Parallel join algorithms based on parallel B/sup +/-trees

In-Memory Interval Joins

The VLDB Journal ◽

10.1007/s00778-020-00639-0 ◽

2021 ◽

Author(s):

Panagiotis Bouros ◽

Nikos Mamoulis ◽

Dimitrios Tsitsigkos ◽

Manolis Terrovitis

Keyword(s):

Parallel Computation ◽

State Of The Art ◽

Complex Data ◽

Plane Sweep ◽

Join Algorithm ◽

Sweep Algorithm ◽

Join Algorithms ◽

Domain Partitioning ◽

Complex Data Structure ◽

Independent Tasks

AbstractThe interval join is a popular operation in temporal, spatial, and uncertain databases. The majority of interval join algorithms assume that input data reside on disk and so, their focus is to minimize the I/O accesses. Recently, an in-memory approach based on plane sweep (PS) for modern hardware was proposed which greatly outperforms previous work. However, this approach relies on a complex data structure and its parallelization has not been adequately studied. In this article, we investigate in-memory interval joins in two directions. First, we explore the applicability of a largely ignored forward scan (FS)-based plane sweep algorithm, for single-threaded join evaluation. We propose four optimizations for FS that greatly reduce its cost, making it competitive or even faster than the state-of-the-art. Second, we study in depth the parallel computation of interval joins. We design a non-partitioning-based approach that determines independent tasks of the join algorithm to run in parallel. Then, we address the drawbacks of the previously proposed hash-based partitioning and suggest a domain-based partitioning approach that does not produce duplicate results. Within our approach, we propose a novel breakdown of the partition-joins into mini-joins to be scheduled in the available CPU threads and propose an adaptive domain partitioning, aiming at load balancing. We also investigate how the partitioning phase can benefit from modern parallel hardware. Our thorough experimental analysis demonstrates the advantage of our novel partitioning-based approach for parallel computation.

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Cache-efficient sweeping-based interval joins for extended Allen relation predicates

The VLDB Journal ◽

10.1007/s00778-020-00650-5 ◽

2021 ◽

Author(s):

Danila Piatov ◽

Sven Helmer ◽

Anton Dignös ◽

Fabio Persia

Keyword(s):

Data Structure ◽

Experimental Evaluation ◽

State Of The Art ◽

Temporal Databases ◽

Access Method ◽

Wide Range ◽

Interval Relation ◽

Cache Efficient ◽

Join Algorithms ◽

Better Than

AbstractWe develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing.

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