September 2016
Beginner to intermediate
326 pages
6h 47m
English
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Introducing Hivemall for Spark
Apache Hive supports three execution engines—MapReduce, Tez, and Spark. Though Hivemall does not support Spark natively, the Hivemall for Spark project (https://github.com/maropu/hivemall-spark) implements a wrapper for Spark. This wrapper enables you to use Hivemall UDFs in SparkContext, DataFrames, or Spark Streaming. It is really easy to get started with Hivemall for Spark. Follow this procedure to start a Scala shell, load UDFs, and execute SQLs:
- Download the
define-udfsscript:[cloudera@quickstart ~]$ wget https://raw.githubusercontent.com/maropu/hivemall-spark/master/scripts/ddl/define-udfs.sh --no-check-certificate - Start a Scala shell with the
packagesoption:[cloudera@quickstart ~]$ spark-1.6.0-bin-hadoop2.6/bin/spark-shell ...
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