Spark-RDD算子
Spark课堂笔记
Spark生态圈：
Spark Core ： RDD（弹性分布式数据集）
Spark SQL
Spark Streaming
Spark MLLib：协同过滤，ALS，逻辑回归等等 --> 机器学习
Spark Graphx ： 图计算
重点在前三章
-----------------Spark Core------------------------
一、什么是Spark？特点？
https://spark.apache.org/
Apache Spark™ is a unified analytics engine for large-scale data processing.
特点：快、易用、通用性、兼容性（完全兼容Hadoop）
快：快100倍（Hadoop 3 之前）
易用：支持多种语言开发
通用性：生态系统全。
易用性：兼容Hadoop
spark 取代 Hadoop
二、安装和部署Spark、Spark 的 HA
1、spark体系结构
Spark的运行方式
Yarn
Standalone：本机调试（demo）
Worker：从节点。每个服务器上，资源和任务的管理者。只负责管理一个节点。
执行过程：
一个Worker 有多个 Executor。 Executor是任务的执行者，按阶段（stage）划分任务。————> RDD
客户端：Driver Program 提交任务到集群中。
1、spark-submit
2、spark-shell
2、spark的搭建
（1）准备工作：JDK 配置主机名 免密码登录
（2）伪分布式模式
在一台虚拟机上模拟分布式环境（Master和Worker在一个节点上）
export JAVA_HOME=/usr/java/jdk1.8.0_201
export SPARK_MASTER_HOST=node3
export SPARK_MASTER_PORT=7077
（3）全分布式环境
修改slave文件 拷贝到其他两台服务器 启动
3、Spark的 HA
回顾HA；
（*）HDFS Yarn Hbase Spark 主从结构
（*）单点故障
（1）基于文件目录的单点恢复
（*）本质：还是只有一个主节点Master，创建了一个恢复目录，保存集群状态和任务的信息。
当Master挂掉，重新启动时，会从恢复目录下读取状态信息，恢复出来原来的状态
用途：用于开发和测试，生产用zookeeper
export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=FILESYSTEM
-Dspark.deploy.recoveryDirectory=/usr/local/spark-2.1.0-bin-hadoop2.7/recovery"
（2）基于Zookeeper ：和Hadoop类似
（*）复习一下zookeeper：
相当于一个数据库，把一些信息存放在zookeeper中，比如集群的信息。
数据同步功能，选举功能，分布式锁功能
数据同步：给一个节点中写入数据，可以同步到其他节点
选举：Zookeeper中存在不同的角色，Leader Follower。如果Leader挂掉，重新选举Leader
分布式锁：秒杀。以目录节点的方式来保存数据。
修改 spark-env.sh
export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER
-Dspark.deploy.zookeeper.url=node3:2181,node4:2181,node5:2181
-Dspark.deploy.zookeeper.dir=/spark"
同步到其他两台服务器。
在node3 start-all node3 master node4 Worker node5 Worker
在node4 start-master node3 master node4 master（standby） node4 Worker node5 Worker
在node3上kill master
node4 master（Active） node4 Worker node5 Worker
在网页http://192.168.109.134:8080/ 可以看到相应信息
三、执行Spark的任务：两个工具
1、spark-submit：用于提交Spark的任务
任务：jar。
举例：蒙特卡洛求PI（圆周率）。
./spark-submit --master spark://node3:7077 --class
--class指明主程序的名字
/usr/local/spark-2.1.0-bin-hadoop2.7/bin/spark-submit --master spark://node3:7077
--class org.apache.spark.examples.SparkPi
/usr/local/spark-2.1.0-bin-hadoop2.7/examples/jars/spark-examples_2.11-2.1.0.jar 100
2、spark-shell 相当于REPL
作为一个独立的Application运行
两种模式：
（1）本地模式
spark-shell 后面不接任何参数，代表本地模式
Spark context available as 'sc' (master = local[*], app id = local-1554038459298).
sc 是 SparkContext 对象名。 local[*] 代表本地模式，不提交到集群中运行。
（2）集群模式
./spark-submit --master spark://node3:7077 提交到集群中运行
Spark context available as 'sc' (master = spark://node3:7077, app id = app-20190331212447-0000).
