Parallel programming framework for large batch transaction processing on scale-out systems

  • Authors:

  • Kazuaki Ishizaki;Ken Mizuno;Toshio Suganuma;Daniel Silva;Akira Koseki;Hideaki Komatsu;Yohei Ueda;Toshio Nakatani

  • Affiliations:

  • IBM Research - Tokyo;IBM Research - Tokyo;IBM Japan;IBM Corporation;IBM Research - Tokyo;IBM Research - Tokyo;IBM Research - Tokyo;IBM Research - Tokyo

  • Venue:
  • Proceedings of the 3rd Annual Haifa Experimental Systems Conference
  • Year:
  • 2010

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Abstract

A scale-out system is a cluster of commodity machines, and offers a good platform to support steadily increasing workloads that process growing data sets. Sharding [4] is a method of partitioning data and processing a computation on a scale-out system. In a database system, a large table can be partitioned into small tables so each node can process its part of the computation. The sharding approach in a large batch transaction processing, which is important in financial area, presents two hard problems to programmers. Programmers have to write complex code (1) to transfer the input data so as to align the computations with the data partitions, and (2) to manage the distributed transactions. This paper presents a new parallel programming framework that makes parallel transactional programming easier by specifying transaction scopes and partitioners to simplify the code. Transaction scopes include series of subtransactions, each of which performs local operations. The system manages the distributed transactions automatically. A partitioner represents how the computation should be decomposed and aligned with the data partitions to avoid remote database accesses. Between paired of subtransactions, the system handles the data shuffling across the network. We implemented our parallel programming framework as a new Java class library. We hide all of the complex details of data transfer and distributed transaction management in the library. Our programming framework can eliminate almost 66% of the lines of code compared to a current programming approach without programming framework support. We also confirmed good scalability, with a scaling factor of 20.6 on 24 nodes using our modified batch program for the TPC-C benchmark.