『Big data technologies』关于各种大数据技术概念的简介（翻译自Quora）

原文

I'll try to give a very crude overview of how the pieces fit in together, because the details span multiple books. Please forgive me for some oversimplifications.

• MapReduce is the Google paper that started it all (Page on googleusercontent.com). It's a paradigm for writing distributed code inspired by some elements of functional programming. You don't have to do things this way, but it neatly fits a lot of problems we try to solve in a distributed way. The Google internal implementation is called MapReduce and Hadoop is it's open-source implementation. Amazon's Hadoop instance is called Elastic MapReduce (EMR) and has plugins for multiple languages.
• HDFS is an implementation inspired by the Google File System (GFS) to store files across a bunch of machines when it's too big for one. Hadoop consumes data in HDFS (Hadoop Distributed File System).
• Apache Spark is an emerging platform that has more flexibility than MapReduce but more structure than a basic message passing interface. It relies on the concept of distributed data structures (what it calls RDDs) and operators. See this page for more: The Apache Software Foundation
• Because Spark is a lower level thing that sits on top of a message passing interface, it has higher level libraries to make it more accessible to data scientists. The Machine Learning library built on top of it is called MLib and there's a distributed graph library called GraphX.
• Pregel and it's open source twin Giraph is a way to do graph algorithms on billions of nodes and trillions of edges over a cluster of machines. Notably, the MapReduce model is not well suited to graph processing so Hadoop/MapReduce are avoided in this model, but HDFS/GFS is still used as a data store.
• Zookeeper is a coordination and synchronization service that a distributed set of computer make decisions by consensus, handles failure, etc.
• Flume and Scribe are logging services, Flume is an Apache project and Scribe is an open-source Facebook project. Both aim to make it easy to collect tons of logged data, analyze it, tail it, move it around and store it to a distributed store.
• Google BigTable and it's open source twin HBase were meant to be read-write distributed databases, originally built for the Google Crawler that sit on top of GFS/HDFS and MapReduce/Hadoop. Google Research Publication: BigTable
• Hive and Pig are abstractions on top of Hadoop designed to help analysis of tabular data stored in a distributed file system (think of excel sheets too big to store on one machine). They operate on top of a data warehouse, so the high level idea is to dump data once and analyze it by reading and processing it instead of updating cells and rows and columns individually much. Hive has a language similar to SQL while Pig is inspired by Google's Sawzall - Google Research Publication: Sawzall. You generally don't update a single cell in a table when processing it with Hive or Pig.
• Hive and Pig turned out to be slow because they were built on Hadoop which optimizes for the volume of data moved around, not latency. To get around this, engineers bypassed and went straight to HDFS. They also threw in some memory and caching and this resulted in Google's Dremel (Dremel: Interactive Analysis of Web-Scale Datasets), F1 (F1 - The Fault-Tolerant Distributed RDBMS Supporting Google's Ad Business), Facebook's Presto (Presto | Distributed SQL Query Engine for Big Data), Apache Spark SQL (Page on apache.org ), Cloudera Impala (Cloudera Impala: Real-Time Queries in Apache Hadoop, For Real), Amazon's Redshift, etc. They all have slightly different semantics but are essentially meant to be programmer or analyst friendly abstractions to analyze tabular data stored in distributed data warehouses.
• Mahout (Scalable machine learning and data mining) is a collection of machine learning libraries written in the MapReduce paradigm, specifically for Hadoop. Google has it's own internal version but they haven't published a paper on it as far as I know.
• Oozie is a workflow scheduler. The oversimplified description would be that it's something that puts together a pipeline of the tools described above. For example, you can write an Oozie script that will scrape your production HBase data to a Hive warehouse nightly, then a Mahout script will train with this data. At the same time, you might use pig to pull in the test set into another file and when Mahout is done creating a model you can pass the testing data through the model and get results. You specify the dependency graph of these tasks through Oozie (I may be messing up terminology since I've never used Oozie but have used the Facebook equivalent).
• Lucene is a bunch of search-related and NLP tools but it's core feature is being a search index and retrieval system. It takes data from a store like HBase and indexes it for fast retrieval from a search query. Solr uses Lucene under the hood to provide a convenient REST API for indexing and searching data. ElasticSearch is similar to Solr.
• Sqoop is a command-line interface to back SQL data to a distributed warehouse. It's what you might use to snapshot and copy your database tables to a Hive warehouse every night.
• Hue is a web-based GUI to a subset of the above tools - http://gethue.com/

