Fast updates on read-optimized databases using multi-core CPUs

Author:

Krueger Jens1,Kim Changkyu2,Grund Martin1,Satish Nadathur2,Schwalb David1,Chhugani Jatin2,Plattner Hasso1,Dubey Pradeep2,Zeier Alexander1

Affiliation:

1. Hasso-Plattner-Institute, Potsdam, Germany

2. Parallel Computing Lab, Intel Corporation

Abstract

Read-optimized columnar databases use differential updates to handle writes by maintaining a separate write-optimized delta partition which is periodically merged with the read-optimized and compressed main partition. This merge process introduces significant overheads and unacceptable downtimes in update intensive systems, aspiring to combine transactional and analytical workloads into one system. In the first part of the paper, we report data analyses of 12 SAP Business Suite customer systems. In the second half, we present an optimized merge process reducing the merge overhead of current systems by a factor of 30. Our linear-time merge algorithm exploits the underlying high compute and bandwidth resources of modern multi-core CPUs with architecture-aware optimizations and efficient parallelization. This enables compressed in-memory column stores to handle the transactional update rate required by enterprise applications, while keeping properties of read-optimized databases for analytic-style queries.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

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