Scala之文件读取、写入、控制台操作的方法示例
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2022-05-24 15:38:37
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.fromfi...
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>
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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>
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<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>
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built-in libraries:
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<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" />
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<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" />
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<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">
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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>.
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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
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。