|NO.Z.00015|——————————|^^ 配置 ^^|——|Hadoop&Spark.V03|——|Spark.v03|sparkcore|RDD编程&sparkcontext创建|

一、设置spark-standalone集群非HA模式

### --- 修改配置文件

[root@hadoop02 ~]# vim $SPARK_HOME/conf/spark-env.sh
export JAVA_HOME=/opt/yanqi/servers/jdk1.8.0_231
export HADOOP_HOME=/opt/yanqi/servers/hadoop-2.9.2
export HADOOP_CONF_DIR=/opt/yanqi/servers/hadoop-2.9.2/etc/hadoop
export SPARK_DIST_CLASSPATH=$(/opt/yanqi/servers/hadoop-2.9.2/bin/hadoop classpath)
export SPARK_MASTER_HOST=hadoop02        # 打开这两行
export SPARK_MASTER_PORT=7077            # 打开这两行
export SPARK_WORKER_CORES=1
export SPARK_WORKER_MEMORY=1g

export SPARK_HISTORY_OPTS="-Dspark.history.ui.port=18080 -Dspark.history.retainedApplications=50 -Dspark.history.fs.logDirectory=hdfs://Hadoop01:9000/spark-eventlog"

~~~     # 注销zookeeper配置

~~~     export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=hadoop01,hadoop02,hadoop03 -Dspark.deploy.zookeeper.dir=/spark"

[root@hadoop02 ~]# rsync-script $SPARK_HOME/conf/spark-env.sh

### --- 启动相关服务

~~~     # 启动hdfs服务
[root@hadoop01 ~]# start-dfs.sh
~~~     # 启动spark-standalone服务
[root@hadoop02 ~]# start-all-spark.sh
~~~     # 通过UI访问spark服务：http://hadoop02:8080/
~~~     通过UI访问standalone服务：http://hadoop02:8080/

### --- 集群验证

[root@hadoop02 ~]# run-example SparkPi 10
~~~ 查看输出参数
Pi is roughly 3.1412511412511415

scala> val lines = sc.textFile("/wcinput/wc.txt")
lines: org.apache.spark.rdd.RDD[String] = /wcinput/wc.txt MapPartitionsRDD[1] at textFile at <console>:24

scala>  lines.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).collect().foreach(println)
(#在文件中输入如下内容,1)                                                       
(yanqi,3)
(mapreduce,3)
(yarn,2)
(hadoop,2)
(hdfs,1)

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二、sparkContext创建

### --- SparkContext

~~~     SparkContext是编写Spark程序用到的第一个类，是Spark的主要入口点，它负责和整个集群的交互;

~~~     # 摘录自源码说明：sparkcontext

/**
 * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark
 * cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster.
 *
 * Only one SparkContext may be active per JVM.  You must `stop()` the active SparkContext before
 * creating a new one.  This limitation may eventually be removed; see SPARK-2243 for more details.
 *
 * @param config a Spark Config object describing the application configuration. Any settings in
 *   this config overrides the default configs as well as system properties.
 */

~~~     # 如把Spark集群当作服务端，那么Driver就是客户端，SparkContext 是客户端的核心；

~~~     SparkContext是Spark的对外接口，负责向调用者提供 Spark 的各种功能；
~~~     SparkContext用于连接Spark集群、创建RDD、累加器、广播变量；
~~~     在 spark-shell 中 SparkContext 已经创建好了，可直接使用；

三、RDD创建

### --- 编写Spark Driver程序第一件事就是：创建SparkContext;

~~~     建议：Standalone模式或本地模式学习RDD的各种算子；
~~~     不需要HA；不需要IDEA

~~~     # 在spark-shell下，spark-context已经创建好了，直接在这里面使用就可以了

[root@hadoop02 ~]# spark-shell 
Warning: Ignoring non-Spark config property: Example
21/10/18 17:15:39 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context Web UI available at http://hadoop02:4040
# --- Spark context available as 'sc' (master = spark://hadoop02:7077, app id = app-20211018171602-0000).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.4.5
      /_/
         
Using Scala version 2.12.10 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_231)
Type in expressions to have them evaluated.
Type :help for more information.

scala> 

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四、集合创建RDD

### --- 从集合创建RDD：主要用于测试
~~~     从集合中创建RDD，主要用于测试。Spark 提供了以下函数：parallelize、makeRDD、range

~~~     # 摘录源码说明：Sparkcontext.scala
~~~     # 714行
  def parallelize[T: ClassTag](
      seq: Seq[T],
      numSlices: Int = defaultParallelism): RDD[T] = withScope {
    assertNotStopped()
    new ParallelCollectionRDD[T](this, seq, numSlices, Map[Int, Seq[String]]())
  }

