原文链接:『 Spark 』1. spark 简介

写在前面

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。
Tips: 如果插图看起来不明显,可以:1. 放大网页;2. 新标签中打开图片,查看原图哦。

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。

参考文章

  • Intro to Apache Spark

  • introducing spark

本系列文章链接

  • 『 Spark 』1. spark 简介

  • 『 Spark 』2. spark 基本概念解析

  • 『 Spark 』3. spark 编程模式

  • 『 Spark 』4. spark 之 RDD

  • 『 Spark 』5. 这些年,你不能错过的 spark 学习资源

  • 『 Spark 』6. 深入研究 spark 运行原理之 job, stage, task

  • 『 Spark 』7. 使用 Spark DataFrame 进行大数据分析