spark standalone模式

--准备：
jdk （1.7之上）
scala （2.10.4）
hadoop2.x（至少hdfs）
spark standalone


--配置conf文件：
[hadoop@node1 conf]$ mv spark-env.sh.template spark-env.sh
[hadoop@node1 conf]$ mv slaves.template slaves
[hadoop@node1 conf]$ mv spark-defaults.conf.template spark-defaults.conf


--启动hadoop
hadoop-daemon.sh start namenode
hadoop-daemon.sh start datanode

--启动spark
"$sbin"/start-master.sh
"$sbin"/start-slaves.sh

spark-shell 
spark-shell --master spark://node1:7077
上面的等价，spark-shell 因为它读取了spark-defaults.conf中的 spark.master                     spark://node1:7077


--web监控
app:
http://node1: 8080

job:
http://node1:4040

---------------------------------------------------------------    
--附配置：    
---------------------------------------------------------------   
[hadoop@node1 conf]$ vi slaves 
node1
[hadoop@node1 conf]$ vi spark-defaults.conf 
spark.master                     spark://node1:7077          
[hadoop@node1 conf]$ vi spark-env.sh
# Options for the daemons used in the standalone deploy mode
# - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports for the master
# - SPARK_MASTER_OPTS, to set config properties only for the master (e.g. "-Dx=y")
# - SPARK_WORKER_CORES, to set the number of cores to use on this machine
# - SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors (e.g. 1000m, 2g)
# - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT, to use non-default ports for the worker
# - SPARK_WORKER_INSTANCES, to set the number of worker processes per node
# - SPARK_WORKER_DIR, to set the working directory of worker processes
# - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y")
# - SPARK_DAEMON_MEMORY, to allocate to the master, worker and history server themselves (default: 1g).
# - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y")
# - SPARK_SHUFFLE_OPTS, to set config properties only for the external shuffle service (e.g. "-Dx=y")
# - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y")
# - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers

JAVA_HOME=/home/hadoop/jdk1.7.0_67
SCALA_HOME=/home/hadoop/scala-2.10.4
HADOOP_CONF_DIR=/home/hadoop/hadoop-2.7.1/etc/hadoop
SPARK_PID_DIR=/home/hadoop/dirtmp

SPARK_MASTER_IP=node1
SPARK_MASTER_PORT=7077
SPARK_MASTER_WEBUI_PORT=8080
SPARK_WORKER_CORES=1              #cpu核心数量
SPARK_WORKER_MEMORY=1024m  #生产中要改大，默认1G
SPARK_WORKER_PORT=7078
SPARK_WORKER_WEBUI_PORT=8081
SPARK_WORKER_INSTANCES=1







---------------------------------------------------------------    
--spark语法测试：    
---------------------------------------------------------------   

--启动单节点hadoop
[hadoop@node1 ~]$ hadoop-daemon.sh start namenode
[hadoop@node1 ~]$ hadoop-daemon.sh start datanode

[hadoop@node1 ~]$ hadoop-daemon.sh stop namenode
[hadoop@node1 ~]$ hadoop-daemon.sh stop datanode

--启动单节点spark
[hadoop@node1 sbin]$ start-master.sh
[hadoop@node1 sbin]$ start-slaves.sh

[hadoop@node1 logs]$ stop-master.sh 
[hadoop@node1 logs]$ stop-slaves.sh 

[hadoop@node1 sbin]$ jps
3526 DataNode
3912 Jps
3699 Master
2534 NameNode
3873 Worker

--数据源
[hadoop@node1 test]$ vi aaa.txt
hello hadoop
hellp spark
hadoop spark
hive hadoop
sqoop hive


[hadoop@node1 ~]$ hadoop fs -ls /test/input
-rw-r--r--   1 hadoop supergroup         61 2015-12-09 12:42 /test/input/aaa.txt

[hadoop@node1 ~]$ spark-shell

--2G的虚拟机基本用完
[hadoop@node1 ~]$ free -h
             total       used       free     shared    buffers     cached
Mem:          1.8G       1.6G       241M       248K        29M       318M
-/+ buffers/cache:       1.3G       588M 
Swap:         1.9G         0B       1.9G 


[hadoop@node1 spark-1.5.2-bin-hadoop2.6]$ spark-shell 
...
Spark context available as sc.
SQL context available as sqlContext.
...

scala> val rdd=sc.textFile("hdfs://node1:8020/test/input")
scala> rdd.collect
res2: Array[String] = Array(hello hadoop, hellp spark, hadoop spark, hive hadoop, sqoop hive)
scala> rdd.first
res3: String = hello hadoop
scala> rdd.count
res4: Long = 5

scala> val wordcount= rdd.flatMap(_.split(" ")).map(x=>(x,1)).reduceByKey(_+_)

scala> wordcount.take(10)
res5: Array[(String, Int)] = Array((hive,2), (hello,1), (sqoop,1), (spark,2), (hadoop,3), (hellp,1))    --结果是没有排序的

scala> val wordsort= wordcount.map(x=>(x._2,x._1)).sortByKey(false).map(x=>(x._2,x._1))

scala> wordsort.take(10)
res6: Array[(String, Int)] = Array((hadoop,3), (hive,2), (spark,2), (hello,1), (sqoop,1), (hellp,1)) --结果按第二列排序

scala> wordsort.take(1)
res7: Array[(String, Int)] = Array((hadoop,3))





val rdd=sc.textFile("hdfs://node1:8020/test/input")