master = spark://node3:7077
Spark session available as 'spark'
Spark Session 是 2.0 以后提供的，利用 SparkSession 可以访问spark所有组件。
示例：WordCount程序
（*）处理本地文件，把结果打印到屏幕上
scala> sc.textFile("/usr/local/tmp_files/test_WordCount.txt")
.flatMap(_.split(" "))
.map((_,1))
.reduceByKey(_+_)
.collect
res0: Array[(String, Int)] = Array((is,1), (love,2), (capital,1), (Beijing,2), (China,2), (I,2), (of,1), (the,1))
（*）处理HDFS文件，结果保存在hdfs上
sc.textFile("hdfs://node1:8020/tmp_files/test_WordCount.txt")
.flatMap(_.split(" "))
.map((_,1))
.reduceByKey(_+_)
.saveAsTextFile("hdfs://node1:8020/output/0331/test_WordCount")
-rw-r--r-- 3 root supergroup 0 2019-03-31 21:43 /output/0331/test_WordCount/_SUCCESS
-rw-r--r-- 3 root supergroup 40 2019-03-31 21:43 /output/0331/test_WordCount/part-00000
-rw-r--r-- 3 root supergroup 31 2019-03-31 21:43 /output/0331/test_WordCount/part-00001
_SUCCESS 代表程序执行成功
part-00000 part-00001 结果文件，分区。里面内容不重复。
（*）单步运行WordCount ----> RDD
scala> val rdd1 = sc.textFile("/usr/local/tmp_files/test_WordCount.txt")
rdd1: org.apache.spark.rdd.RDD[String] = /usr/local/tmp_files/test_WordCount.txt MapPartitionsRDD[12] at textFile at <console>:24
scala> 1+1
res2: Int = 2
scala> rdd1.collect
res3: Array[String] = Array(I love Beijing, I love China, Beijing is the capital of China)
scala> val rdd2 = rdd1.flatMap(_.split(" "))
rdd2: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[13] at flatMap at <console>:26
scala> rdd2.collect
res4: Array[String] = Array(I, love, Beijing, I, love, China, Beijing, is, the, capital, of, China)
scala> val rdd3 = rdd2.map((_,1))
rdd3: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[14] at map at <console>:28
scala> rdd3.collect
res5: Array[(String, Int)] = Array((I,1), (love,1), (Beijing,1), (I,1), (love,1), (China,1), (Beijing,1), (is,1), (the,1), (capital,1), (of,1), (China,1))
scala> val rdd4 = rdd3.reduceByKey(_+_)
rdd4: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[15] at reduceByKey at <console>:30
scala> rdd4.collect
res6: Array[(String, Int)] = Array((is,1), (love,2), (capital,1), (Beijing,2), (China,2), (I,2), (of,1), (the,1))
RDD 弹性分布式数据集
（1）依赖关系 ： 宽依赖和窄依赖
（2）算子：
函数：
Transformation ： 延时计算 map flatMap textFile
Action ： 立即触发计算 collect
说明：scala复习
（*）flatten：把嵌套的结果展开
scala> List(List(2,4,6,8,10),List(1,3,5,7,9)).flatten
res21: List[Int] = List(2, 4, 6, 8, 10, 1, 3, 5, 7, 9)
（*）flatmap : 相当于一个 map + flatten
scala> var myList = List(List(2,4,6,8,10),List(1,3,5,7,9))
myList: List[List[Int]] = List(List(2, 4, 6, 8, 10), List(1, 3, 5, 7, 9))
scala> myList.flatMap(x=>x.map(_*2))
res22: List[Int] = List(4, 8, 12, 16, 20, 2, 6, 10, 14, 18)
myList.flatMap(x=>x.map(_*2))
执行过程：
1、将 List(2, 4, 6, 8, 10), List(1, 3, 5, 7, 9) 调用 map(_*2) 方法。x 代表一个List
2、flatten
3、在IDE中开发scala版本和Java版本的WorkCount。
(1)scala版本的WordCount
新建一个工程，把jar引入到工程中。
export jar 点击下一步下一步，不需要设置main class
把jar上传到服务器上。
spark-submit --master spark://node3:7077
--class day1025.MyWordCount
/usr/local/tmp_files/Demo1.jar
hdfs://node2:8020/tmp_files/test_WordCount.txt
hdfs://node2:8020/output/1025/demo1
（2）java版本的WordCount
./spark-submit --master spark://node3:7077 --class day0330.JavaWordCount /usr/local/tmp_files/Demo2.jar
四、分析Spark的任务流程
1、分析WordCount程序处理过程
见图片
2、Spark调度任务的过程
提交到及群众运行任务时，spark执行任务调度。
见图片
五、RDD和RDD特性、RDD的算子
1、RDD：弹性分布式数据集
（*）Spark中最基本的数据抽象。
（*）RDD的特性
* Internally, each RDD is characterized by five main properties:
*
* - A list of partitions
1、是一组分区。
RDD由分区组成，每个分区运行在不同的Worker上，通过这种方式来实现分布式计算。
* - A function for computing each split
在RDD中，提供算子处理每个分区中的数据
* - A list of dependencies on other RDDs
RDD存在依赖关系：宽依赖和窄依赖。
* - Optionally, a Partitioner for key-value RDDs (e.g. to say that the RDD is hash-partitioned)
可以自定义分区规则来创建RDD
* - Optionally, a list of preferred locations to compute each split on (e.g. block locations for
* an HDFS file)
优先选择离文件位置近的节点来执行
如何创建RDD？
（1）通过SparkContext.parallelize方法来创建
scala> val rdd1 = sc.parallelize(Array(1,2,3,4,5,6,7,8),3)
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[32] at parallelize at <console>:29
