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Optimizing Write Performance for Read Optimized Databases

  • Jens Krueger
  • Martin Grund
  • Christian Tinnefeld
  • Hasso Plattner
  • Alexander Zeier
  • Franz Faerber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)

Abstract

Compression in column-oriented databases has been proven to offer both performance enhancements and reductions in storage consumption. This is especially true for read access as compressed data can directly be processed for query execution.Nevertheless, compression happens to be disadvantageous when it comes to write access due to unavoidable re-compression: write-access requires significantly more data to be read than involved in the particular operation, more tuples may have to be modified depending on the compression algorithm, and table-level locks have to be acquired instead of row-level locks as long as no second version of the data is stored. As an effect the duration of a single modification — both insert and update — limits both throughput and response time significantly. In this paper, we propose to use an additional write-optimized buffer to maintain the delta that in conjunction with the compressed main store represents the current state of the data. This buffer facilitates an uncompressed, column-oriented data structure. To address the mentioned disadvantages of data compression, we trade write-performance for query-performance and memory consumption by using the buffer as an intermediate storage for several modifications which are then populated as a bulk in a merge operation. Hereby, the overhead created by one single re-compression is shared among all recent modifications. We evaluated our implementation inside SAP’s in memory column store. We then analyze the different parameters influencing the merge process, and make a complexity analysis. Finally, we show optimizations regarding resource consumption and merge duration.

Keywords

Query Execution Enterprise Application Document Vector Sort Order OLAP Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jens Krueger
    • 1
  • Martin Grund
    • 1
  • Christian Tinnefeld
    • 1
  • Hasso Plattner
    • 1
  • Alexander Zeier
    • 1
  • Franz Faerber
    • 2
  1. 1.Hasso–Plattner–InstitutPotsdamGermany
  2. 2.SAP AGWalldorf

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