Scala中如何实现文件读取、写入、控制台操作

发布时间：2021-07-26 10:10:32 作者：小新
来源：亿速云阅读：150

这篇文章主要介绍了Scala中如何实现文件读取、写入、控制台操作，具有一定借鉴价值，感兴趣的朋友可以参考下，希望大家阅读完这篇文章之后大有收获，下面让小编带着大家一起了解一下。

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

Scala中如何实现文件读取、写入、控制台操作

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：

Scala中如何实现文件读取、写入、控制台操作

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

Scala中如何实现文件读取、写入、控制台操作

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：

<!DOCTYPE html>
<html lang="en">
<head>
 <meta charset="utf-8">
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">

 <title>
  Apache Spark&trade; - Lightning-Fast Cluster Computing

 </title>




 <meta name="description" content="Apache Spark is a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.">


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   <li><a href="/news/submit-talks-to-spark-summit-east-2016.html" rel="external nofollow" >Submission is open for Spark Summit East 2016</a>
   <span class="small">(Oct 14, 2015)</span></li>

   <li><a href="/news/spark-1-5-1-released.html" rel="external nofollow" >Spark 1.5.1 released</a>
   <span class="small">(Oct 02, 2015)</span></li>

   <li><a href="/news/spark-1-5-0-released.html" rel="external nofollow" >Spark 1.5.0 released</a>
   <span class="small">(Sep 09, 2015)</span></li>

   <li><a href="/news/spark-summit-europe-agenda-posted.html" rel="external nofollow" >Spark Summit Europe agenda posted</a>
   <span class="small">(Sep 07, 2015)</span></li>

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  Download Spark
  </a>
  <p >
  Built-in Libraries:
  </p>
  <ul class="list-none">
  <li><a href="/sql/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" >SQL and DataFrames</a></li>
  <li><a href="/streaming/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" >Spark Streaming</a></li>
  <li><a href="/mllib/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" >MLlib (machine learning)</a></li>
  <li><a href="/graphx/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" >GraphX (graph)</a></li>
  </ul>
  <a href="http://spark-packages.org" rel="external nofollow" rel="external nofollow" >Third-Party Packages</a>
 </div>
 </div>

 <div class="col-md-9 col-md-pull-3">
 <div class="jumbotron">
 <b>Apache Spark&trade;</b> is a fast and general engine for large-scale data processing.
</div>

<div class="row row-padded">
 <div class="col-md-7 col-sm-7">
 <h3>Speed</h3>

 <p class="lead">
  Run programs up to 100x faster than
  Hadoop MapReduce in memory, or 10x faster on disk.
 </p>

 <p>
  Spark has an advanced DAG execution engine that supports cyclic data flow and
  in-memory computing.
 </p>
 </div>
 <div class="col-md-5 col-sm-5 col-padded-top col-center">
 <div >
  <img src="/images/logistic-regression.png"  />
  <div class="caption" >Logistic regression in Hadoop and Spark</div>
 </div>
 </div>
</div>

<div class="row row-padded">
 <div class="col-md-7 col-sm-7">
 <h3>Ease of Use</h3>

 <p class="lead">
  Write applications quickly in Java, Scala, Python, R.
 </p>

 <p>
  Spark offers over 80 high-level operators that make it easy to build parallel apps.
  And you can use it <em>interactively</em>
  from the Scala, Python and R shells.
 </p>
 </div>
 <div class="col-md-5 col-sm-5 col-padded-top col-center">
 <div >
  <div class="code">
  text_file = spark.textFile(<span class="string">"hdfs://..."</span>)<br />
  &nbsp;<br />
  text_file.<span class="sparkop">flatMap</span>(<span class="closure">lambda&nbsp;line:&nbsp;line.split()</span>)<br />
  &nbsp;&nbsp;&nbsp;&nbsp;.<span class="sparkop">map</span>(<span class="closure">lambda word: (word, 1)</span>)<br />
  &nbsp;&nbsp;&nbsp;&nbsp;.<span class="sparkop">reduceByKey</span>(<span class="closure">lambda a, b: a+b</span>)
  </div>
  <div class="caption">Word count in Spark's Python API</div>
 </div>
 <!--
 <div class="code" >
  text_file = spark.textFile(<span class="string">"hdfs://..."</span>)<br/>
  &nbsp;<br/>
  text_file.<span class="sparkop">filter</span>(<span class="closure">lambda line: "ERROR" in line</span>)<br/>
  &nbsp;&nbsp;&nbsp;&nbsp;.<span class="sparkop">count</span>()
 </div>
 -->
 <!--<div class="caption">Word count in Spark</div>-->
 </div>
</div>

<div class="row row-padded">
 <div class="col-md-7 col-sm-7">
 <h3>Generality</h3>