scala> rdd1.partitions.length
res35: Int = 3
scala> val rdd1 = sc.parallelize(Array(1,2,3,4,5,6,7,8),2)
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[33] at parallelize at <console>:29
scala> rdd1.partitions.length
res36: Int = 2
（2）通过外部数据源来创建
sc.textFile()
scala> val rdd2 = sc.textFile("/usr/local/tmp_files/test_WordCount.txt")
rdd2: org.apache.spark.rdd.RDD[String] = /usr/local/tmp_files/test_WordCount.txt MapPartitionsRDD[35] at textFile at <console>:29
2、算子
（1）Transformation
map(func)：相当于for循环，返回一个新的RDD
filter(func)：过滤
flatMap(func)：flat+map 压平
mapPartitions(func)：对RDD中的每个分区进行操作
mapPartitionsWithIndex(func)：对RDD中的每个分区进行操作，可以取到分区号。
sample(withReplacement, fraction, seed)：采样
集合运算
union(otherDataset)
intersection(otherDataset)
distinct([numTasks]))：去重
聚合操作：group by
groupByKey([numTasks])
reduceByKey(func, [numTasks])
aggregateByKey(zeroValue)(seqOp,combOp,[numTasks])
排序
sortByKey([ascending], [numTasks])
sortBy(func,[ascending], [numTasks])
join(otherDataset, [numTasks])
cogroup(otherDataset, [numTasks])
cartesian(otherDataset)
pipe(command, [envVars])
coalesce(numPartitions)
重分区：
repartition(numPartitions)
repartitionAndSortWithinPartitions(partitioner)
举例：
1、创建一个RDD，每个元素乘以2，再排序
scala> val rdd1 = sc.parallelize(Array(3,4,5,100,79,81,6,8))
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[36] at parallelize at <console>:29
scala> val rdd2 = rdd1.map(_*2)
rdd2: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[37] at map at <console>:31
scala> rdd2.collect
res37: Array[Int] = Array(6, 8, 10, 200, 158, 162, 12, 16)
scala> rdd2.sortBy(x=>x,true).collect
res39: Array[Int] = Array(6, 8, 10, 12, 16, 158, 162, 200)
scala> rdd2.sortBy(x=>x,false).collect
res40: Array[Int] = Array(200, 162, 158, 16, 12, 10, 8, 6)
def sortBy[K](f: (T) ⇒ K, ascending: Boolean = true)
过滤出大于20的元素：
scala> val rdd3 = rdd2.filter(_>20)
rdd3: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[53] at filter at <console>:33
scala> rdd3.collect
res41: Array[Int] = Array(200, 158, 162)
2、字符串（字符）类型的RDD
scala> val rdd4 = sc.parallelize(Array("a b c","d e f","g h i"))
rdd4: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[54] at parallelize at <console>:29
scala> rdd4.flatMap(_.split(" ")).collect
res42: Array[String] = Array(a, b, c, d, e, f, g, h, i)
3、RDD的集合运算：
scala> val rdd6 = sc.parallelize(List(1,2,3,6,7,8,9,100))
rdd6: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[56] at parallelize at <console>:29
scala> val rdd7 = sc.parallelize(List(1,2,3,4))
rdd7: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[57] at parallelize at <console>:29
scala> val rdd8 = rdd6.union(rdd7)
rdd8: org.apache.spark.rdd.RDD[Int] = UnionRDD[58] at union at <console>:33
scala> rdd8.collect
res43: Array[Int] = Array(1, 2, 3, 6, 7, 8, 9, 100, 1, 2, 3, 4)
scala> rdd8.distinct.collect
res44: Array[Int] = Array(100, 4, 8, 1, 9, 6, 2, 3, 7)
4、分组操作：reduceByKey
<key value>
scala> val rdd1 = sc.parallelize(List(("Tom",1000),("Andy",2000),("Lily",1500)))
rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[62] at parallelize at <console>:29
scala> val rdd2 = sc.parallelize(List(("Andy",1000),("Tom",2000),("Mike",500)))
rdd2: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[63] at parallelize at <console>:29
scala> val rdd3 = rdd1 union rdd2
rdd3: org.apache.spark.rdd.RDD[(String, Int)] = UnionRDD[64] at union at <console>:33
scala> rdd3.collect
res45: Array[(String, Int)] = Array((Tom,1000), (Andy,2000), (Lily,1500), (Andy,1000), (Tom,2000), (Mike,500))
scala> val rdd4= rdd3.groupByKey
rdd4: org.apache.spark.rdd.RDD[(String, Iterable[Int])] = ShuffledRDD[65] at groupByKey at <console>:35
scala> rdd4.collect
res46: Array[(String, Iterable[Int])] = Array(
(Tom,CompactBuffer(1000, 2000)),
(Andy,CompactBuffer(2000, 1000)),
(Mike,CompactBuffer(500)), (
Lily,CompactBuffer(1500)))
scala> rdd3.reduceByKey(_+_).collect
res47: Array[(String, Int)] = Array((Tom,3000), (Andy,3000), (Mike,500), (Lily,1500))
reduceByKey will provide much better performance.
