Scala之文件讀取、寫入、控制臺操作的方法示例
更新時間：2019年06月17日 11:00:28 作者：robwang151
這篇文章主要介紹了Scala之文件讀取、寫入、控制臺操作的方法示例,文中通過示例代碼介紹的非常詳細，對大家的學習或者工作具有一定的參考學習價值，需要的朋友們下面隨著小編來一起學習學習吧
Scala文件讀取
E盤根目錄下scalaIO.txt文件內(nèi)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(wǎng)絡資源讀取
//網(wǎng)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(wǎng)絡資源資源內(nèi)容如下：
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Apache Spark™ - Lightning-Fast Cluster Computing
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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>
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<span class="small">(Sep 09, 2015)</span></li>
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<b>Apache Spark™</b> is a fast and general engine for large-scale data processing.
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<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.
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Write applications quickly in Java, Scala, Python, R.
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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.
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<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>
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<!--
<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>
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<h2>Generality</h2>
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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>
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<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 rel="external nofollow" >Apache Mesos</a>.
Access data in <a rel="external nofollow" >HDFS</a>, <a rel="external nofollow" >Cassandra</a>, <a rel="external nofollow" >HBase</a>,
<a rel="external nofollow" >Hive</a>, <a rel="external nofollow" >Tachyon</a>, and any Hadoop data source.
</p>
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</div>
</div>
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<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 rel="external nofollow" >Spark Summit</a>
conference, or on the
<a 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 rel="external nofollow" >Bay Area Spark meetup</a> and
<a rel="external nofollow" >Spark Summit</a>.</li>
<li>We use <a rel="external nofollow" rel="external nofollow" >JIRA</a> for issue tracking.</li>
</ul>
</div>
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<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 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 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 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>
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Process finished with exit code 0
//網(wǎng)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(xiàn)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
以上就是本文的全部內(nèi)容，希望對大家的學習有所幫助，也希望大家多多支持腳本之家。
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