brdd 惰性执行 mapreduce 提取指定类型值 WebUi 作业信息 全局临时视图 pyspark scala spark 安装

【rdd 惰性执行】

为了提高计算效率 spark 采用了哪些机制

1-rdd 基于分布式内存数据集进行运算

2-lazy evaluation  ：惰性执行，即rdd的变换操作并不是在运行该代码时立即执行，而仅记录下转换操作的对象；只有当运行到一个行动代码时，变换操作的计算逻辑才真正执行。

http://spark.apache.org/docs/latest/rdd-programming-guide.html#resilient-distributed-datasets-rdds

【 rdd 是数据结构，spark最小的处理单元，rdd的方法(rdd算子)实现数据的转换和归约。】

RDD Operations

RDDs support two types of operations: transformations, which create a new dataset from an existing one, and actions, which return a value to the driver program after running a computation on the dataset. For example, map is a transformation that passes each dataset element through a function and returns a new RDD representing the results. On the other hand, reduce is an action that aggregates all the elements of the RDD using some function and returns the final result to the driver program (although there is also a parallel reduceByKey that returns a distributed dataset).

All transformations in Spark are lazy, in that they do not compute their results right away. Instead, they just remember the transformations applied to some base dataset (e.g. a file). The transformations are only computed when an action requires a result to be returned to the driver program. This design enables Spark to run more efficiently. For example, we can realize that a dataset created through map will be used in a reduce and return only the result of the reduce to the driver, rather than the larger mapped dataset.

By default, each transformed RDD may be recomputed each time you run an action on it. However, you may also persist an RDD in memory using the persist (or cache) method, in which case Spark will keep the elements around on the cluster for much faster access the next time you query it. There is also support for persisting RDDs on disk, or replicated across multiple nodes.

http://wiki.mbalib.com/wiki/肯德尔等级相关系数

http://baike.baidu.com/item/kendall秩相关系数

斯皮尔曼等级相关（Spearman Rank Correlation）

http://wiki.mbalib.com/wiki/斯皮尔曼等级相关

Spark3重相关系数的计算

【安装】

C:\spark211binhadoop27\bin>echo %HADOOP_HOME%
C:\spark211binhadoop27

C:\spark211binhadoop27\bin>%HADOOP_HOME%\bin\winutils.exe ls \tmp\hive
d--------- 1 PC-20170702HMLH\Administrator PC-20170702HMLH\None 0 Jul  7 2017 \tmp\hive

C:\spark211binhadoop27\bin> %HADOOP_HOME%\bin\winutils.exe chmod 777 \tmp\hive

C:\spark211binhadoop27\bin> %HADOOP_HOME%\bin\winutils.exe ls \tmp\hive
drwxrwxrwx 1 PC-20170702HMLH\Administrator PC-20170702HMLH\None 0 Jul  7 2017 \tmp\hive

C:\spark211binhadoop27\bin>spark-shell
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
17/07/07 09:43:13 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/07/07 09:43:14 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
17/07/07 09:43:15 WARN General: Plugin (Bundle) "org.datanucleus" is already registered. Ensure you dont have multiple JAR versions of the same plugin in the classpath. The URL "file:/C:/spark211binha
doop27/bin/../jars/datanucleus-core-3.2.10.jar" is already registered, and you are trying to register an identical plugin located at URL "file:/C:/spark211binhadoop27/jars/datanucleus-core-3.2.10.jar.
"
17/07/07 09:43:15 WARN General: Plugin (Bundle) "org.datanucleus.store.rdbms" is already registered. Ensure you dont have multiple JAR versions of the same plugin in the classpath. The URL "file:/C:/s
park211binhadoop27/bin/../jars/datanucleus-rdbms-3.2.9.jar" is already registered, and you are trying to register an identical plugin located at URL "file:/C:/spark211binhadoop27/jars/datanucleus-rdbm
s-3.2.9.jar."
17/07/07 09:43:15 WARN General: Plugin (Bundle) "org.datanucleus.api.jdo" is already registered. Ensure you dont have multiple JAR versions of the same plugin in the classpath. The URL "file:/C:/spark
211binhadoop27/bin/../jars/datanucleus-api-jdo-3.2.6.jar" is already registered, and you are trying to register an identical plugin located at URL "file:/C:/spark211binhadoop27/jars/datanucleus-api-jd
o-3.2.6.jar."
17/07/07 09:43:18 WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 1.2.0
17/07/07 09:43:18 WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
17/07/07 09:43:20 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
Spark context Web UI available at http://192.168.0.155:4041
Spark context available as 'sc' (master = local[*], app id = local-1499391794538).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.1.1
      /_/

Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_131)
Type in expressions to have them evaluated.
Type :help for more information.

scala> var file = sc.textFile("E:\\LearnSpark\\word.txt")
file: org.apache.spark.rdd.RDD[String] = E:\LearnSpark\word.txt MapPartitionsRDD[1] at textFile at <console>:24

scala> var counts = file.flatMap(line=>line.split(" ")).map(word=>(word,1)).reduceByKey(_ + _)
counts: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[4] at reduceByKey at <console>:26

scala> counts.saveASTextFile("E:\\LearnSpark\\count.txt")
<console>:29: error: value saveASTextFile is not a member of org.apache.spark.rdd.RDD[(String, Int)]
       counts.saveASTextFile("E:\\LearnSpark\\count.txt")
              ^

scala> counts.saveAsTextFile("E:\\LearnSpark\\count.txt")
org.apache.hadoop.mapred.FileAlreadyExistsException: Output directory file:/E:/LearnSpark/count.txt already exists
  at org.apache.hadoop.mapred.FileOutputFormat.checkOutputSpecs(FileOutputFormat.java:131)
  at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply$mcV$sp(PairRDDFunctions.scala:1191)
  at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply(PairRDDFunctions.scala:1168)
  at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply(PairRDDFunctions.scala:1168)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
  at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
  at org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopDataset(PairRDDFunctions.scala:1168)
  at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$4.apply$mcV$sp(PairRDDFunctions.scala:1071)
  at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$4.apply(PairRDDFunctions.scala:1037)
  at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$4.apply(PairRDDFunctions.scala:1037)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
  at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
  at org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopFile(PairRDDFunctions.scala:1037)
  at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$1.apply$mcV$sp(PairRDDFunctions.scala:963)
  at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$1.apply(PairRDDFunctions.scala:963)
  at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopFile$1.apply(PairRDDFunctions.scala:963)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
  at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
  at org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopFile(PairRDDFunctions.scala:962)
  at org.apache.spark.rdd.RDD$$anonfun$saveAsTextFile$1.apply$mcV$sp(RDD.scala:1489)
  at org.apache.spark.rdd.RDD$$anonfun$saveAsTextFile$1.apply(RDD.scala:1468)
  at org.apache.spark.rdd.RDD$$anonfun$saveAsTextFile$1.apply(RDD.scala:1468)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
  at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
  at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
  at org.apache.spark.rdd.RDD.saveAsTextFile(RDD.scala:1468)
  ... 48 elided

scala> counts.saveAsTextFile("E:\\LearnSpark\\count.txt")

scala>

　　

https://blogs.msdn.microsoft.com/arsen/2016/02/09/resolving-spark-1-6-0-java-lang-nullpointerexception-not-found-value-sqlcontext-error-when-running-spark-shell-on-windows-10-64-bit/

C:\spark211binhadoop27\bin>echo %HADOOP_HOME%
C:\spark211binhadoop27

C:\spark211binhadoop27\bin>%HADOOP_HOME%\bin\winutils.exe ls \tmp\hive
d---------  PC-20170702HMLH\Administrator PC-20170702HMLH\None  Jul    \tmp\hive