~~~     # 摘录源码说明：Sparkcontext.scala
~~~     733行

  def range(
      start: Long,
      end: Long,
      step: Long = 1,
      numSlices: Int = defaultParallelism): RDD[Long] = withScope {
    assertNotStopped()
    // when step is 0, range will run infinitely
    require(step != 0, "step cannot be 0")
    val numElements: BigInt = {
      val safeStart = BigInt(start)
      val safeEnd = BigInt(end)
      if ((safeEnd - safeStart) % step == 0 || (safeEnd > safeStart) != (step > 0)) {
        (safeEnd - safeStart) / step
      } else {
        // the remainder has the same sign with range, could add 1 more
        (safeEnd - safeStart) / step + 1
      }
    }

~~~     # 摘录源码说明：Sparkcontext.scala
~~~     800行

  def makeRDD[T: ClassTag](
      seq: Seq[T],
      numSlices: Int = defaultParallelism): RDD[T] = withScope {
    parallelize(seq, numSlices)
  }

### --- 创建sparkcontext

~~~     # 创建rdd
scala> val rdd1 = sc.parallelize(Array(1,2,3,4,5))
rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at parallelize at <console>:24

scala> val rdd2 = sc.parallelize(1 to 100)
rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[1] at parallelize at <console>:24

~~~     # 检查RDD分区数

scala> rdd2.getNumPartitions
res2: Int = 3

scala> rdd2.partitions.length
res3: Int = 3

~~~     # 创建 RDD，并指定分区数
~~~     rdd.collect 方法在生产环境中不要使用，会造成Driver OOM

scala> val rdd2 = sc.parallelize(1 to 100)
rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[2] at parallelize at <console>:24

scala> rdd2.getNumPartitions
res4: Int = 3

scala> val rdd3 = sc.makeRDD(List(1,2,3,4,5))
rdd3: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[3] at makeRDD at <console>:24

scala> val rdd4 = sc.makeRDD(1 to 100)
rdd4: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[4] at makeRDD at <console>:24

scala> rdd4.getNumPartitions
res5: Int = 3

scala> val rdd5 = sc.range(1, 100, 3)
rdd5: org.apache.spark.rdd.RDD[Long] = MapPartitionsRDD[6] at range at <console>:24

scala> rdd5.getNumPartitions
res6: Int = 3

scala> val rdd6 = sc.range(1, 100, 2 ,10)
rdd6: org.apache.spark.rdd.RDD[Long] = MapPartitionsRDD[8] at range at <console>:24

scala> rdd6.getNumPartitions
res7: Int = 10

---

五、从文件系统创建RDD

### --- 从文件系统创建RDD

~~~     # 用 textFile() 方法来从文件系统中加载数据创建RDD。方法将文件的 URI 作为参数，这个URI可以是：
~~~     本地文件系统
~~~     使用本地文件系统要注意：该文件是不是在所有的节点存在（在Standalone模式下）
~~~     分布式文件系统HDFS的地址
~~~     Amazon S3的地址

六、从文件系统创建RDD

### --- 从本地文件系统创建RDD

~~~     # 从本地文件系统加载数据
scala> val lines = sc.textFile("file:///root/data/wc.txt")
lines: org.apache.spark.rdd.RDD[String] = file:///root/data/wc.txt MapPartitionsRDD[10] at textFile at <console>:24

scala> lines.count
~~~     # 报错：ileNotFoundException: File file:/root/data/wc.txt does not exist   //文件不存在
~~~     # 因为这个文件只存在于hadoop02主机的本地，而这个文件是发送到spark集群中执行，所以并不是所有主机都有文件
~~~     # 发送到所有主机
[root@hadoop02 ~]# rsync-script data/

scala> val lines = sc.textFile("file:///root/data/wc.txt")
lines: org.apache.spark.rdd.RDD[String] = file:///root/data/wc.txt MapPartitionsRDD[18] at textFile at <console>:24

scala> lines.count
res16: Long = 6 

### --- 从分布式文件系统加载数据

~~~     # 从分布式文件系统加载数据
~~~     # 方案一：
scala> val lines = sc.textFile("hdfs://hadoop01:9000/user/root/data/yanqi.dat")
lines: org.apache.spark.rdd.RDD[String] = hdfs://hadoop01:9000/user/root/data/yanqi.dat MapPartitionsRDD[30] at textFile at <console>:24

scala> lines.count
res25: Long = 980                                                            

~~~     # 方案二：

scala> val lines = sc.textFile("/user/root/data/yanqi.dat")
lines: org.apache.spark.rdd.RDD[String] = /user/root/data/yanqi.dat MapPartitionsRDD[32] at textFile at <console>:24

scala> lines.count
res23: Long = 980

~~~     # 方案三：

scala> val lines = sc.textFile("data/yanqi.dat")
lines: org.apache.spark.rdd.RDD[String] = data/yanqi.dat MapPartitionsRDD[34] at textFile at <console>:24

scala> lines.count
res25: Long = 980

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七、从RDD创建RDD

### --- 从RDD创建RDD

~~~     本质是将一个RDD转换为另一个RDD。详细信息参见 3.5 Transformation

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Walter Savage Landor:strove with none,for none was worth my strife.Nature I loved and, next to Nature, Art:I warm'd both hands before the fire of life.It sinks, and I am ready to depart

                                                                                                                                                   ——W.S.Landor

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