Using Spark with MongoDB
I recently started investigating Apache Spark as a framework for data mining. Spark builds upon Apache Hadoop, and allows a multitude of operations more than map-reduce. It also supports streaming data with iterative algorithms. Since Spark builds upon Hadoop and HDFS, it is compatible with any HDFS data source. Our server uses MongoDB, so we naturally turned to the mongo-hadoop connector, which a
Apache Spark SQL and Drill -- DAG vs. tree execution, value vector vs. Schema RDD format. What are the pros and cons?
Answer: I think that there is no real difference of architecture, but difference of "exposed layers". Drill must have shuffle in some way to support joins, Spark has "Tree aggregate" which can be called "Dremel style aggregation tree". Difference is that Spark has "exposed" execution engine with ...
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