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Adaptive correlation exploitation in big data query optimization

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Abstract

Correlations among the data attributes are abundant and inherent in most application domains. These correlations, if managed in systematic and efficient ways, would enable various optimization opportunities. Unfortunately, the state-of-art techniques are all heavily tailored toward optimizing factors intrinsic to relational databases, e.g., predicate selectivity, random I/O accesses, and secondary indexes, which are mostly not applicable to the modern big data infrastructures, e.g., Hadoop and Spark. In this paper, we propose the EXORD\(^+\) system for exploiting the data’s correlations in big data query optimization. EXORD\(^+\) supports two types of correlations; hard (which does not allow for exceptions) and soft (which allows for exceptions). We introduce a three-phase approach for managing soft correlations including: (1) validating and judging the worthiness of soft correlations, (2) selecting and preparing the soft correlations for deployment, and (3) exploiting the correlations in query optimization. EXORD\(^+\) introduces a novel cost-benefit model for adaptively selecting the most beneficial soft correlations given a query workload. We show the complexity of this problem (NP-Hard) and propose a heuristic to efficiently solve it in a polynomial time. Moreover, we present incremental maintenance algorithms for efficiently updating the system’s state under data appends and workload changes. EXORD\(^+\) prototype is implemented as an extension to the Hive engine on top of Hadoop. The experimental evaluation shows the potential of EXORD\(^+\) in achieving more than 10x speedup while introducing minimal storage overheads.

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Notes

  1. We use term “MapReduce” to refer to the system and architecture, and term “map-reduce” to refer to a single job executed in the system.

  2. Depending on the PiggybackEnabled system-level configuration parameter (Sect. 3.1), the Prep4Deployment task is either piggybacked on the next user’s query or manually triggered as part of the “ASSESS CORRELATIONS ...” command (Sect. 7.1).

  3. If \(\mathcal {V}_{ORD}\) is not empty, then each mapper needs to examine these correlations.

  4. Definition 16 assumes un-weighted query workload and extends Definition 12. It is straightforward to extend Definition 15 for weighted query workload in the same manner.

  5. The system maintains few other configuration parameters that are omitted from the discussion.

  6. The system dumps continuous reports on the queries that are optimized by correlation-based rewriting, the queries that are not optimized, the number of times a correlation is used to optimized queries. These reports enable system admins to tune the system as desired, or even manually trigger re-assessment using the “ASSESS CORRELATIONS ..” command.

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Authors and Affiliations

  1. Computer Science Department, Worcester Polytechnic Institute (WPI), 100 Institute Rd., Worcester, MA, 01609, USA

    Yuchen Liu, Hai Liu, Dongqing Xiao & Mohamed Y. Eltabakh

Authors
  1. Yuchen Liu
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  2. Hai Liu
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  3. Dongqing Xiao
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  4. Mohamed Y. Eltabakh
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Correspondence to Mohamed Y. Eltabakh.

Additional information

This project is partially supported by NSF-CRI 1305258 Grant.

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Liu, Y., Liu, H., Xiao, D. et al. Adaptive correlation exploitation in big data query optimization. The VLDB Journal 27, 873–898 (2018). https://doi.org/10.1007/s00778-018-0515-8

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