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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：
Lightning-fast cluster computing
Built-in Libraries:
Third-Party Packages
Apache Spark™ is a fast and general engine for large-scale data processing.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.
Spark has an advanced DAG execution engine that supports cyclic data flow and in-memory computing.
Logistic regression in Hadoop and Spark
Write applications quickly in Java, Scala, Python, R.
Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python and R shells.
text_file = spark.textFile("hdfs://...")
text_file.flatMap(lambdaline:line.split())
.map(lambda word: (word, 1))
.reduceByKey(lambda a, b: a+b)
Word count in Spark's Python API
Combine SQL, streaming, and complex analytics.
Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.
Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access perse data sources including HDFS, Cassandra, HBase, and S3.
You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, or on Apache Mesos. Access data in HDFS, Cassandra, HBase, Hive, Tachyon, and any Hadoop data source.
Spark is used at a wide range of organizations to process large datasets. You can find example use cases at the Spark Summit conference, or on the Powered By page.
There are many ways to reach the community:
Apache Spark is built by a wide set of developers from over 200 companies. Since 2009, more than 800 developers have contributed to Spark!
The project's committers come from 16 organizations.
If you'd like to participate in Spark, or contribute to the libraries on top of it, learn how to contribute.
Learning Spark is easy whether you come from a Java or Python background:
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
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