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author | Xin Ren <iamshrek@126.com> | 2016-09-16 16:31:23 -0700 |
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committer | Xin Ren <iamshrek@126.com> | 2016-09-16 16:31:23 -0700 |
commit | eee58685c39269c191a921c39f1520c747a42318 (patch) | |
tree | 3d28771ee1d32c285250a054c12a7cd1ea63e39e | |
parent | a78faf5822bca343694776ea3ec8457fa780f09f (diff) | |
download | spark-website-eee58685c39269c191a921c39f1520c747a42318.tar.gz spark-website-eee58685c39269c191a921c39f1520c747a42318.tar.bz2 spark-website-eee58685c39269c191a921c39f1520c747a42318.zip |
replace with valid url to rdd paper
-rw-r--r-- | research.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/research.md b/research.md index 41841a1c7..ec7dd54d8 100644 --- a/research.md +++ b/research.md @@ -27,7 +27,7 @@ Traditional MapReduce and DAG engines are suboptimal for these applications beca </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 <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>). +Spark offers an abstraction called <a href="http://people.csail.mit.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 the following papers:</p> |