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這篇文章主要介紹了Scala中如何實現文件讀取、寫入、控制臺操作，具有一定借鑒價值，感興趣的朋友可以參考下，希望大家閱讀完這篇文章之后大有收獲，下面讓小編帶著大家一起了解一下。
Scala文件讀取
E盤根目錄下scalaIO.txt文件內容如下：
文件讀取示例代碼：
//文件讀取
val file=Source.fromFile("E:\\scalaIO.txt")
for(line <- file.getLines)
{
println(line)
}
file.close
說明1：file=Source.fromFile(“E:\scalaIO.txt”)，其中Source中的fromFile()方法源自 import scala.io.Source源碼包，源碼如下圖：
file.getLines(),返回的是一個迭代器-Iterator;源碼如下：(scala.io)
Scala 網絡資源讀取
//網絡資源讀取
val webFile=Source.fromURL("http://spark.apache.org")
webFile.foreach(print)
webFile.close()fromURL()方法源碼如下：
/** same as fromURL(new URL(s)) */ def fromURL(s: String)(implicit codec: Codec): BufferedSource = fromURL(new URL(s))(codec)
讀取的網絡資源資源內容如下：
<!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™ - Lightning-Fast Cluster Computing
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<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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Lightning-fast cluster computing
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<li><a href="/docs/latest/" rel="external nofollow" >Latest Release (Spark 1.5.1)</a></li>
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<h6>Latest News</h6>
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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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<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>
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<div class="jumbotron">
<b>Apache Spark™</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 />
<br />
text_file.<span class="sparkop">flatMap</span>(<span class="closure">lambda line: line.split()</span>)<br />
.<span class="sparkop">map</span>(<span class="closure">lambda word: (word, 1)</span>)<br />
.<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/>
<br/>
text_file.<span class="sparkop">filter</span>(<span class="closure">lambda line: "ERROR" in line</span>)<br/>
.<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 — 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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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>.
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Process finished with exit code 0 //網絡資源讀取
val webFile=Source.fromURL("http://www.baidu.com/")
webFile.foreach(print)
webFile.close()讀取中文資源站點，出現編碼混亂問題如下：（解決辦法自行解決，本文不是重點）
Exception in thread "main" java.nio.charset.MalformedInputException: Input length = 1
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