【Spark九十五】Spark Shell操作Spark SQL

在Spark Shell上，通过创建HiveContext可以直接进行Hive操作
1. 操作Hive中已存在的表
[hadoop@hadoop bin]$ ./spark-shell
Spark assembly has been built with Hive, including Datanucleus jars on classpath
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 1.2.0
      /_/

Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_67)
Type in expressions to have them evaluated.
Type :help for more information.
Spark context available as sc.

////创建HiveContext
scala> val sqlContext = new org.apache.spark.sql.hive.HiveContext(sc)
sqlContext: org.apache.spark.sql.hive.HiveContext = org.apache.spark.sql.hive.HiveContext@42503a9b

///切换数据库，是一个本地命令，由Hive执行，但是没有生成分布式Job
scala> sqlContext.sql("use default");
res1: org.apache.spark.sql.SchemaRDD = 
SchemaRDD[0] at RDD at SchemaRDD.scala:108
== Query Plan ==
<Native command: executed by Hive>

///显示当前数据库的表，是一个transformation操作，生成RDD
scala> sqlContext.sql("show tables");
res2: org.apache.spark.sql.SchemaRDD = 
SchemaRDD[2] at RDD at SchemaRDD.scala:108
== Query Plan ==
<Native command: executed by Hive>

////获取结果
scala> sqlContext.sql("show tables").collect;
res3: Array[org.apache.spark.sql.Row] = Array([abc], [avro_table], [employees], [invites], [my_word], [mytable1], [parquet_table], [table1], [word], [word3], [word4], [word5], [word6])

///执行切换数据库动作
scala> sqlContext.sql("use default").collect;
res4: Array[org.apache.spark.sql.Row] = Array()

////从表中查找数据，word6是一个Hive表，是一个transformation操作
scala> sqlContext.sql("select * from word6")
res5: org.apache.spark.sql.SchemaRDD = 
SchemaRDD[8] at RDD at SchemaRDD.scala:108
== Query Plan ==
== Physical Plan ==
HiveTableScan [id#20,word#21], (MetastoreRelation default, word6, None), None

////从表中查找数据，word6是一个Hive表，执行具体的查询
scala> sqlContext.sql("select * from word6").collect
【Spark九十五】Spark Shell操作Spark SQL

猜你喜欢