C:\spark211binhadoop27\bin> %HADOOP_HOME%\bin\winutils.exe chmod  \tmp\hive

C:\spark211binhadoop27\bin> %HADOOP_HOME%\bin\winutils.exe ls \tmp\hive
drwxrwxrwx  PC-20170702HMLH\Administrator PC-20170702HMLH\None  Jul    \tmp\hive

C:\spark211binhadoop27\bin>spark-shell
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
// :: WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
// :: WARN Utils: Service 'SparkUI' could not bind on port . Attempting port .
// :: WARN General: Plugin (Bundle) "org.datanucleus" is already registered. Ensure you dont have multiple JAR versions of the same plugin in the classpath. The URL "file:/C:/spark211binha
doop27/bin/../jars/datanucleus-core-3.2..jar" is already registered, and you are trying to register an identical plugin located at URL "file:/C:/spark211binhadoop27/jars/datanucleus-core-3.2..jar.
"
// :: WARN General: Plugin (Bundle) "org.datanucleus.store.rdbms" is already registered. Ensure you dont have multiple JAR versions of the same plugin in the classpath. The URL "file:/C:/s
park211binhadoop27/bin/../jars/datanucleus-rdbms-3.2..jar" is already registered, and you are trying to register an identical plugin located at URL "file:/C:/spark211binhadoop27/jars/datanucleus-rdbm
s-3.2..jar."
// :: WARN General: Plugin (Bundle) "org.datanucleus.api.jdo" is already registered. Ensure you dont have multiple JAR versions of the same plugin in the classpath. The URL "file:/C:/spark
211binhadoop27/bin/../jars/datanucleus-api-jdo-3.2..jar" is already registered, and you are trying to register an identical plugin located at URL "file:/C:/spark211binhadoop27/jars/datanucleus-api-jd
o-3.2..jar."
// :: WARN ObjectStore: Version information not found in metastore. hive.metastore.schema.verification is not enabled so recording the schema version 1.2.
// :: WARN ObjectStore: Failed to get database default, returning NoSuchObjectException
// :: WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
Spark context Web UI available at http://192.168.0.155:4041
Spark context available as 'sc' (master = local[*], app id = local-).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.1.
      /_/

Using Scala version 2.11. (Java HotSpot(TM) -Bit Server VM, Java 1.8.0_131)
Type in expressions to have them evaluated.
Type :help for more information.

scala>

设置Windows系统的环境变量 HADDOP_HOME为

HADDOP_HOME  E:\LearnSpark\spark-2.1.1-bin-hadoop2.7

from pyspark.sql import SparkSession
import re

f = 'part-m-00003'
spark = SparkSession.builder.appName("spark_from_log_chk_unique_num").getOrCreate()

unique_mac_num = spark.read.text(f).rdd.map(lambda r: r[0]).map(
    lambda s: sorted(re.findall('[0-9a-z]{2}[0-9a-z]{2}[0-9a-z]{2}[0-9a-z]{2}[0-9a-z]{2}', s)[3:])).map(
    lambda l: ' '.join(l)).flatMap(lambda x: x.split(' ')).distinct().count()

ssh://sci@192.168.16.128:22/usr/bin/python -u /home/sci/.pycharm_helpers/pydev/pydevd.py --multiproc --qt-support --client '0.0.0.0' --port 45131 --file /home/win_pymine_clean/filter_mac/do_action.py
warning: Debugger speedups using cython not found. Run '"/usr/bin/python" "/home/sci/.pycharm_helpers/pydev/setup_cython.py" build_ext --inplace' to build.
pydev debugger: process 10092 is connecting