官方不推荐使用 groupByKey 推荐使用 reduceByKey
5、cogroup
scala> val rdd1 = sc.parallelize(List(("tom", 1), ("tom", 2), ("jerry", 3), ("kitty", 2)))
rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[67] at parallelize at <console>:29
scala> val rdd2 = sc.parallelize(List(("jerry", 2), ("tom", 1), ("shuke", 2)))
rdd2: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[68] at parallelize at <console>:29
scala> val rdd3 = rdd1.cogroup(rdd2)
rdd3: org.apache.spark.rdd.RDD[(String, (Iterable[Int], Iterable[Int]))] = MapPartitionsRDD[70] at cogroup at <console>:33
scala> rdd3.collect
res48: Array[(String, (Iterable[Int], Iterable[Int]))] = Array(
(tom,(CompactBuffer(1, 2),CompactBuffer(1))),
(jerry,(CompactBuffer(3),CompactBuffer(2))),
(shuke,(CompactBuffer(),CompactBuffer(2))),
(kitty,(CompactBuffer(2),CompactBuffer())))
6、reduce操作（Action）
聚合操作
scala> val rdd1 = sc.parallelize(List(1,2,3,4,5))
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[71] at parallelize at <console>:29
scala> rdd1.reduce(_+_)
res49: Int = 15
7、需求：按照value排序。
做法：
1、交换，把key 和 value交换，然后调用sortByKey方法
2、再次交换
scala> val rdd1 = sc.parallelize(List(("tom",1),("jerry",3),("ketty",2),("shuke",2)))
rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[72] at parallelize at <console>:29
scala> val rdd2 = sc.parallelize(List(("jerry",1),("tom",3),("shuke",5),("ketty",1)))
rdd2: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[73] at parallelize at <console>:29
scala> val rdd3 = rdd1.union(rdd2)
rdd3: org.apache.spark.rdd.RDD[(String, Int)] = UnionRDD[74] at union at <console>:33
scala> val rdd4 = rdd3.reduceByKey(_+_)
rdd4: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[75] at reduceByKey at <console>:35
scala> rdd4.collect
res50: Array[(String, Int)] = Array((tom,4), (jerry,4), (shuke,7), (ketty,3))
scala> val rdd5 = rdd4.map(t=>(t._2,t._1)).sortByKey(false).map(t=>(t._2,t._1))
rdd5: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[80] at map at <console>:37
scala> rdd5.collect
res51: Array[(String, Int)] = Array((shuke,7), (tom,4), (jerry,4), (ketty,3))
（2）Action
reduce(func)
collect()
count()
first()
take(n)
takeSample(withReplacement,num, [seed])
takeOrdered(n, [ordering])
saveAsTextFile(path)
saveAsSequenceFile(path)
saveAsObjectFile(path)
countByKey()
foreach(func)：与map类似，没有返回值。
3、特性：
（1）RDD的缓存机制
（*）作用：提高性能
（*）使用：标识RDD可以被缓存 persist cache
（*）可以缓存的位置：
val NONE = new StorageLevel(false, false, false, false)
val DISK_ONLY = new StorageLevel(true, false, false, false)
val DISK_ONLY_2 = new StorageLevel(true, false, false, false, 2)
val MEMORY_ONLY = new StorageLevel(false, true, false, true)
val MEMORY_ONLY_2 = new StorageLevel(false, true, false, true, 2)
val MEMORY_ONLY_SER = new StorageLevel(false, true, false, false)
val MEMORY_ONLY_SER_2 = new StorageLevel(false, true, false, false, 2)
val MEMORY_AND_DISK = new StorageLevel(true, true, false, true)
val MEMORY_AND_DISK_2 = new StorageLevel(true, true, false, true, 2)
val MEMORY_AND_DISK_SER = new StorageLevel(true, true, false, false)
val MEMORY_AND_DISK_SER_2 = new StorageLevel(true, true, false, false, 2)
val OFF_HEAP = new StorageLevel(true, true, true, false, 1)
/**
* Persist this RDD with the default storage level (`MEMORY_ONLY`).
*/
def persist(): this.type = persist(StorageLevel.MEMORY_ONLY)
/**
* Persist this RDD with the default storage level (`MEMORY_ONLY`).
