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Robert Bradshaw

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    Preview abstract Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business (e.g. Web logs, mobile usage statistics, and sensor networks). At the same time, consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves, in addition to an insatiable hunger for faster answers. Meanwhile, practicality dictates that one can never fully optimize along all dimensions of correctness, latency, and cost for these types of input. As a result, data processing practitioners are left with the quandary of how to reconcile the tensions between these seemingly competing propositions, often resulting in disparate implementations and systems. We propose that a fundamental shift of approach is necessary to deal with these evolved requirements in modern data processing. We as a field must stop trying to groom unbounded datasets into finite pools of information that eventually become complete, and instead live and breathe under the assumption that we will never know if or when we have seen all of our data, only that new data will arrive, old data may be retracted, and the only way to make this problem tractable is via principled abstractions that allow the practitioner the choice of appropriate tradeoffs along the axes of interest: correctness, latency, and cost. In this paper, we present one such approach, the Dataflow Model, along with a detailed examination of the semantics it enables, an overview of the core principles that guided its design, and a validation of the model itself via the real-world experiences that led to its development. View details
    Cython: The Best of Both Worlds
    Stefan Behnel
    Craig Citro
    Lisandro Dalcin
    Dag Sverre Seljebotn
    Kurt Smith
    Computing in Science and Engineering, vol. 13.2 (2011), pp. 31-39
    Preview abstract Cython is an extension to the Python language that allows explicit type declarations and is compiled directly to C. This addresses Python's large overhead for numerical loops and the difficulty of efficiently making use of existing C and Fortran code, which Cython code can interact with natively. The Cython language combines the speed of C with the power and simplicity of the Python language. View details
    FlumeJava: Easy, Efficient Data-Parallel Pipelines
    Craig Chambers
    Ashish Raniwala
    Frances Perry
    Stephen Adams
    Robert Henry
    ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), ACM New York, NY 2010, 2 Penn Plaza, Suite 701 New York, NY 10121-0701 (2010), pp. 363-375
    Preview abstract MapReduce and similar systems significantly ease the task of writing data-parallel code. However, many real-world computations require a pipeline of MapReduces, and programming and managing such pipelines can be difficult. We present FlumeJava, a Java library that makes it easy to develop, test, and run efficient dataparallel pipelines. At the core of the FlumeJava library are a couple of classes that represent immutable parallel collections, each supporting a modest number of operations for processing them in parallel. Parallel collections and their operations present a simple, high-level, uniform abstraction over different data representations and execution strategies. To enable parallel operations to run efficiently, FlumeJava defers their evaluation, instead internally constructing an execution plan dataflow graph. When the final results of the parallel operations are eventually needed, FlumeJava first optimizes the execution plan, and then executes the optimized operations on appropriate underlying primitives (e.g., MapReduces). The combination of high-level abstractions for parallel data and computation, deferred evaluation and optimization, and efficient parallel primitives yields an easy-to-use system that approaches the efficiency of hand-optimized pipelines. FlumeJava is in active use by hundreds of pipeline developers within Google. Categories and Subject Descriptors D.1.3 [Concurrent Programming]: Parallel Programming General Terms Algorithms, Languages, Performance Keywords data-parallel programming, MapReduce, Java View details
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