Size Bounds for Factorised Representations of Query Results

Olteanu, Dan; Závodný, Jakub

doi:10.1145/2656335

Cited by 107 publications

(167 citation statements)

References 39 publications

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“…Similar size bounds were also obtained for the factorised representations of query results [119], where relations are succinctly represented through relational algebra expressions comprising Cartesian products, relations with singleton tuples, and their unions.…”

Section: More Accurate Boundssupporting

confidence: 60%

Hypertree Decompositions

Gottlob

Greco

Leone

et al. 2016

Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

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In the database context, the hypertree decomposition method is used for query optimization, whereby conjunctive queries having a low degree of cyclicity can be recognized and decomposed automatically, and efficiently evaluated. Hypertree decompositions were introduced at ACM PODS 1999. The present paper reviews -in form of questions and answers-the main relevant concepts and algorithms and surveys selected related work including applications and test results.

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Section: More Accurate Boundssupporting

confidence: 60%

Hypertree Decompositions

Gottlob

Greco

Leone

et al. 2016

Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

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“…Instead, the cache will not be utilized in the a1a2a3a4 plan. Our cache gives benefits similar to factorization [33]. In factorized processing, the results of a query are represented as Cartesian products of independent components of the query.…”

Section: Intersection Cache Hitsmentioning

confidence: 99%

Optimizing subgraph queries by combining binary and worst-case optimal joins

2019

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We study the problem of optimizing subgraph queries using the new worst-case optimal join plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time using multiway intersections. The core problem in optimizing worst-case optimal plans is to pick an ordering of the query vertices to match. We design a cost-based optimizer that (i) picks efficient query vertex orderings for worst-case optimal plans; and (ii) generates hybrid plans that mix traditional binary joins with worst-case optimal style multiway intersections. Our cost metric combines the cost of binary joins with a new cost metric called intersection-cost. The plan space of our optimizer contains plans that are not in the plan spaces based on tree decompositions from prior work. In addition to our optimizer, we describe an adaptive technique that changes the orderings of the worst-case optimal sub-plans during query execution. We demonstrate the effectiveness of the plans our optimizer picks and adaptive technique through extensive experiments. Our optimizer is integrated into the Graphflow DBMS.

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“…Previous work has investigated aggregations over hypertree decompositions [13, 48]. EmptyHeaded adopts this previous work in a straightforward way.…”

Section: Query Compilermentioning

confidence: 99%

EmptyHeaded

Aberger

Lamb

et al. 2016

Proceedings of the 2016 International Conference on Management of Data

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There are two types of high-performance graph processing engines: low- and high-level engines. Low-level engines (Galois, PowerGraph, Snap) provide optimized data structures and computation models but require users to write low-level imperative code, hence ensuring that efficiency is the burden of the user. In high-level engines, users write in query languages like datalog (SociaLite) or SQL (Grail). High-level engines are easier to use but are orders of magnitude slower than the low-level graph engines. We present EmptyHeaded, a high-level engine that supports a rich datalog-like query language and achieves performance comparable to that of low-level engines. At the core of EmptyHeaded’s design is a new class of join algorithms that satisfy strong theoretical guarantees but have thus far not achieved performance comparable to that of specialized graph processing engines. To achieve high performance, EmptyHeaded introduces a new join engine architecture, including a novel query optimizer and data layouts that leverage single-instruction multiple data (SIMD) parallelism. With this architecture, EmptyHeaded outperforms high-level approaches by up to three orders of magnitude on graph pattern queries, PageRank, and Single-Source Shortest Paths (SSSP) and is an order of magnitude faster than many low-level baselines. We validate that EmptyHeaded competes with the best-of-breed low-level engine (Galois), achieving comparable performance on PageRank and at most 3× worse performance on SSSP.

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Size Bounds for Factorised Representations of Query Results

Cited by 107 publications

References 39 publications

Hypertree Decompositions

Hypertree Decompositions

Optimizing subgraph queries by combining binary and worst-case optimal joins

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