一切开始源于学以致用!终于开始研究Spark Sql了,之前一直参考官网的例子和大神博客,学习过这些例子之后,发现要么基于本地数据源,要么就是基于hdfs数据源。而我试图想从Hbase获取数据后,再实现Spark Sql例子,而这种示例也是我多天来没有google到的,所以想记录下来方便自己查阅,或许也能帮到像我一下开始学习Spark Sql的菜菜鸟!废话不多说,马上开始!
部署环境:Centos6.6
部署软件:Hadoop-2.7.0 + Hbase-0.98.12.1 + Spark-1.3.1
依赖:jdk1.7+scala-2.10.4
开发环境:ideaIU-14.1.4
运行环境:Centos6.6/win7
示例代码:
import java.io.Serializable
import java.util.logging.Logger
import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.spark.{SparkConf, SparkContext}
object MySparkSql extends Serializable {
case class Score(name: String, chinese: Int, english: Int, math: Int)
val logger = Logger.getLogger(MySparkSql.getClass.getName)
def main(args: Array[String]) {
val jars: Array[String] = Array("D:\\workspace\\mysparksql_2.10-1.0.jar")
System.setProperty("hadoop.home.dir" , "E:\\Program Files\\hadoop-2.7.0" )//win7环境下运行须加
val sconf = new SparkConf()
.setMaster("local")
.setAppName("MySparkSql")//win7环境下设置
.set("spark.executor.memory", "1g")
.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
// .setJars(jars)//在集群测试下,设置应用所依赖的jar包
val sc = new SparkContext(sconf)
val conf = HBaseConfiguration.create()
conf.set("hbase.zookeeper.property.clientPort", "2181")
conf.set("hbase.zookeeper.quorum", "h230")
conf.set("hbase.master", "h230:60000")
// conf.addResource("/home/hadoop/SW/hbase/conf/hbase-site.xml")//替代以上三条配置信息
conf.set(TableInputFormat.INPUT_TABLE, "score")
// Scan操作
val hBaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat],
classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],
classOf[org.apache.hadoop.hbase.client.Result])
val score = hBaseRDD.map(m => m._2.listCells()).map(c =>
Score(new String(c.get(0).getRow()),
new String(c.get(0).getValue).toInt,
new String(c.get(1).getValue).toInt,
new String(c.get(2).getValue).toInt)
)
score.foreach(println)
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
val scoreSchema = sqlContext.createDataFrame(score)
scoreSchema.registerTempTable("score")
var result = sqlContext.sql("SELECT count(0) FROM score")
result.collect().foreach(println)
result = sqlContext.sql("SELECT sum(chinese) FROM score")
result.collect().foreach(println)
result = sqlContext.sql("SELECT avg(chinese) FROM score")
result.collect().foreach(println)
result = sqlContext.sql("SELECT sum(math) FROM score")
result.collect().foreach(println)
result = sqlContext.sql("SELECT avg(math) FROM score")
result.collect().foreach(println)
result = sqlContext.sql("SELECT sum(english) FROM score")
result.collect().foreach(println)
result = sqlContext.sql("SELECT avg(english) FROM score")
result.collect().foreach(println)
}
}
我的build.sbt配置:
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | name := "MySparkSql"
version := "1.0"
scalaVersion := "2.10.4"
autoScalaLibrary := false
libraryDependencies ++= Seq("org.apache.hadoop" % "hadoop-main" % "2.7.0",
"org.scala-lang" % "scala-library" % "2.10.4",
"org.scala-lang" % "scala-reflect" % "2.10.4",
"org.scala-lang" % "scala-compiler" % "2.10.4",
"org.scala-lang" % "scalap" % "2.10.4",
"org.apache.thrift" % "libthrift" % "0.9.2",
"org.apache.hbase" % "hbase-server" % "0.98.12.1-hadoop2",
"org.apache.hbase" % "hbase-client" % "0.98.12.1-hadoop2",
"org.apache.hbase" % "hbase-common" % "0.98.12.1-hadoop2",
"org.apache.spark" % "spark-sql_2.10" % "1.3.1",
"org.apache.spark" % "spark-core_2.10" % "1.3.1"
)
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关于代码中的数据库表‘score’,在前面的文章中有:http://mangocool.com/1435117759266.html
最后运行结果:
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1 2 3 4 5 6 7 8 9 10 | Score(lisi,96,76,88)
Score(wangwu,98,99,89)
Score(zhangsan,98,78,80)
[3]
[292]
[97.33333333333333]
[257]
[85.66666666666667]
[253]
[84.33333333333333]
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本文作者:mangocool 来源:mangocool
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