*/
def cache(): this.type = persist()
举例：测试数据，92万条
scala> val rdd1 = sc.textFile("hdfs://192.168.109.131:8020/tmp_files/test_Cache.txt")
rdd1: org.apache.spark.rdd.RDD[String] = hdfs://192.168.109.131:8020/tmp_files/test_Cache.txt MapPartitionsRDD[82] at textFile at <console>:29
scala> rdd1.count --> 直接出发计算
res52: Long = 923452
scala> rdd1.cache --> 标识RDD可以被缓存，不会触发计算
res53: rdd1.type = hdfs://192.168.109.131:8020/tmp_files/test_Cache.txt MapPartitionsRDD[82] at textFile at <console>:29
scala> rdd1.count --> 和第一步一样，触发计算，但是，把结果进行缓存
res54: Long = 923452
scala> rdd1.count --> 从缓存中直接读出结果
res55: Long = 923452
（2）RDD的容错机制
Spark课堂笔记
Spark生态圈：Spark Core ： RDD（弹性分布式数据集）Spark SQLSpark StreamingSpark MLLib：协同过滤，ALS，逻辑回归等等 --> 机器学习Spark Graphx ： 图计算
重点在前三章
-----------------Spark Core------------------------一、什么是Spark？特点？https://spark.apache.org/Apache Spark™ is a unified analytics engine for large-scale data processing.特点：快、易用、通用性、兼容性（完全兼容Hadoop）快：快100倍（Hadoop 3 之前）易用：支持多种语言开发通用性：生态系统全。易用性：兼容Hadoopspark 取代 Hadoop
二、安装和部署Spark、Spark 的 HA
1、spark体系结构Spark的运行方式YarnStandalone：本机调试（demo）Worker：从节点。每个服务器上，资源和任务的管理者。只负责管理一个节点。执行过程：一个Worker 有多个 Executor。 Executor是任务的执行者，按阶段（stage）划分任务。————> RDD客户端：Driver Program 提交任务到集群中。1、spark-submit2、spark-shell
2、spark的搭建（1）准备工作：JDK 配置主机名 免密码登录（2）伪分布式模式在一台虚拟机上模拟分布式环境（Master和Worker在一个节点上）export JAVA_HOME=/usr/java/jdk1.8.0_201export SPARK_MASTER_HOST=node3export SPARK_MASTER_PORT=7077（3）全分布式环境修改slave文件 拷贝到其他两台服务器 启动3、Spark的 HA回顾HA；（*）HDFS Yarn Hbase Spark 主从结构（*）单点故障（1）基于文件目录的单点恢复（*）本质：还是只有一个主节点Master，创建了一个恢复目录，保存集群状态和任务的信息。当Master挂掉，重新启动时，会从恢复目录下读取状态信息，恢复出来原来的状态用途：用于开发和测试，生产用zookeeperexport SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=FILESYSTEM -Dspark.deploy.recoveryDirectory=/usr/local/spark-2.1.0-bin-hadoop2.7/recovery"（2）基于Zookeeper ：和Hadoop类似（*）复习一下zookeeper：相当于一个数据库，把一些信息存放在zookeeper中，比如集群的信息。数据同步功能，选举功能，分布式锁功能数据同步：给一个节点中写入数据，可以同步到其他节点选举：Zookeeper中存在不同的角色，Leader Follower。如果Leader挂掉，重新选举Leader分布式锁：秒杀。以目录节点的方式来保存数据。修改 spark-env.shexport SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=node3:2181,node4:2181,node5:2181 -Dspark.deploy.zookeeper.dir=/spark"同步到其他两台服务器。在node3 start-all node3 master node4 Worker node5 Worker在node4 start-master node3 master node4 master（standby） node4 Worker node5 Worker在node3上kill masternode4 master（Active） node4 Worker node5 Worker在网页http://192.168.109.134:8080/ 可以看到相应信息三、执行Spark的任务：两个工具1、spark-submit：用于提交Spark的任务任务：jar。举例：蒙特卡洛求PI（圆周率）。./spark-submit --master spark://node3:7077 --class--class指明主程序的名字/usr/local/spark-2.1.0-bin-hadoop2.7/bin/spark-submit --master spark://node3:7077 --class org.apache.spark.examples.SparkPi /usr/local/spark-2.1.0-bin-hadoop2.7/examples/jars/spark-examples_2.11-2.1.0.jar 1002、spark-shell 相当于REPL 作为一个独立的Application运行两种模式：（1）本地模式spark-shell 后面不接任何参数，代表本地模式Spark context available as 'sc' (master = local[*], app id = local-1554038459298).sc 是 SparkContext 对象名。 local[*] 代表本地模式，不提交到集群中运行。（2）集群模式./spark-submit --master spark://node3:7077 提交到集群中运行Spark context available as 'sc' (master = spark://node3:7077, app id = app-20190331212447-0000).master = spark://node3:7077Spark session available as 'spark'Spark Session 是 2.0 以后提供的，利用 SparkSession 可以访问spark所有组件。示例：WordCount程序（*）处理本地文件，把结果打印到屏幕上scala> sc.textFile("/usr/local/tmp_files/test_WordCount.txt").flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).collectres0: Array[(String, Int)] = Array((is,1), (love,2), (capital,1), (Beijing,2), (China,2), (I,2), (of,1), (the,1))（*）处理HDFS文件，结果保存在hdfs上sc.textFile("hdfs://node1:8020/tmp_files/test_WordCount.txt").flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).saveAsTextFile("hdfs://node1:8020/output/0331/test_WordCount")-rw-r--r-- 3 root supergroup 0 2019-03-31 21:43 /output/0331/test_WordCount/_SUCCESS-rw-r--r-- 3 root supergroup 40 2019-03-31 21:43 /output/0331/test_WordCount/part-00000-rw-r--r-- 3 root supergroup 31 2019-03-31 21:43 /output/0331/test_WordCount/part-00001_SUCCESS 代表程序执行成功part-00000 part-00001 结果文件，分区。里面内容不重复。（*）单步运行WordCount ----> RDDscala> val rdd1 = sc.textFile("/usr/local/tmp_files/test_WordCount.txt")rdd1: org.apache.spark.rdd.RDD[String] = /usr/local/tmp_files/test_WordCount.txt MapPartitionsRDD[12] at textFile at <console>:24
