of nodes as secondary information Intel Pentium 4 2.6GHz, 1Gb ram Berkeley DB: 100 buffers, page size 8Kb, B + tree Real/benchmark datasets XMark (Internet auction, 1.4 GB raw data, ± 17 million nodes), Protein Sequence Database UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 21

22 XMark Time (sec) X1 X2 X4 X6 Queries XBTwigStack SingleDFA IdxDFA INLJ StrIdx UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 22

23 Custom Data Goal: isolate important features Query //a//b[.//c]//d Simple enough for detailed investigation Complex enough to provide large number of different data access possibilities Vary selectivity of each element separately Add recursion to key elements (root, leaf) c a b d UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 23

24 Custom Data Time (sec) XBTwigStack IdxDFA INLJ a 0 D1:100 D2:80 D3:50 D4:10 D5:01 Dataset:Selectivity c b d UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 24

25 Custom Data Time (sec) XBTwigStack IdxDFA INLJ a 0 D1:100 D6:80 D8:10 D10:80 D12:10 Dataset:Selectivity c b d UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 25

26 XBTwigStack x INLJ Time (sec) R40 ABCD ABDC BACD BADC BCAD BCDA BDAC BDCA CBAD CBDA DBAC DBCA XBTwig On large dataset, 40mi nodes, 1Gb, 1% selectivity Difference of 40s between XBTwig and INLJ best plan UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 26

27 XBTwigStack x INLJ R1 R3 R4 R7 R9 ABCD ABDC BACD BADC BCAD BCDA BDAC BDCA CBAD CBDA DBAC DBCA XBTWIG UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 27

28 Conclusions Categorization of TPQ processing algorithms Adaptations for processing TPQ DFA + accessing nodes from B + tree INLJ + ancestor skipping DFA-based improved, IdxDFA, not enough Structural summary available and smaller than document: StrIdx XBTwigStack: most robust and predictable INLJ when high selectivity: no guarantee about chosen plan without optimizer module UC Riverside Tree-Pattern Queries on a Lightweight XML Processor 28

29 Questions?