Scalable Data-Intensive Analytics

Abstract

To effectively handle the scale of processing required in information extraction and analytical tasks in an era of information explosion, partitioning the data streams and applying computation to each partition in parallel is the key. Even though the concept of MapReduce has been around for some time and is well known in the functional programming literatures, it is Google which demonstrated that this very high-level abstraction is especially suitable for data-intensive computation and potentially has very high performance implementation as well. If we observe the behavior of a query plan on a modern shared-nothing parallel database system such as Teradata and HP NeoView, one notices that it also offers large-scale parallel processing while maintaining the high level abstraction of a declarative query language. The correspondence between the MapReduce parallel processing paradigm and the paradigm for parallel query processing has been observed. In addition to integrated schema management and declarative query language, the strengths of parallel SQL engines also include workload management and richer expressive power and parallel processing patterns. Compared to the MapReduce parallel processing paradigm, however, the parallel query processing paradigm has focused on native, built-in, algebraic query operators that are supported in the SQL language. Parallel query processing engines lack the ability to efficiently handle dynamically-defined procedures. While the “user-defined function” in SQL can be used to inject dynamically defined procedures, the ability of standard SQL to support flexibility of their invocation, and efficient implementation of these user-defined functions, especially in a highly scaled-out architecture, are not adequate. This paper discusses some issues and approaches in integrating large scale information extraction and analytical tasks with parallel data management.