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We developed a recursive partitioning method to optimize band-joins in distributed systems. This approach balances worker load and input duplication, significantly improving join performance and reducing costs.

Keywords:
band-joindistributed joinsrunning-time optimization

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Area of Science:

  • Computer Science
  • Database Systems
  • Distributed Computing

Background:

  • Distributed systems require efficient data partitioning for tasks like band-joins.
  • Balancing maximum load per worker and input duplication is a key challenge in distributed data processing.
  • Existing methods for band-join partitioning face high optimization costs or restricted partitioning strategies.

Purpose of the Study:

  • To develop a novel approach for optimizing running-time performance of band-joins in distributed systems.
  • To resolve the tension between maximum load per worker and input duplication.
  • To improve upon previous methods in terms of optimization cost and join performance.

Main Methods:

  • Recursive partitioning of the join-attribute space.
  • Utilizing an appropriate split scoring measure for partitioning.
  • Evaluating the method for one-dimensional and multi-attribute band-joins.

Main Results:

  • The recursive partitioning method achieves low optimization cost and low join cost.
  • The approach is effective for both one-dimensional and multi-attribute band-joins.
  • Experimental results show partitionings within 10% of the lower bound for load and duplication.

Conclusions:

  • Recursive partitioning of the join-attribute space is an effective strategy for optimizing band-joins.
  • This method offers significant improvements over previous work in distributed systems.
  • The approach successfully balances load and duplication, leading to enhanced join performance.