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Spark
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Python虚拟环境
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Spark本地安装部署
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<a href="." >Spark本地安装部署</a>
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<p>Spark的线上环境通常部署在集群中，同时还依赖Java、Scala以及Hadoop。对于Spark的学习者，或者工作中需要在本机开发测试的同学，在自己工作学习的机器上搭建一套Spark系统是一件相对复杂，而且对本机性能要求比较高的一件事情。</p>
<p>这里推荐两种本机部署Spark开发环境的方法。</p>
<h2 id="pip安装pyspark">Pip安装pySpark</h2>
<p>最简单的方法，没有之一：</p>
<pre class="language-"><code class="lang-bash">$ pip <span class="token function">install</span> pyspark
</code></pre>
<p>就完成了Spark的本地安装。可以开始利用python开发Spark代码，用本地模式运行。对于初学者极为友好。本地机器部署，完全不需要采用常规的先装Java、Scala以及Hadoop，再下载Spark压缩包的方式进行安装。</p>
<p>另外，别看它是装的pySpark，但实际上已经包含了全部Spark的内容。具体可以查看pySpark的安装路径，例如：<code>lib/python3.9/site-packages/pyspark</code>：</p>
<pre class="language-"><code class="lang-bash">pyspark $ <span class="token function">ls</span>
__init__.py broadcast.py examples java_gateway.py profiler.py resultiterable.py statcounter.py util.py
__pycache__ cloudpickle files.py join.py py.typed sbin status.py version.py
_globals.py conf.py find_spark_home.py licenses python serializers.py storagelevel.py worker.py
_typing.pyi context.py install.py ml rdd.py shell.py streaming
accumulators.py daemon.py instrumentation_utils.py mllib rddsampler.py shuffle.py taskcontext.py
bin data jars pandas resource sql traceback_utils.py
</code></pre>
<p>其中<code>bin</code>目录下包含了<code>spark-shell</code>是Scala的交互式命令行，也包含了<code>spark-submit</code>可以用来提交<code>jar</code>包，等等。同样也可以在本地<code>4040</code>端口打开Web页面监控程序运行情况。</p>
<h2 id="docker">Docker</h2>
<p>对于想要体验或者研究Spark集群运行的同学，推荐使用Docker的方式。可以极大降低对本地机器性能的要求。</p>
<p>To do</p>
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