sparkSql两个最重要的类SqlContext、DataFrame，DataFrame功能强大，能够与rdd互转换、支持sql操作如sql().where.order.join.groupBy.limit等。 
SparkSql的查询响应性能是hive的几何级倍数，并且SparkSql支持多种数据源操作包括hive、hdfs、rdd、json、mysql，本文先讲解hive、hdfs、rdd、json4种数据源操作。

• 1.1 版本预览

  Cnetos 6.5 已安装
  Hadoop 2.8 已安装集群
  Hive 2.3 待安装
  Mysql 5.6 已安装
  Spark 2.1.1 已安装

• 1.2 机器环境

  192.168.0.251 slave
  192.168.0.252 master
  Hadoop：hadoop已做双机无密码登录

• 1.3 工作路径

  Hadoop：/home/data/app/hadoop/hadoop-2.8.0/etc/hadoop
  Spark：/home/data/app/hadoop/spark-2.1.1-bin-hadoop2.7
  Hive数据路径： /user/hive/warehouse/

• 2.1 spark连接hive

节点Spark conf下增加hive-site.xml

<configuration>
  <property>
    <name>hive.metastore.uris</name>
    <value>thrift://shulaibao2:9083</value>
    <description>Thrift URI for the remote metastore. Used by metastore client to connect to remote metastore.</description>
  </property>
</configuration>

• 2.2 启动hive支持metastore

  nohup hive --service metastore > metastore.log 2>&1 &

• 2.3 spark集群重启

  ./stop-all.sh
  ./start-all.sh

• 3.1 sparkSql操作

  ./spark-sql --master spark://shulaibao2:7077 --executor-memory 1g

  按年统计交易订单数量、交易金额
  select c.theyear,count(distinct a.ordernumber),sum(b.amount) from tbStock a join tbStockDetail b on a.ordernumber=b.ordernumber
  join tbDate c on a.dateid=c.dateid
  group by c.theyear order by c.theyear;

  计算每年销售额最大的订单
  select c.theyear,max(d.sumofamount) from tbDate c join (select a.dateid,a.ordernumber,sum(b.amount) as sumofamount from tbStock a join tbStockDetail b on a.ordernumber=b.ordernumber group by a.dateid,a.ordernumber ) d on c.dateid=d.dateid group by c.theyear sort by c.theyear;

• 3.2 spark shell编码

  val hiveQuery = sql("select * from hive_data.tbstock limit 10")

  hiveQuery.collect()
  res14: Array[org.apache.spark.sql.Row] = Array([BYSL00000893,ZHAO,2007-8-23], [BYSL00000897,ZHAO,2007-8-24], [BYSL00000898,ZHAO,2007-8-25], [BYSL00000899,ZHAO,2007-8-26], [BYSL00000900,ZHAO,2007-8-26], [BYSL00000901,ZHAO,2007-8-27], [BYSL00000902,ZHAO,2007-8-27], [BYSL00000904,ZHAO,2007-8-28], [BYSL00000905,ZHAO,2007-8-28], [BYSL00000906,ZHAO,2007-8-28])

• 4.1 hdfs数据源

  import spark.implicits._
  case class Person(name: String, age: Int)
  val peopleDF =
  spark.sparkContext.textFile("hdfs://shulaibao2:9010/home/hadoop/upload/test/people.txt").map(_.split(",")).map(attributes => Person(attributes(0), attributes(1).trim.toInt)).toDF()

  peopleDF.createOrReplaceTempView("people") ： registerTempTable - deprecation
  val teenagersDF = spark.sql("SELECT name, age FROM people WHERE age BETWEEN 24 AND 40")

  teenagersDF.map(teenager => "Name: " + teenager(0)).show()
  teenagersDF.map(teenager => "Name: " + teenager.getAsString).show()

• 4.2 RDD数据源

  import spark.implicits._
  case class Person(name:String, age:Int, state:String)
  sc.parallelize(Person("Michael",29,"CA")::Person("Andy",30,"NY")::Person("Justin",19,"CA")::Person("Justin",25,"CA")::Nil).toDF().registerTempTable("people")

  val query= sql("select * from people") : @return dataFrame

  查询的schem
  query.printSchema

  query.collect() ： @return Array[org.apache.spark.sql.Row]

  查看整个运行计划：
  query.queryExecution

  hadoop fs -put /data/software/sougou/jsonPerson.json /home/hadoop/upload/test/

  spark.sqlContext.jsonFile("/home/hadoop/upload/test/jsonPerson.json").registerTempTable("jsonPerson")
  val jsonQuery = sql("select * from jsonPerson")
  查看结构：
  jsonQuery.printSchema