Connected to pydev debugger (build 171.3780.115)
pydev debugger: warning: trying to add breakpoint to file that does not exist: /home/win_pymine_clean/filter_mac/4.py (will have no effect)
pydev debugger: warning: trying to add breakpoint to file that does not exist: /home/win_pymine_clean/feature_wifi/mechanical_proving.py (will have no effect)
pydev debugger: warning: trying to add breakpoint to file that does not exist: /home/win_pymine_clean/filter_mac/chk_single_mac.py (will have no effect)
pydev debugger: warning: trying to add breakpoint to file that does not exist: /home/win_pymine_clean/spider_businessarea/spider_58.py (will have no effect)
pydev debugger: warning: trying to add breakpoint to file that does not exist: /home/win_pymine_clean/filter_mac/4.py (will have no effect)
pydev debugger: warning: trying to add breakpoint to file that does not exist: /home/win_pymine_clean/filter_mac/4.py (will have no effect)
pydev debugger: warning: trying to add breakpoint to file that does not exist: /home/win_pymine_clean/spider_map/one_row_unique_num.py (will have no effect)
pydev debugger: warning: trying to add breakpoint to file that does not exist: /home/win_pymine_clean/spider_businessarea/spider_58.py (will have no effect)
pydev debugger: warning: trying to add breakpoint to file that does not exist: /home/win_pymine_clean/feature_wifi/matrix_singular_value_decomposition.py (will have no effect)
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
17/10/24 00:45:54 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/10/24 00:45:54 WARN Utils: Your hostname, ubuntu resolves to a loopback address: 127.0.1.1; using 192.168.16.128 instead (on interface ens33)
17/10/24 00:45:54 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address
17/10/24 00:45:55 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
17/10/24 00:45:55 WARN Utils: Service 'SparkUI' could not bind on port 4041. Attempting port 4042.

【  WebUi 作业信息 】

【DAG】

sci 192.168.16.128

sudo apt install net-tools
h0 192.168.16.129
h1 192.168.16.130
h2 192.168.16.131
h3 192.168.16.132
h4 192.168.16.133
h5 192.168.16.134
test 192.168.16.135
sudo apt install ssh

【
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.

Apache Spark has an advanced DAG execution engine that supports acyclic data flow and in-memory computing.

Two types of links

There are two types of links

symbolic links: Refer to a symbolic path indicating the abstract location of another file
hard links : Refer to the specific location of physical data.
How do I create soft link / symbolic link?

Soft links are created with the ln command. For example, the following would create a soft link named link1 to a file named file1, both in the current directory
$ ln -s file1 link1

To verify new soft link run:
$ ls -l file1 link1
】

【
Hard link vs. Soft link in Linux or UNIX

Hard links cannot link directories.
Cannot cross file system boundaries.
Soft or symbolic links are just like hard links. It allows to associate multiple filenames with a single file. However, symbolic links allows:

To create links between directories.
Can cross file system boundaries.
These links behave differently when the source of the link is moved or removed.

Symbolic links are not updated.
Hard links always refer to the source, even if moved or removed.
】

{

【
lrwxrwxrwx  1 root root          9 Jan 24  2017 python -> python2.7*
lrwxrwxrwx  1 root root          9 Jan 24  2017 python2 -> python2.7*
-rwxr-xr-x  1 root root    3785256 Jan 19  2017 python2.7*
lrwxrwxrwx  1 root root         33 Jan 19  2017 python2.7-config -> x86_64-linux-gnu-python2.7-config*
lrwxrwxrwx  1 root root         16 Jan 24  2017 python2-config -> python2.7-config*
lrwxrwxrwx  1 root root          9 Sep 17 20:24 python3 -> python3.5*

}
sudo rm  py*2
sudo rm  python
sudo ln -s  ./python3.5  python

【
Main entry point for Spark functionality. A SparkContext represents the connection to a Spark cluster, and can be used to create RDD and broadcast variables on that cluster.
】