scala> 1+1res2: Int = 2
scala> rdd1.collectres3: Array[String] = Array(I love Beijing, I love China, Beijing is the capital of China)
scala> val rdd2 = rdd1.flatMap(_.split(" "))rdd2: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[13] at flatMap at <console>:26
scala> rdd2.collectres4: Array[String] = Array(I, love, Beijing, I, love, China, Beijing, is, the, capital, of, China)
scala> val rdd3 = rdd2.map((_,1))rdd3: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[14] at map at <console>:28
scala> rdd3.collectres5: Array[(String, Int)] = Array((I,1), (love,1), (Beijing,1), (I,1), (love,1), (China,1), (Beijing,1), (is,1), (the,1), (capital,1), (of,1), (China,1))
scala> val rdd4 = rdd3.reduceByKey(_+_)rdd4: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[15] at reduceByKey at <console>:30
scala> rdd4.collectres6: Array[(String, Int)] = Array((is,1), (love,2), (capital,1), (Beijing,2), (China,2), (I,2), (of,1), (the,1))
RDD 弹性分布式数据集（1）依赖关系 ： 宽依赖和窄依赖（2）算子：函数：Transformation ： 延时计算 map flatMap textFileAction ： 立即触发计算 collect说明：scala复习（*）flatten：把嵌套的结果展开scala> List(List(2,4,6,8,10),List(1,3,5,7,9)).flattenres21: List[Int] = List(2, 4, 6, 8, 10, 1, 3, 5, 7, 9)（*）flatmap : 相当于一个 map + flattenscala> var myList = List(List(2,4,6,8,10),List(1,3,5,7,9))myList: List[List[Int]] = List(List(2, 4, 6, 8, 10), List(1, 3, 5, 7, 9))
scala> myList.flatMap(x=>x.map(_*2))res22: List[Int] = List(4, 8, 12, 16, 20, 2, 6, 10, 14, 18)myList.flatMap(x=>x.map(_*2))执行过程：1、将 List(2, 4, 6, 8, 10), List(1, 3, 5, 7, 9) 调用 map(_*2) 方法。x 代表一个List2、flatten3、在IDE中开发scala版本和Java版本的WorkCount。
(1)scala版本的WordCount新建一个工程，把jar引入到工程中。export jar 点击下一步下一步，不需要设置main class 把jar上传到服务器上。spark-submit --master spark://node3:7077 --class day1025.MyWordCount /usr/local/tmp_files/Demo1.jar hdfs://node2:8020/tmp_files/test_WordCount.txt hdfs://node2:8020/output/1025/demo1（2）java版本的WordCount./spark-submit --master spark://node3:7077 --class day0330.JavaWordCount /usr/local/tmp_files/Demo2.jar四、分析Spark的任务流程1、分析WordCount程序处理过程见图片2、Spark调度任务的过程提交到及群众运行任务时，spark执行任务调度。见图片五、RDD和RDD特性、RDD的算子
1、RDD：弹性分布式数据集（*）Spark中最基本的数据抽象。（*）RDD的特性* Internally, each RDD is characterized by five main properties: * * - A list of partitions 1、是一组分区。RDD由分区组成，每个分区运行在不同的Worker上，通过这种方式来实现分布式计算。
* - A function for computing each split在RDD中，提供算子处理每个分区中的数据 * - A list of dependencies on other RDDs RDD存在依赖关系：宽依赖和窄依赖。 * - Optionally, a Partitioner for key-value RDDs (e.g. to say that the RDD is hash-partitioned) 可以自定义分区规则来创建RDD * - Optionally, a list of preferred locations to compute each split on (e.g. block locations for * an HDFS file) 优先选择离文件位置近的节点来执行如何创建RDD？（1）通过SparkContext.parallelize方法来创建scala> val rdd1 = sc.parallelize(Array(1,2,3,4,5,6,7,8),3)rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[32] at parallelize at <console>:29scala> rdd1.partitions.lengthres35: Int = 3
scala> val rdd1 = sc.parallelize(Array(1,2,3,4,5,6,7,8),2)rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[33] at parallelize at <console>:29
