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Relax and Adapt: Computing Top-k Matches to XPath Queries

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Presentation on theme: "Relax and Adapt: Computing Top-k Matches to XPath Queries"— Presentation transcript:

1 Relax and Adapt: Computing Top-k Matches to XPath Queries
Amélie Marian (Columbia University) Joint work with: Sihem Amer-Yahia (AT&T Research) Nick Koudas (University of Toronto) Divesh Srivastava (AT&T Research)

2 Amélie Marian - Columbia University
Example book book book title (Great Expectations) edition (paperback) info author (Dickens) info info author (Dickens) title (Great Expectations) edition (paperback) title (Great Expectations) author (Dickens) Heterogeneous XML Data about books Query: book[./info/title=“Great Expectations”] and [./info/author=“Dickens”] and [./edition=“paperback”] book title (Great Expectations) edition (paperback) info author (Dickens) Query root node: Distinguished node 7/25/2019 Amélie Marian - Columbia University

3 Amélie Marian - Columbia University
XML Query Relaxation Query [Amer-Yahia et al. EDBT’02] book title (Great Expectations) edition (paperback) info author (Dickens) Tree pattern relaxations: Leaf node deletion Edge generalization Subtree promotion book book book title (Great Expectations) edition (paperback) info author (Dickens) Data edition? info info author (Dickens) title (Great Expectations) edition (paperback) title (Great Expectations) author (Dickens) 7/25/2019 Amélie Marian - Columbia University

4 Top-k Queries over XML Data: Motivations and Challenges
Structure heterogeneity Efficient identification of approximate matches Top-k Ranking of approximate matches based on similarity to query Early pruning Query processing cost Cost increases with number of matches evaluated Data explosion Many approximate matches XML path queries akin to joins Prioritization to increase pruning 7/25/2019 Amélie Marian - Columbia University

5 Amélie Marian - Columbia University
Contributions Whirlpool: adaptive architecture and top-k query processing strategy for XPath queries Goal: early pruning of non-top-k partial matches Approach: partial matches may follow different plans, and may be at different stages of query execution Real prototype implementation of Whirlpool Instantiation of Whirlpool for various “routing strategies” and “prioritization” alternatives 7/25/2019 Amélie Marian - Columbia University

6 Amélie Marian - Columbia University
Closely Related Work Adaptive query processing Eddies: Dynamic query join plans to adapt to processing environment No pruning Adaptive top-k query processing Upper: Prioritization of partial matches based on maximum possible scores Adaptive routing based on scores No joins [Avnur and Hellerstein. SIGMOD’00] [Bruno et al. ICDE’01] 7/25/2019 Amélie Marian - Columbia University

7 Amélie Marian - Columbia University
Outline Whirlpool Architecture Query Processing Strategy Alternatives Evaluation Settings Evaluation Results 7/25/2019 Amélie Marian - Columbia University

8 Whirlpool Architecture
book info edition (paperback) Router author (Dickens) title (Great Expectations) book server edition server title server info server author server Top-k Set 7/25/2019 Amélie Marian - Columbia University

9 Whirlpool Architecture: Components
Top-k Set Only one match with a given root node Used for pruning Complete matches are not processed further, incomplete matches are sent to the router Router Router Queue is based on partial matches maximum possible final scores Dynamically choose which server to send partial match based on routing strategy 7/25/2019 Amélie Marian - Columbia University

10 Whirlpool Architecture: Components
Root server: Generates candidate matches Node servers: Maintain priority queue of partial matches For each partial match that is processed: Compute a set of extended partial (or complete) matches Compute scores of new matches Checks partial matches against current top-k set 7/25/2019 Amélie Marian - Columbia University

11 Query Processing Alternatives
Prioritization Strategies (at each server) FIFO Current Score Maximum Possible Next Score Maximum Possible Final Score Routing Decisions (at the router) Static Score-based Likely to increase score the most Likely to increase score the least Size-based Likely to produce the fewest matches 7/25/2019 Amélie Marian - Columbia University

12 Evaluation Strategies
Lockstep (Static) Partial matches follow same execution plan Partial matches have gone through exactly the same number of operations Whirlpool Single-threaded (Adaptive) Partial matches adaptively routed Process the partial match with the highest maximum final score (Query processing similar to Upper) Only one partial match processed at a time Whirlpool Multi-threaded (Adaptive) Prioritization strategy at server decides which partial match to process next at server System determines which server to process next 7/25/2019 Amélie Marian - Columbia University

13 Amélie Marian - Columbia University
Evaluation Metrics Parameters: Query size Document size k Parallelism Scoring function (tf.idf proposed in paper) Measures: Query execution time Number of server operations Number of partial matches created 7/25/2019 Amélie Marian - Columbia University

14 Amélie Marian - Columbia University
Evaluation Setting C++ implementation, with POSIX threads Default machine: Red Hat 7.1 Linux 1.4GHz dual processor 2Gb RAM XML Documents generated using XMark generating tool XPath Queries chosen from XMark to illustrate different relaxations XML nodes stored using Dewey encoding 7/25/2019 Amélie Marian - Columbia University

15 Comparison of Adaptive Routing Strategies
Whirlpool-S and Whirlpool-M perform approximately the same number of server operations 7/25/2019 Amélie Marian - Columbia University

16 Static Routing Strategies vs. Best Adaptive
7/25/2019 Amélie Marian - Columbia University

17 Amélie Marian - Columbia University
Effect of Parallelism 7/25/2019 Amélie Marian - Columbia University

18 Varying Query Size and k (log scale)
60% 48% 20% For large queries and high values of k, Whirlpool-M performs less server operations that Whirlpool-S (and is faster even on a one-processor machine)! (27% less server operations for q3 k=75) 7/25/2019 Amélie Marian - Columbia University

19 Varying Query Size and Document Size
Almost twice as fast 7/25/2019 Amélie Marian - Columbia University

20 Amélie Marian - Columbia University
Scalability Document Size 1M 10M 50M Q1 100% 93.12% 85.66% Q2 49.56% 67.66% Q3 39.59% 31.20% Percentage of partial matches created by Whirlpool-M as a function of the maximum possible number of partial matches 7/25/2019 Amélie Marian - Columbia University

21 Amélie Marian - Columbia University
Conclusions Efficient adaptive top-k query processing strategy Minimize number of partial matches evaluated Benefit from parallelism with little threading overhead Adapt to different environments Score distribution Selectivity distribution Extensive experimental evaluation Good scalability 7/25/2019 Amélie Marian - Columbia University

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