--原始数据，一行为一个元素
scala> rdd.collect
res2: Array[String] = Array(hello hadoop, hellp spark, hadoop spark, hive hadoop, sqoop hive)  

scala> rdd.map(_.split(" ")).collect
res6: Array[Array[String]] = Array(Array(hello, hadoop), Array(hellp, spark), Array(hadoop, spark), Array(hive, hadoop), Array(sqoop, hive))


--一个单词为一个元素
scala> rdd.flatMap(_.split(" ")).collect
res8: Array[String] = Array(hello, hadoop, hellp, spark, hadoop, spark, hive, hadoop, sqoop, hive) 

--用map方法封装：key为单词，value为1
scala> rdd.flatMap(_.split(" ")).map((_,1)).collect
res10: Array[(String, Int)] = Array((hello,1), (hadoop,1), (hellp,1), (spark,1), (hadoop,1), (spark,1),--map (hive,1), (hadoop,1), (sqoop,1), (hive,1))

--用reduceByKey求和
scala> rdd.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).collect
res11: Array[(String, Int)] = Array((hive,2), (hello,1), (sqoop,1), (spark,2), (hadoop,3), (hellp,1))





--sortByKey(true)排序，h到s
scala> rdd.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).sortByKey(true).collect
res19: Array[(String, Int)] = Array((hadoop,3), (hello,1), (hellp,1), (hive,2), (spark,2), (sqoop,1))




--倒序排列，先k,v倒过来，再sortByKey，再把k，v倒过来，map().sortByKey(false).map()

--x当成是一个数组的元素，如(hadoop,3)， x._2是第二个元素,x._1是第一个元素
scala> rdd.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).sortByKey(true).map(x=>(x._2,x._1)).collect
res23: Array[(Int, String)] = Array((3,hadoop), (1,hello), (1,hellp), (2,hive), (2,spark), (1,sqoop))

scala> rdd.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).sortByKey(true).map(x=>(x._2,x._1)).sortByKey(false).collect
res24: Array[(Int, String)] = Array((3,hadoop), (2,hive), (2,spark), (1,hello), (1,hellp), (1,sqoop))

scala> rdd.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).sortByKey(true).map(x=>(x._2,x._1)).sortByKey(false).map(x=>(x._2,x._1)).collect
res21: Array[(String, Int)] = Array((hadoop,3), (hive,2), (spark,2), (hello,1), (hellp,1), (sqoop,1))


--rownum<=3前3个
scala> rdd.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).sortByKey(true).map(x=>(x._2,x._1)).sortByKey(false).map(x=>(x._2,x._1)).take(3)
res26: Array[(String, Int)] = Array((hadoop,3), (hive,2), (spark,2))

--结果保存到hdfs
scala> rdd.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).sortByKey(true).map(x=>(x._2,x._1)).sortByKey(false).map(x=>(x._2,x._1)).saveAsTextFile("hdfs://node1:8020/test/output")

[hadoop@node1 ~]$ hadoop fs -ls /test/output
15/12/10 15:32:19 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Found 3 items
-rw-r--r--   1 hadoop supergroup          0 2015-12-10 15:31 /test/output/_SUCCESS
-rw-r--r--   1 hadoop supergroup         11 2015-12-10 15:31 /test/output/part-00000
-rw-r--r--   1 hadoop supergroup         49 2015-12-10 15:31 /test/output/part-00001
[hadoop@node1 ~]$ hadoop fs -cat /test/output/part-00000
(hadoop,3)
[hadoop@node1 ~]$ hadoop fs -cat /test/output/part-00001
(hive,2)
(spark,2)
(hello,1)
(hellp,1)
(sqoop,1)

--hdfs中保存到本地?
[hadoop@node1 ~]$ hadoop fs -getmerge hdfs://node1:8020/test/output/part-00000 hdfs://node1:8020/test/output/part-00001 /home/hadoop/test/output
[hadoop@node1 test]$ cat output
(hadoop,3)
(hive,2)
(spark,2)
(hello,1)
(hellp,1)
(sqoop,1)




--cache缓存起来
scala> val rdd=sc.textFile("hdfs://node1:8020/test/input")
scala> rdd.collect  
scala> rdd.cache            res30: rdd.type = MapPartitionsRDD[95] at textFile at <console>:21
scala> rdd.collect    --第二次比第一次快很多


http://node1:4040/storage/