scala> rdd1.partitions.lengthres36: Int = 2（2）通过外部数据源来创建sc.textFile()scala> val rdd2 = sc.textFile("/usr/local/tmp_files/test_WordCount.txt")rdd2: org.apache.spark.rdd.RDD[String] = /usr/local/tmp_files/test_WordCount.txt MapPartitionsRDD[35] at textFile at <console>:292、算子（1）Transformationmap(func)：相当于for循环，返回一个新的RDDfilter(func)：过滤flatMap(func)：flat+map 压平mapPartitions(func)：对RDD中的每个分区进行操作mapPartitionsWithIndex(func)：对RDD中的每个分区进行操作，可以取到分区号。sample(withReplacement, fraction, seed)：采样集合运算union(otherDataset)intersection(otherDataset)distinct([numTasks]))：去重聚合操作：group by groupByKey([numTasks])reduceByKey(func, [numTasks])aggregateByKey(zeroValue)(seqOp,combOp,[numTasks])排序sortByKey([ascending], [numTasks])sortBy(func,[ascending], [numTasks])join(otherDataset, [numTasks])cogroup(otherDataset, [numTasks])cartesian(otherDataset)pipe(command, [envVars])coalesce(numPartitions)重分区：repartition(numPartitions)repartitionAndSortWithinPartitions(partitioner)举例：1、创建一个RDD，每个元素乘以2，再排序scala> val rdd1 = sc.parallelize(Array(3,4,5,100,79,81,6,8))rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[36] at parallelize at <console>:29
scala> val rdd2 = rdd1.map(_*2)rdd2: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[37] at map at <console>:31
scala> rdd2.collectres37: Array[Int] = Array(6, 8, 10, 200, 158, 162, 12, 16) scala> rdd2.sortBy(x=>x,true).collectres39: Array[Int] = Array(6, 8, 10, 12, 16, 158, 162, 200)
scala> rdd2.sortBy(x=>x,false).collectres40: Array[Int] = Array(200, 162, 158, 16, 12, 10, 8, 6) def sortBy[K](f: (T) ⇒ K, ascending: Boolean = true)过滤出大于20的元素：scala> val rdd3 = rdd2.filter(_>20)rdd3: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[53] at filter at <console>:33
scala> rdd3.collectres41: Array[Int] = Array(200, 158, 162) 2、字符串（字符）类型的RDDscala> val rdd4 = sc.parallelize(Array("a b c","d e f","g h i"))rdd4: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[54] at parallelize at <console>:29
scala> rdd4.flatMap(_.split(" ")).collectres42: Array[String] = Array(a, b, c, d, e, f, g, h, i) 3、RDD的集合运算：scala> val rdd6 = sc.parallelize(List(1,2,3,6,7,8,9,100))rdd6: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[56] at parallelize at <console>:29
scala> val rdd7 = sc.parallelize(List(1,2,3,4))rdd7: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[57] at parallelize at <console>:29
scala> val rdd8 = rdd6.union(rdd7)rdd8: org.apache.spark.rdd.RDD[Int] = UnionRDD[58] at union at <console>:33
scala> rdd8.collectres43: Array[Int] = Array(1, 2, 3, 6, 7, 8, 9, 100, 1, 2, 3, 4)
scala> rdd8.distinct.collectres44: Array[Int] = Array(100, 4, 8, 1, 9, 6, 2, 3, 7) 4、分组操作：reduceByKey <key value>scala> val rdd1 = sc.parallelize(List(("Tom",1000),("Andy",2000),("Lily",1500)))rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[62] at parallelize at <console>:29
scala> val rdd2 = sc.parallelize(List(("Andy",1000),("Tom",2000),("Mike",500)))rdd2: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[63] at parallelize at <console>:29
scala> val rdd3 = rdd1 union rdd2rdd3: org.apache.spark.rdd.RDD[(String, Int)] = UnionRDD[64] at union at <console>:33
scala> rdd3.collectres45: Array[(String, Int)] = Array((Tom,1000), (Andy,2000), (Lily,1500), (Andy,1000), (Tom,2000), (Mike,500))
scala> val rdd4= rdd3.groupByKeyrdd4: org.apache.spark.rdd.RDD[(String, Iterable[Int])] = ShuffledRDD[65] at groupByKey at <console>:35
scala> rdd4.collectres46: Array[(String, Iterable[Int])] = Array((Tom,CompactBuffer(1000, 2000)), (Andy,CompactBuffer(2000, 1000)), (Mike,CompactBuffer(500)), (Lily,CompactBuffer(1500)))scala> rdd3.reduceByKey(_+_).collectres47: Array[(String, Int)] = Array((Tom,3000), (Andy,3000), (Mike,500), (Lily,1500))reduceByKey will provide much better performance.官方不推荐使用 groupByKey 推荐使用 reduceByKey5、cogroupscala> val rdd1 = sc.parallelize(List(("tom", 1), ("tom", 2), ("jerry", 3), ("kitty", 2)))rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[67] at parallelize at <console>:29
