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diff --git a/research.md b/research.md index 858acbc31..c7f3070b4 100644 --- a/research.md +++ b/research.md @@ -23,14 +23,14 @@ Our goal was to design a programming model that supports a much wider class of a </ul> <p> -MapReduce and Dryad are suboptimal for these applications because they are based on acyclic data flow: an application has to run as a series of distinct jobs, each of which reads data from stable storage (e.g. a distributed file system) and writes it back to stable storage. They incur significant cost loading the data on each step and writing it back to replicated storage. +Traditional MapReduce and DAG engines are suboptimal for these applications because they are based on acyclic data flow: an application has to run as a series of distinct jobs, each of which reads data from stable storage (e.g. a distributed file system) and writes it back to stable storage. They incur significant cost loading the data on each step and writing it back to replicated storage. </p> <p> -Spark offers an abstraction called <a href="http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf"><em>resilient distributed datasets (RDDs)</em></a> to support these applications efficiently. RDDs can be stored in memory between queries <em>without</em> requiring replication. Instead, they rebuild lost data on failure using <em>lineage</em>: each RDD remembers how it was built from other datasets (by transformations like <em>map</em>, <em>join</em> or <em>group-by</em>) to rebuild itself. RDDs allow Spark to outperform existing models by up to 100x in multi-pass analytics. We showed that RDDs can support a wide variety of iterative algorithms, as well as interactive data mining and a highly efficient SQL engine (the <a href="http://shark.cs.berkeley.edu">Shark</a> project). +Spark offers an abstraction called <a href="http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf"><em>resilient distributed datasets (RDDs)</em></a> to support these applications efficiently. RDDs can be stored in memory between queries <em>without</em> requiring replication. Instead, they rebuild lost data on failure using <em>lineage</em>: each RDD remembers how it was built from other datasets (by transformations like <code>map</code>, <code>join</code> or <code>groupBy</code>) to rebuild itself. RDDs allow Spark to outperform existing models by up to 100x in multi-pass analytics. We showed that RDDs can support a wide variety of iterative algorithms, as well as interactive data mining and a highly efficient SQL engine (<a href="http://shark.cs.berkeley.edu">Shark</a>). </p> -<p class="noskip">You can find more about the research behind Spark in our papers:</p> +<p class="noskip">You can find more about the research behind Spark in the following papers:</p> <ul> <li> |