【pyspark mapreduce  计算文件中指定类型值的个数】

from pyspark.sql import SparkSession
import re

f = 'part-m-00003'
spark = SparkSession.builder.appName("spark_from_log_chk_unique_num").getOrCreate()

unique_mac_num = spark.read.text(f).rdd.map(lambda r: r[0]).map(
    lambda s: sorted(re.findall('[0-9a-z]{2}[0-9a-z]{2}[0-9a-z]{2}[0-9a-z]{2}[0-9a-z]{2}', s)[3:])).map(
    lambda l: ' '.join(l)).flatMap(lambda x: x.split(' ')).distinct().count()

dd =9

　　

【全局临时视图】

Global Temporary View

http://spark.apache.org/docs/latest/sql-programming-guide.html#global-temporary-view

【 session-scoped】

Temporary views in Spark SQL are session-scoped and will disappear if the session that creates it terminates. If you want to have a temporary view that is shared among all sessions and keep alive until the Spark application terminates, you can create a global temporary view. Global temporary view is tied to a system preserved database global_temp, and we must use the qualified name to refer it, e.g. SELECT * FROM global_temp.view1.

// Register the DataFrame as a global temporary view
df.createGlobalTempView("people");

// Global temporary view is tied to a system preserved database `global_temp`
spark.sql("SELECT * FROM global_temp.people").show();
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+

// Global temporary view is cross-session
spark.newSession().sql("SELECT * FROM global_temp.people").show();
// +----+-------+
// | age|   name|
// +----+-------+
// |null|Michael|
// |  30|   Andy|
// |  19| Justin|
// +----+-------+

scala  spark  wordcount  windows

1-安装

- jdk  jdk-8u171-windows-x64.exe   并添加到系统Path spark-shell运行需要  D:\scalajre\jre\bin\java.exe  ；  路径问题，在代码中有明确的答案

- IDE http://scala-ide.org/download/sdk.html Download Scala IDE for Eclipse

- scala https://scala-lang.org/download/   https://downloads.lightbend.com/scala/2.12.6/scala-2.12.6.msi   添加到系统Path，便于在cmd开启

注意安装到无空格目录

cmd scala  环境变量  SCALA_HOME   D:\scalainstall\bin

- 下载spark

　http://220.112.193.199/files/9037000006180326/mirrors.shu.edu.cn/apache/spark/spark-2.3.0/spark-2.3.0-bin-hadoop2.7.tgz

　解压至 D:\LearnSpark\win

下载 http://public-repo-1.hortonworks.com/hdp-win-alpha/winutils.exe （win7 可用） ，拷贝到D:\LearnSpark\win\spark-2.3.0-bin-hadoop2.7\bin

　版本参考  Running Spark Applications on Windows · Mastering Apache Spark https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/spark-tips-and-tricks-running-spark-windows.html

　设置HADOOP_HOME 为 D:\LearnSpark\win\spark-2.3.0-bin-hadoop2.7

　为了在cmd.exe 中直接使用spark-shell，将D:\LearnSpark\win\spark-2.3.0-bin-hadoop2.7\bin添加到Path

C:\Users\Administrator>spark-shell
2018-05-20 20:10:42 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platfo
rm... 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://192.168.1.102:4040
Spark context available as 'sc' (master = local[*], app id = local-1526818258887).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.3.0
      /_/

Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_171)
Type in expressions to have them evaluated.
Type :help for more information.

scala>

移除winutils.exe

Microsoft Windows [版本 6.1.7601]
版权所有 (c) 2009 Microsoft Corporation。保留所有权利。

C:\Users\Administrator>spark-shell
2018-05-20 20:15:37 ERROR Shell:397 - Failed to locate the winutils binary in the hadoop binary path

java.io.IOException: Could not locate executable D:\LearnSpark\win\spark-2.3.0-bin-hadoop2.7\bin\win
utils.exe in the Hadoop binaries.
        at org.apache.hadoop.util.Shell.getQualifiedBinPath(Shell.java:379)
        at org.apache.hadoop.util.Shell.getWinUtilsPath(Shell.java:394)
        at org.apache.hadoop.util.Shell.<clinit>(Shell.java:387)
        at org.apache.hadoop.util.StringUtils.<clinit>(StringUtils.java:80)
        at org.apache.hadoop.security.SecurityUtil.getAuthenticationMethod(SecurityUtil.java:611)
        at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:273)

