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：
<!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
</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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Lightning-fast cluster computing
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<li><a href="/downloads.html" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" >Download</a></li>
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<li><a href="/docs/latest/" rel="external nofollow" >Latest Release (Spark 1.5.1)</a></li>
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<li><a href="https://cwiki.apache.org/confluence/display/SPARK/Powered+By+Spark" rel="external nofollow" rel="external nofollow" >Powered By</a></li>
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<h5>Latest News</h5>
<ul class="list-unstyled">
<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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Built-in Libraries:
</p>
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<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">
<h2>Speed</h2>
<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 style="width: 100%; max-width: 272px; display: inline-block; text-align: center;">
<img src="/images/logistic-regression.png" style="width: 100%; max-width: 250px;" />
<div class="caption" style="min-width: 272px;">Logistic regression in Hadoop and Spark</div>
</div>
</div>
</div>
<div class="row row-padded">
<div class="col-md-7 col-sm-7">
<h2>Ease of Use</h2>
<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 style="text-align: left; display: inline-block;">
<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" style="margin-top: 20px; text-align: left; display: inline-block;">
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">
<h2>Generality</h2>
<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" style="margin-top: 15px; width: 100%; max-width: 296px;" 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" <area shape="rect" coords="74,0,150,95" href="/streaming/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" <area shape="rect" coords="150,0,224,95" href="/mllib/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" <area shape="rect" coords="225,0,300,95" href="/graphx/" rel="external nofollow" rel="external nofollow" rel="external nofollow" rel="external nofollow" </map>
</div>
</div>
<div class="row row-padded" style="margin-bottom: 15px;">
<div class="col-md-7 col-sm-7">
<h2>Runs Everywhere</h2>
<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" style="width: 100%; max-width: 280px;" />
</div>
</div>
</div>
</div>
<div class="row">
<div class="col-md-4 col-padded">
<h3>Community</h3>
<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">
<h3>Contributors</h3>
<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">
<h3>Getting Started</h3>
<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>
<div class="row">
<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" style="width: 262px;">Download Spark</a>
</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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</body>
</html>
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服务器之家。
