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Conference paper(2016)
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Alexander Alexandrov, Andreas Salzmann, Georgi Krastev, Asterios Katsifodimos, Volker Markl
Parallel dataow APIs based on second-order functions were originally seen as a exible alternative to SQL. Over time, however, their complexity increased due to the number of physical aspects that had to be exposed by the underlying engines in order to facilitate efficient execution. To retain a sufficient level of abstraction and lower the barrier of entry for data scientists, projects like Spark and Flink currently offer domain-specific APIs on top of their parallel collection abstractions. This demonstration highlights the benefits of an alternative design based on deep language embedding. We showcase Emma-A programming language embedded in Scala. Emma promotes parallel collection processing through native constructs like Scala's for-comprehensions-A declarative syntax akin to SQL. In addition, Emma also advocates quoting the entire data analysis algorithm rather than its individual dataow expressions. This allows for decomposing the quoted code into (sequential) control ow and (parallel) dataow fragments, optimizing the dataows in context, and transparently offloading them to an engine like Spark or Flink. The proposed design promises increased programmer productivity due to avoiding an impedance mismatch, thereby reducing the lag times and cost of data analysis.
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Parallel dataow APIs based on second-order functions were originally seen as a exible alternative to SQL. Over time, however, their complexity increased due to the number of physical aspects that had to be exposed by the underlying engines in order to facilitate efficient execution. To retain a sufficient level of abstraction and lower the barrier of entry for data scientists, projects like Spark and Flink currently offer domain-specific APIs on top of their parallel collection abstractions. This demonstration highlights the benefits of an alternative design based on deep language embedding. We showcase Emma-A programming language embedded in Scala. Emma promotes parallel collection processing through native constructs like Scala's for-comprehensions-A declarative syntax akin to SQL. In addition, Emma also advocates quoting the entire data analysis algorithm rather than its individual dataow expressions. This allows for decomposing the quoted code into (sequential) control ow and (parallel) dataow fragments, optimizing the dataows in context, and transparently offloading them to an engine like Spark or Flink. The proposed design promises increased programmer productivity due to avoiding an impedance mismatch, thereby reducing the lag times and cost of data analysis.
Journal article(2016)
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Alexander Alexandrov, Asterios Katsifodimos, Georgi Krastev, Volker Markl
Parallel collection processing based on second-order functions such as map and reduce has been widely adopted for scalable data analysis. Initially popularized by Google, over the past decade this programming paradigm has found its way in the core APIs of parallel dataflow engines such as Hadoop's MapReduce, Spark's RDDs, and Flink's DataSets. We review programming patterns typical of these APIs and discuss how they relate to the underlying parallel execution model. We argue that fixing the abstraction leaks exposed by these patterns will reduce the cost of data analysis due to improved programmer productivity. To achieve that, we first revisit the algebraic foundations of parallel collection processing. Based on that, we propose a simplified API that (i) provides proper support for nested collection processing and (ii) alleviates the need of certain second-order primitives through comprehensions - a declarative syntax akin to SQL. Finally, we present a metaprogramming pipeline that performs algebraic rewrites and physical optimizations which allow us to target parallel dataflow engines like Spark and Flink with competitive performance.
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Parallel collection processing based on second-order functions such as map and reduce has been widely adopted for scalable data analysis. Initially popularized by Google, over the past decade this programming paradigm has found its way in the core APIs of parallel dataflow engines such as Hadoop's MapReduce, Spark's RDDs, and Flink's DataSets. We review programming patterns typical of these APIs and discuss how they relate to the underlying parallel execution model. We argue that fixing the abstraction leaks exposed by these patterns will reduce the cost of data analysis due to improved programmer productivity. To achieve that, we first revisit the algebraic foundations of parallel collection processing. Based on that, we propose a simplified API that (i) provides proper support for nested collection processing and (ii) alleviates the need of certain second-order primitives through comprehensions - a declarative syntax akin to SQL. Finally, we present a metaprogramming pipeline that performs algebraic rewrites and physical optimizations which allow us to target parallel dataflow engines like Spark and Flink with competitive performance.
