Scala之文件读取、写入、控制台操作的方法示例

Scala文件读取

E盘根目录下scalaIO.txt文件内容如下：

文件读取示例代码：

 //文件读取
 val file=Source.fromFile("E:\\scalaIO.txt")
 for(line <- file.getLines)
 {
  println(line)
 }
 file.close

说明1：file=Source.fromFile(“E:\scalaIO.txt”)，其中Source中的fromFile()方法源自 import scala.io.Source源码包，源码如下图：

file.getLines(),返回的是一个迭代器-Iterator;源码如下：(scala.io)

Scala 网络资源读取

 //网络资源读取
 val webFile=Source.fromURL("http://spark.apache.org")
 webFile.foreach(print)
 webFile.close()

fromURL()方法源码如下：

 /** same as fromURL(new URL(s))
 */
 def fromURL(s: String)(implicit codec: Codec): BufferedSource =
 fromURL(new URL(s))(codec)

读取的网络资源资源内容如下：

 
 
 

 




 


 
 
 

 

 
 













 

  
  
   Lightning-fast cluster computing
  
 




 
 

 
  Toggle navigation
  
  
  
 
 

 
 

 

  
Download
  

  
   Libraries 
  
  

   
SQL and DataFrames
   
Spark Streaming
   
MLlib (machine learning)
   
GraphX (graph)
   

   
Third-Party Packages
  
  
  

  
   Documentation 
  
  

   
Latest Release (Spark 1.5.1)
   
Other Resources
  
  
  
Examples
  

  
   Community 
  
  

   
Mailing Lists
   
Events and Meetups
   
Project History
   
Powered By
   
Project Committers
   
Issue Tracker
  
  
  
FAQ
 
 
 




 

 

  
Latest News
  


   
Submission is open for Spark Summit East 2016
   (Oct 14, 2015)

   
Spark 1.5.1 released
   (Oct 02, 2015)

   
Spark 1.5.0 released
   (Sep 09, 2015)

   
Spark Summit Europe agenda posted
   (Sep 07, 2015)

  
  
Archive
 
 

  
  Download Spark
  
  

  Built-in Libraries:
  
  

  
SQL and DataFrames
  
Spark Streaming
  
MLlib (machine learning)
  
GraphX (graph)
  
  Third-Party Packages
 
 

 

 

 Apache Spark™ is a fast and general engine for large-scale data processing.



 

 
Speed

 

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

 

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

 

  
  
Logistic regression in Hadoop and Spark
 
 



 

 
Ease of Use

 

  Write applications quickly in Java, Scala, Python, R.
 

 

  Spark offers over 80 high-level operators that make it easy to build parallel apps.
  And you can use it interactively
  from the Scala, Python and R shells.
 
 
 

 

  

  text_file = spark.textFile("hdfs://...")

   

  text_file.flatMap(lambda line: line.split())

      .map(lambda word: (word, 1))

      .reduceByKey(lambda a, b: a+b)
  
  
Word count in Spark's Python API
 
 
 
 



 

 
Generality

 

  Combine SQL, streaming, and complex analytics.
 

 

  Spark powers a stack of libraries including
  SQL and DataFrames, MLlib for machine learning,
  GraphX, and Spark Streaming.
  You can combine these libraries seamlessly in the same application.
 
 
 

 
 
 



 

 
Runs Everywhere

 

  Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, and S3.
 

 

  You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, or on Apache Mesos.
  Access data in HDFS, Cassandra, HBase,
  Hive, Tachyon, and any Hadoop data source.
 
 
 

 
 



 





 

 
Community

 

  Spark is used at a wide range of organizations to process large datasets.
  You can find example use cases at the Spark Summit
  conference, or on the
  Powered By
  page.
 

 

  There are many ways to reach the community:
 
 

  
Use the mailing lists to ask questions.
  
In-person events include the Bay Area Spark meetup and
  Spark Summit.
  
We use JIRA for issue tracking.
 
 

 

 
Contributors

 

  Apache Spark is built by a wide set of developers from over 200 companies.
  Since 2009, more than 800 developers have contributed to Spark!
 

 

  The project's
  committers
  come from 16 organizations.
 

 

  If you'd like to participate in Spark, or contribute to the libraries on top of it, learn
  how to
  contribute.
 
 

 

 
Getting Started

 
Learning Spark is easy whether you come from a Java or Python background:
 

  
Download the latest release — you can run Spark locally on your laptop.
  
Read the quick start guide.
  

  Spark Summit 2014 contained free training videos and exercises.
  
  
Learn how to deploy Spark on a cluster.
 
 



 

 Download Spark
 






 

 Apache Spark, Spark, Apache, and the Spark logo are trademarks of
 The Apache Software Foundation.







Process finished with exit code 0

 //网络资源读取
 val webFile=Source.fromURL("http://www.baidu.com/")
 webFile.foreach(print)
 webFile.close()

读取中文资源站点，出现编码混乱问题如下：（解决办法自行解决，本文不是重点）

Exception in thread "main" java.nio.charset.MalformedInputException: Input length = 1

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。