Optimizing analytic data flows for multiple execution engines

  • Authors:

  • Alkis Simitsis;Kevin Wilkinson;Malu Castellanos;Umeshwar Dayal

  • Affiliations:

  • HP Labs, Palo Alto, CA, USA;HP Labs, Palo Alto, CA, USA;HP Labs, Palo Alto, CA, USA;HP Labs, Palo Alto, CA, USA

  • Venue:
  • SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
  • Year:
  • 2012

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Abstract

Next generation business intelligence involves data flows that span different execution engines, contain complex functionality like data/text analytics, machine learning operations, and need to be optimized against various objectives. Creating correct analytic data flows in such an environment is a challenging task and is both labor-intensive and time-consuming. Optimizing these flows is currently an ad-hoc process where the result is largely dependent on the abilities and experience of the flow designer. Our previous work addressed analytic flow optimization for multiple objectives over a single execution engine. This paper focuses on optimizing flows for a single objective, namely performance, over multiple execution engines. We consider flows that span a DBMS, a Map-Reduce engine, and an orchestration engine (e.g., an ETL tool or scripting language). This configuration is emerging as a common paradigm used to combine analysis of unstructured data with analysis of structured data (e.g., NoSQL plus SQL). We present flow transformations that model data shipping, function shipping, and operation decomposition and we describe how flow graphs are generated for multiple engines. Performance results for various configurations demonstrate the benefit of optimization.