Multi-Agent Join
Keywords: Data Systems, Join Processing, Online Learning, Many-Armed Bandit, Multi-Agent Learning
Abstract: Real-time performance is crucial for interactive and exploratory data analysis,
where users require quick access to subsets or progressive presentations of query
results. Delivering real-time results over large data for common relational binary
operators like join is challenging, as join algorithms often spend considerable time
scanning and attempting to join parts of relations that may not produce any results.
Existing solutions often involve repetitive preprocessing, which is costly and may
not be feasible for interactive workloads or evolving datasets. Additionally, these
solutions may support only restricted types of joins. This paper presents a novel
approach for achieving efficient progressive join processing. The scan operator of
the join learns online during query execution, identifying portions of its underlying
relation that satisfy the join condition. Additionally, an algorithm is introduced
where both scan operators collaboratively learn to optimize join execution.
Submission Number: 17
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