AI Article Synopsis
- The study focuses on how to efficiently compute distributed equi-joins in databases when there's an imbalance in data distribution (join-attribute skew), leading to uneven workload.
- Addressing this skew requires finer partitioning of data, but this also means duplicating input, which creates a balance between overall load and the variance in that load.
- The researchers demonstrate that minimizing load variance with certain constraints can be framed as a specific optimization problem, allowing for effective algorithms that significantly enhance performance, even under deterministic load assignments.
Article Abstract
We study distributed equi-join computation in the presence of join-attribute skew, which causes load imbalance. Skew can be addressed by more fine-grained partitioning, at the cost of input duplication. For random load assignment, e.g., using a hash function, fine-grained partitioning creates a tradeoff between load expectation and variance. We show that minimizing load variance subject to a constraint on expectation is a monotone submodular maximization problem with Knapsack constraints, hence admitting provably near-optimal greedy solutions. In contrast to previous work on formal optimality guarantees, we can prove this result also for self-joins and more general load functions defined as weighted sum of input and output. We further demonstrate through experiments that this theoretical result leads to an effective algorithm for the problem of minimizing running time, even when load is assigned deterministically.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6404978 | PMC |
| http://dx.doi.org/10.1145/3183713.3183728 | DOI Listing |
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