简要译文

注：由于我没有真正的使用过下面的这些技术，仍是处于学习阶段，因此各类翻译的内容会比较生硬且存在错误的地方，因此若是你的英文比较好，建议直接看上面的原文。

• MapReduce - 来自谷歌的一篇论文。是一种受到一些函数式编程元素所启发的编写分布式代码的范例。Google内部将它叫作MapReduce，且Hadoop是它的一种开源实现。Amazon的Hadoop实例叫作EMR（Elastic MapReduce）。
• HDFS - 是一种受到GFS(Google File System)启发的实现，用于在集群中存储文件。Hadoop使用HDFS用于存储。
• Apache Spark - 是一种比MapReduce更加适用的工程平台，相对于在传递时使用基础的消息，须要更多的结构。它依赖于分布式数据结构(RDDs)和操做的概念。点击查看更多。
• 由于Spark在顶层信息传递时使用更底层的方式，它对于数据科学有更高层的库来带来更好的可访问性。机器学习库构建在它的顶层叫MLib，而且有一个叫GraphX的分布式图形库。
• Pregel及它的开源实现Giraph 是一种处理十亿级节点和万亿级边界覆盖机器簇。值得注意的是MapReduce模型与图形处理不是很适配，因此Hadoop/MapReduce不适合这个模型，可是HDFS/GFS仍然用于数据存储。
• Zookeeper 是一种分布式计数机集的同步和异步服务。根据一致性、处理失败等来制定决策。
• Flume和Scribe是一种日志服务。Flume是一个Apache项目，Scribe是一个开源的Facebook项目。它们两个旨在让收集大量的日志数据，而后分析这些数据、跟踪这些数据、移动这些数据围绕/存储在分布式存储中。
• Google BigTable以及他的开源实现HBase，用于读/写分布式数据库，源于构建GoogleCrawler，处于GFS/HDFS和MapReduce/Hadoop的顶层。更多点击查看。
• Hive和Pig是在Hadoop的抽象，被设计用于分析存储于分布式文件系统的平滑数据（tabular data）。他们操做在一个数据仓库的顶层，因此高层级的思路是加载数据一次，而后经过读和处理来分析它，代替分别修改单元格、行、列。Hive有一种相似SQL的语言，Pig是受Google的Sawzall启发。当使用Hive或者Pig处理数据的时候，一般不用更新在表格中的单独的一个单元格。
• 由于构建在Hadoop（围绕优化处理数据容量）上，因此Hive和Pig会变得比较慢。基于这个缘由，工程师们绕过它，并直接面向HDFS。
• Mahout - 一种写在MapReduce范例中的机器学籍库的集合。
• Oozie - 一种工做流序列。简单的描述，它用于集成上面描述的各类工具的管道。例如，你能够写一个Oozie脚本，用来从HBase中提取数据到一个Hive数据仓库，而后使用一个Mahout脚原本使用这个数据完成‘训练’。同时，你可使用pig来测试集到另外一个文件，当Mahout已经建立一个模型，你能够传递测试数据到这个模型，而后获取结果。你经过Oozie指定这些任务的图形依赖。
• Lucene - 是一个搜索相关和NLP工具的集，它的核心功能是一个搜索索引以及一个检索系统。它从一个存储（如HBase）中取得数据，而后索引化它，以便更快的从一个搜索结果中检索数据。Solr使用Lucene在遮盖下来提供一种便利的REST API，来索引和搜索数据。ElasticSearch与Solr类似。
• Sqoop是一个命令行接口，来处理SQL数据到一个分布式仓库。它用于处理快照和复制你的数据表到一个Hive仓库中。
• Hue是一个基于Web的GUI，针对以上工具的一个子集。