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Accelerating SPARQL queries by exploiting hash-based locality and adaptive partitioning

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Abstract

State-of-the-art distributed RDF systems partition data across multiple computer nodes (workers). Some systems perform cheap hash partitioning, which may result in expensive query evaluation. Others try to minimize inter-node communication, which requires an expensive data preprocessing phase, leading to a high startup cost. Apriori knowledge of the query workload has also been used to create partitions, which, however, are static and do not adapt to workload changes. In this paper, we propose AdPart, a distributed RDF system, which addresses the shortcomings of previous work. First, AdPart applies lightweight partitioning on the initial data, which distributes triples by hashing on their subjects; this renders its startup overhead low. At the same time, the locality-aware query optimizer of AdPart takes full advantage of the partitioning to (1) support the fully parallel processing of join patterns on subjects and (2) minimize data communication for general queries by applying hash distribution of intermediate results instead of broadcasting, wherever possible. Second, AdPart monitors the data access patterns and dynamically redistributes and replicates the instances of the most frequent ones among workers. As a result, the communication cost for future queries is drastically reduced or even eliminated. To control replication, AdPart implements an eviction policy for the redistributed patterns. Our experiments with synthetic and real data verify that AdPart: (1) starts faster than all existing systems; (2) processes thousands of queries before other systems become online; and (3) gracefully adapts to the query load, being able to evaluate queries on billion-scale RDF data in subseconds.

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Notes

  1. http://www.bio2rdf.org/

  2. http://yago-knowledge.org/

  3. http://www.w3.org/TR/rdf-sparql-query/

  4. For simplicity, we use: \(i = t.subject \mod W\).

  5. In many RDF datasets, vertex degrees follow a power-law distribution, where few ones have extremely high degrees. For example, vertices that appear as objects in triples with rdf:type have very high degree centrality. Treating such vertices as cores results in imbalanced partitions and prevents the system from taking full advantage of parallelism [19].

  6. Recall if a core vertex is a subject, we do not redistribute.

  7. Auto-tuning the frequency threshold is a subject of our future work.

  8. http://swat.cse.lehigh.edu/projects/lubm/

  9. http://db.uwaterloo.ca/watdiv/

  10. http://yago-knowledge.org/

  11. http://download.bio2rdf.org/release/2/

  12. http://cloud.kaust.edu.sa/Pages/adpart.aspx

  13. http://db.uwaterloo.ca/watdiv/basic-testing.shtml

  14. Only query patterns are used. Classes and properties are fixed so queries return large intermediate results.

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Authors and Affiliations

  1. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Razen Harbi, Ibrahim Abdelaziz, Panos Kalnis & Majed Sahli

  2. University of Ioannina, Ioannina, Greece

    Nikos Mamoulis

  3. Microsoft Corporation, Redmond, WA, 98052, USA

    Yasser Ebrahim

Authors
  1. Razen Harbi
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  2. Ibrahim Abdelaziz
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  3. Panos Kalnis
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  4. Nikos Mamoulis
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  5. Yasser Ebrahim
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  6. Majed Sahli
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Corresponding author

Correspondence to Razen Harbi.

Appendices

Appendix 1: Workload queries repetition

In this experiment, we test AdPart’s performance using a real scenario workload where a certain percentage of the queries is repeated, while other new queries are taken into account. We use three workloads, each workload contains 10 K  LUBM random queries out of which a certain percentage is repeated. Figure 20a shows AdPart’s performance while varying the amount of repeated queries between 20, 40 and 80 %. As the results suggest, the more the repeated queries, the less the workload execution time. Since AdPart monitors the query patterns and not the individual queries, it could capture most of the patterns in the workload even with only 20 % of its queries repeated.

Appendix 2: Average partition size

In this experiment, we report how the average partition size changes during the workload execution. Using the 10K queries LUBM workload, Fig. 20b shows how the partition size increases as more queries are executed. Initially, each partition contains around 19M triples. This corresponds to a 0 % replication ratio as AdPart loads only the original dataset. As the system adapts, the size of each partition slightly increases till reaching an average size of around 33 M triples, which counts for a 72 % replication ratio after executing the whole 10K workload queries.

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Harbi, R., Abdelaziz, I., Kalnis, P. et al. Accelerating SPARQL queries by exploiting hash-based locality and adaptive partitioning. The VLDB Journal 25, 355–380 (2016). https://doi.org/10.1007/s00778-016-0420-y

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