• Submission is open for Spark Summit East 2016   (Oct 14, 2015)
 
• Spark 1.5.1 released   (Oct 02, 2015)
 
• Spark 1.5.0 released   (Sep 09, 2015)
 
• Spark Summit Europe agenda posted   (Sep 07, 2015)
 

Archive

   Download Spark    

 Built-in Libraries:  

 
• SQL and DataFrames
 
• Spark Streaming
 
• MLlib (machine learning)
 
• GraphX (graph)
 

Apache Spark™ is a fast and general engine for large-scale data processing.

Speed

 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

Ease of Use

 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

Generality

 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.

Runs Everywhere

 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.

Community

 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:

 
• Use the mailing lists to ask questions.
 
• In-person events include the Bay Area Spark meetup and  Spark Summit.
 
• We use JIRA for issue tracking.

Contributors

 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.

Getting Started

Learning Spark is easy whether you come from a Java or Python background:

 
• Download the latest release — you can run Spark locally on your laptop.
 
• Read the quick start guide.
 
•  Spark Summit 2014 contained free training videos and exercises.  
 
• Learn how to deploy Spark on a cluster.

Download Spark

//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

“Scala的文件读取、写入、控制台操作的方法示例”的内容就介绍到这里了，感谢大家的阅读。如果想了解更多行业相关的知识可以关注创新互联网站，小编将为大家输出更多高质量的实用文章！