 <p class="lead">
  Combine SQL, streaming, and complex analytics.
 </p>

 <p>
  Spark powers a stack of libraries including
  <a href="/sql/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" >SQL and DataFrames</a>, <a href="/mllib/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" >MLlib</a> for machine learning,
  <a href="/graphx/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" >GraphX</a>, and <a href="/streaming/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" >Spark Streaming</a>.
  You can combine these libraries seamlessly in the same application.
 </p>
 </div>
 <div class="col-md-5 col-sm-5 col-padded-top col-center">
 <img src="/images/spark-stack.png"  usemap="#stack-map" />
 <map name="stack-map">
  <area shape="rect" coords="0,0,74,95" href="/sql/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" alt="Spark SQL" title="Spark SQL" />
  <area shape="rect" coords="74,0,150,95" href="/streaming/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" alt="Spark Streaming" title="Spark Streaming" />
  <area shape="rect" coords="150,0,224,95" href="/mllib/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" alt="MLlib (machine learning)" title="MLlib" />
  <area shape="rect" coords="225,0,300,95" href="/graphx/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" alt="GraphX" title="GraphX" />
 </map>
 </div>
</div>

<div class="row row-padded" >
 <div class="col-md-7 col-sm-7">
 <h3>Runs Everywhere</h3>

 <p class="lead">
  Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, and S3.
 </p>

 <p>
  You can run Spark using its <a href="/docs/latest/spark-standalone.html" rel="external nofollow" >standalone cluster mode</a>, on <a href="/docs/latest/ec2-scripts.html" rel="external nofollow" >EC2</a>, on Hadoop YARN, or on <a href="http://mesos.apache.org" rel="external nofollow" >Apache Mesos</a>.
  Access data in <a href="http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsUserGuide.html" rel="external nofollow" >HDFS</a>, <a href="http://cassandra.apache.org" rel="external nofollow" >Cassandra</a>, <a href="http://hbase.apache.org" rel="external nofollow" >HBase</a>,
  <a href="http://hive.apache.org" rel="external nofollow" >Hive</a>, <a href="http://tachyon-project.org" rel="external nofollow" >Tachyon</a>, and any Hadoop data source.
 </p>
 </div>
 <div class="col-md-5 col-sm-5 col-padded-top col-center">
 <img src="/images/spark-runs-everywhere.png"  />
 </div>
</div>


 </div>
</div>



<div class="row">
 <div class="col-md-4 col-padded">
 <h4>Community</h4>

 <p>
  Spark is used at a wide range of organizations to process large datasets.
  You can find example use cases at the <a href="http://spark-summit.org/summit-2013/" rel="external nofollow" >Spark Summit</a>
  conference, or on the
  <a href="https://cwiki.apache.org/confluence/display/SPARK/Powered+By+Spark" rel="external nofollow" rel="external nofollow" >Powered By</a>
  page.
 </p>

 <p>
  There are many ways to reach the community:
 </p>
 <ul class="list-narrow">
  <li>Use the <a href="/community.html#mailing-lists" rel="external nofollow" >mailing lists</a> to ask questions.</li>
  <li>In-person events include the <a href="http://www.meetup.com/spark-users/" rel="external nofollow" >Bay Area Spark meetup</a> and
  <a href="http://spark-summit.org/" rel="external nofollow" >Spark Summit</a>.</li>
  <li>We use <a href="https://issues.apache.org/jira/browse/SPARK" rel="external nofollow" rel="external nofollow" >JIRA</a> for issue tracking.</li>
 </ul>
 </div>

 <div class="col-md-4 col-padded">
 <h4>Contributors</h4>

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

 <p>
  The project's
  <a href="https://cwiki.apache.org/confluence/display/SPARK/Committers" rel="external nofollow" rel="external nofollow" >committers</a>
  come from 16 organizations.
 </p>

 <p>
  If you'd like to participate in Spark, or contribute to the libraries on top of it, learn
  <a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark" rel="external nofollow" >how to
  contribute</a>.
 </p>
 </div>

 <div class="col-md-4 col-padded">
 <h4>Getting Started</h4>

 <p>Learning Spark is easy whether you come from a Java or Python background:</p>
 <ul class="list-narrow">
  <li><a href="/downloads.html" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" >Download</a> the latest release &mdash; you can run Spark locally on your laptop.</li>
  <li>Read the <a href="/docs/latest/quick-start.html" rel="external nofollow" >quick start guide</a>.</li>
  <li>
  Spark Summit 2014 contained free <a href="http://spark-summit.org/2014/training" rel="external nofollow" >training videos and exercises</a>.
  </li>
  <li>Learn how to <a href="/docs/latest/#launching-on-a-cluster" rel="external nofollow" >deploy</a> Spark on a cluster.</li>
 </ul>
 </div>
</div>

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 <div class="col-sm-12 col-center">
 <a href="/downloads.html" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" class="btn btn-success btn-lg" >Download Spark</a>
 </div>
</div>




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 <hr>
 Apache Spark, Spark, Apache, and the Spark logo are trademarks of
 <a href="http://www.apache.org" rel="external nofollow" >The Apache Software Foundation</a>.
</footer>

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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

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Scala中如何实现文件读取、写入、控制台操作

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