scala> val rdd2 = sc.parallelize(List(("jerry", 2), ("tom", 1), ("shuke", 2)))rdd2: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[68] at parallelize at <console>:29
scala> val rdd3 = rdd1.cogroup(rdd2)rdd3: org.apache.spark.rdd.RDD[(String, (Iterable[Int], Iterable[Int]))] = MapPartitionsRDD[70] at cogroup at <console>:33
scala> rdd3.collectres48: Array[(String, (Iterable[Int], Iterable[Int]))] = Array((tom,(CompactBuffer(1, 2),CompactBuffer(1))), (jerry,(CompactBuffer(3),CompactBuffer(2))), (shuke,(CompactBuffer(),CompactBuffer(2))), (kitty,(CompactBuffer(2),CompactBuffer())))6、reduce操作（Action）聚合操作scala> val rdd1 = sc.parallelize(List(1,2,3,4,5))rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[71] at parallelize at <console>:29
scala> rdd1.reduce(_+_)res49: Int = 157、需求：按照value排序。做法：1、交换，把key 和 value交换，然后调用sortByKey方法2、再次交换scala> val rdd1 = sc.parallelize(List(("tom",1),("jerry",3),("ketty",2),("shuke",2)))rdd1: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[72] at parallelize at <console>:29
scala> val rdd2 = sc.parallelize(List(("jerry",1),("tom",3),("shuke",5),("ketty",1)))rdd2: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[73] at parallelize at <console>:29
scala> val rdd3 = rdd1.union(rdd2)rdd3: org.apache.spark.rdd.RDD[(String, Int)] = UnionRDD[74] at union at <console>:33
scala> val rdd4 = rdd3.reduceByKey(_+_)rdd4: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[75] at reduceByKey at <console>:35
scala> rdd4.collectres50: Array[(String, Int)] = Array((tom,4), (jerry,4), (shuke,7), (ketty,3))
scala> val rdd5 = rdd4.map(t=>(t._2,t._1)).sortByKey(false).map(t=>(t._2,t._1))rdd5: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[80] at map at <console>:37
scala> rdd5.collectres51: Array[(String, Int)] = Array((shuke,7), (tom,4), (jerry,4), (ketty,3)) （2）Actionreduce(func)collect()count()first()take(n)takeSample(withReplacement,num, [seed])takeOrdered(n, [ordering])saveAsTextFile(path)saveAsSequenceFile(path) saveAsObjectFile(path) countByKey()foreach(func)：与map类似，没有返回值。3、特性：（1）RDD的缓存机制（*）作用：提高性能（*）使用：标识RDD可以被缓存 persist cache（*）可以缓存的位置： val NONE = new StorageLevel(false, false, false, false) val DISK_ONLY = new StorageLevel(true, false, false, false) val DISK_ONLY_2 = new StorageLevel(true, false, false, false, 2) val MEMORY_ONLY = new StorageLevel(false, true, false, true) val MEMORY_ONLY_2 = new StorageLevel(false, true, false, true, 2) val MEMORY_ONLY_SER = new StorageLevel(false, true, false, false) val MEMORY_ONLY_SER_2 = new StorageLevel(false, true, false, false, 2) val MEMORY_AND_DISK = new StorageLevel(true, true, false, true) val MEMORY_AND_DISK_2 = new StorageLevel(true, true, false, true, 2) val MEMORY_AND_DISK_SER = new StorageLevel(true, true, false, false) val MEMORY_AND_DISK_SER_2 = new StorageLevel(true, true, false, false, 2) val OFF_HEAP = new StorageLevel(true, true, true, false, 1) /** * Persist this RDD with the default storage level (`MEMORY_ONLY`). */ def persist(): this.type = persist(StorageLevel.MEMORY_ONLY)
/** * Persist this RDD with the default storage level (`MEMORY_ONLY`). */ def cache(): this.type = persist() 举例：测试数据，92万条scala> val rdd1 = sc.textFile("hdfs://192.168.109.131:8020/tmp_files/test_Cache.txt")rdd1: org.apache.spark.rdd.RDD[String] = hdfs://192.168.109.131:8020/tmp_files/test_Cache.txt MapPartitionsRDD[82] at textFile at <console>:29
scala> rdd1.count --> 直接出发计算res52: Long = 923452
scala> rdd1.cache --> 标识RDD可以被缓存，不会触发计算res53: rdd1.type = hdfs://192.168.109.131:8020/tmp_files/test_Cache.txt MapPartitionsRDD[82] at textFile at <console>:29
scala> rdd1.count --> 和第一步一样，触发计算，但是，把结果进行缓存res54: Long = 923452
scala> rdd1.count --> 从缓存中直接读出结果res55: Long = 923452（2）RDD的容错机制
