spark Basic code demo

spark-shell --master=spark://namenode01:7077 --executor-memory 2g --driver-class-path /app/spark141/lib/mysql-connector-java-5.1.6-bin.jar

hdfs dfs -put README.md ./
val file=sc.textFile("hdfs:///user/hadoop/README.md").filter(line=>line.contains("spark"))
val wordcount=sc.textFile("hdfs:///user/hadoop/README.md").flatMap(_.split(' ')).map（(_,1)）.reduceByKey(_+_)
wordcount.saveTextFile("/data/result")

//sort by count
val wordcount2=sc.textFile("hdfs:///user/hadoop/README.md").flatMap(_.split(' ')).map((_,1)).reduceByKey(_+_).map(x=>(x._2,x._1)).sortByKey().map(x=>(x._2,x._1))
wordcount2.saveAsTextFile("/data/wordcount2")

 
//启动hive metasotre service SPARK sql show
nohup hive --service metastore > metastore.log 2>&1 &
注意：如果要使用hive，需要将hive-site.xml文件复制到conf/下
pssh " cp /app/hive/lib/mysql-connector-java-5.1.6-bin.jar /app/spark141/lib/"
spark-shell --master=spark://namenode01:7077 --executor-memory 2g --driver-class-path /app/spark141/lib/mysql-connector-java-5.1.6-bin.jar
val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
hiveContext.sql("use test")
hiveContext.sql("show tables").collect().foreach(println)

spark-sql --driver-class-path /app/spark141/lib/mysql-connector-java-5.1.6-bin.jar
just like use hive , write sql
use test
show tables

//parallelize show
val num=sc.parallelize(1 to 10)
val alpha=sc.parallelize('a' to 'z')
val num2=num.map(_*2).collect().foreach(println)
val num3=num.map(_%3==0).collect().foreach(println)
val num3=num.filter(_%3==0).collect().foreach(println)

num.reduce(_+_)
num.reduce(_*_)
num.reduceByKey(_+_)
num.sortBy(x=>x,false)

//K-V演示
val kv1=sc.parallelize(List(("A",1),("B",2),("C",3),("A",4),("B",5)))
kv1.sortByKey().collect //注意sortByKey的小括号不能省 asc
kv1.sortByKey(false).collect //desc 
//how to sort by value?
kv1.map(x=>(x._2,x._1)).sortByKey().map(x=>(x._2,x._1)).collect
kv1.sortBy(x=>x).collect 
kv1.groupByKey().collect 
kv1.reduceByKey(_+_).collect

val kv2=sc.parallelize(List(("A",4),("A",4),("C",3),("A",4),("B",5)))
kv2.distinct.collect
kv1.union(kv2).collect

val kv3=sc.parallelize(List(("A",10),("B",20),("D",30)))
kv1.join(kv3).collect
kv1.cogroup(kv3).collect

val kv4=sc.parallelize(List(List(1,2),List(3,4)))
kv4.flatMap(x=>x.map(_+1)).collect