        at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.ja
va:261)
        at org.apache.hadoop.security.UserGroupInformation.loginUserFromSubject(UserGroupInformation
.java:791)
        at org.apache.hadoop.security.UserGroupInformation.getLoginUser(UserGroupInformation.java:76
1)
        at org.apache.hadoop.security.UserGroupInformation.getCurrentUser(UserGroupInformation.java:
634)
        at org.apache.spark.util.Utils$$anonfun$getCurrentUserName$1.apply(Utils.scala:2464)
        at org.apache.spark.util.Utils$$anonfun$getCurrentUserName$1.apply(Utils.scala:2464)
        at scala.Option.getOrElse(Option.scala:121)
        at org.apache.spark.util.Utils$.getCurrentUserName(Utils.scala:2464)
        at org.apache.spark.SecurityManager.<init>(SecurityManager.scala:222)
        at org.apache.spark.deploy.SparkSubmit$.secMgr$lzycompute$1(SparkSubmit.scala:393)
        at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$secMgr$1(SparkS
ubmit.scala:393)
        at org.apache.spark.deploy.SparkSubmit$$anonfun$prepareSubmitEnvironment$7.apply(SparkSubmit
.scala:401)
        at org.apache.spark.deploy.SparkSubmit$$anonfun$prepareSubmitEnvironment$7.apply(SparkSubmit
.scala:401)
        at scala.Option.map(Option.scala:146)
        at org.apache.spark.deploy.SparkSubmit$.prepareSubmitEnvironment(SparkSubmit.scala:400)
        at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:170)
        at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:136)
        at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
2018-05-20 20:15:37 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platfo
rm... 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://192.168.1.102:4040
Spark context available as 'sc' (master = local[*], app id = local-1526818554657).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.3.0
      /_/

Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_171)
Type in expressions to have them evaluated.
Type :help for more information.

scala>

　　主要windows下winutils.exe的位置，结果文件名不要在目标文件夹存在

Microsoft Windows [版本 6.1.7601]
版权所有 (c) 2009 Microsoft Corporation。保留所有权利。

C:\Users\Administrator>spark-shell
2018-05-20 20:29:41 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platfo
rm... 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://192.168.1.102:4040
Spark context available as 'sc' (master = local[*], app id = local-1526819397675).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.3.0
      /_/

Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_171)
Type in expressions to have them evaluated.
Type :help for more information.

scala> var file=sc.textFile("D:\\LearnSpark\\win\\mytest.txt")
file: org.apache.spark.rdd.RDD[String] = D:\LearnSpark\win\mytest.txt MapPartitionsRDD[1] at textFil
e at <console>:24

scala> var counts100=file.flatMap(line=>line.split(" ")).map(word=>(word,100)).reduceByKey(_+_)
counts100: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[4] at reduceByKey at <console>:25

scala> counts100.saveAsTextFile("D:\\LearnSpark\\win\\my_unique_name.txt")

scala>

　　

win10 运行demo

var textFile=spark.read.textFile("D://SparkProducing//userdata//testenv.txt")

The root scratch dir: /tmp/hive on HDFS should be writable

The root scratch dir: /tmp/hive on HDFS should be writable. Current permissions are: rw-rw-rw- (on Windows) - Stack Overflow https://stackoverflow.com/questions/34196302/the-root-scratch-dir-tmp-hive-on-hdfs-should-be-writable-current-permissions

查C盘  存在 c://tmp//hive  修改权限

textFile.count() // Number of items in this Dataset

textFile.first() // First item in this Dataset

var lineWithSpark=textFile.filter(line=>line.contains("Spark"))

lineWithSpark.collect()