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