『 Spark 』1. spark 简介




原文链接:『 Spark 』1. spark 简介


本系列是综合了自己在学习spark过程中的理解记录 + 对参考文章中的一些理解 + 个人实践spark过程中的一些心得而来。写这样一个系列仅仅是为了梳理个人学习spark的笔记记录,并非为了做什么教程,所以一切以个人理解梳理为主,没有必要的细节就不会记录了。若想深入了解,最好阅读参考文章和官方文档。

其次,本系列是基于目前最新的 spark 1.6.0 系列开始的,spark 目前的更新速度很快,记录一下版本好还是必要的。
最后,如果各位觉得内容有误,欢迎留言备注,所有留言 24 小时内必定回复,非常感谢。
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1. 如何向别人介绍 spark

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

Apache Spark is a fast and general-purpose cluster computing system.
It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs.
It also supports a rich set of higher-level tools including :

  • Spark SQL for SQL and structured data processing, extends to DataFrames and DataSets
  • MLlib for machine learning
  • GraphX for graph processing
  • Spark Streaming for stream data processing

2. spark 诞生的一些背景

Spark started in 2009, open sourced 2010, unlike the various specialized systems[hadoop, storm], Spark’s goal was to :

  • generalize MapReduce to support new apps within same engine

    • it’s perfectly compatible with hadoop, can run on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, and S3.
  • speed up iteration computing over hadoop.

    • use memory + disk instead of disk as data storage medium
    • design a new programming modal, RDD, which make the data processing more graceful [RDD transformation, action, distributed jobs, stages and tasks]

3. 为何选用 spark

  • designed, implemented and used as libs, instead of specialized systems;
    • much more useful and maintainable

  • from history, it is designed and improved upon hadoop and storm, it has perfect genes;
  • documents, community, products and trends;
  • it provides sql, dataframes, datasets, machine learning lib, graph computing lib and activitily growth 3-party lib, easy to use, cover lots of use cases in lots field;
  • it provides ad-hoc exploring, which boost your data exploring and pre-processing and help you build your data ETL, processing job;

4. Next

下一篇,简单介绍 spark 里必须深刻理解的基本概念。