hadoopdb 介绍
- 格式:pdf
- 大小:398.68 KB
- 文档页数:12
Hadoop概述⼀、Hadoop概述Hadoop实现了⼀个分布式⽂件系统,简称HDFS。
Hadoop在数据提取、变形和加载(ETL)⽅⾯有着天然的优势。
Hadoop的HDFS实现了⽂件的⼤批量存储,Hadoop的MapReduce功能实现了将单个任务打碎,将碎⽚任务(Map)发送到多个节点上,之后再以单个数据集的形式加载(Reduce)到数据仓库⾥。
Hadoop的ETL可批量操作数据,使处理结果直接⾛向存储。
Hadoop有以下特点:1、⾼可靠性。
因为它假设计算元素和存储会失败,因此它维护多个⼯作数据副本,能够确保针对失败的节点重新分布处理。
2、⾼扩展性。
Hadoop是在可⽤的计算机集簇间分配数据并完成计算任务的,这些集簇可⽅便的扩展到数以千计的节点中。
3、⾼效性。
它以并⾏的⽅式⼯作,能够在节点之间动态移动数据,并保证各个节点动态平衡,因此处理速度⾮常快。
4、⾼容错性。
Hadoop能够⾃动保存数据的多个副本,能够⾃动将失败的任务重新分配。
5、可伸缩性。
Hadoop能够处理PB级数据。
6、低成本。
Hadoop是开源的,项⽬软件成本⼤⼤降低。
Hadoop的组成:1、最底部的是HDFS(Hadoop Distribute File System),它存储Hadoop集群中所有存储节点上的⽂件,是数据存储的主要载体。
它由Namenode和DataNode组成。
2、HDFS的上⼀层是MapReduce引擎,该引擎由JobTrackers和TaskTrackers组成。
它通过MapReduce过程实现了对数据的处理。
3、Yarn实现了任务分配和集群资源管理的任务。
它由ResourceManager、nodeManager和ApplicationMaster组成。
Hadoop由以上三个部分组成,下⾯我们就这三个组成部分详细介绍:1、HDFSHadoop HDFS 的架构是基于⼀组特定的节点构建的,(1)名称节点(NameNode仅⼀个)负责管理⽂件系统名称空间和控制外部客户机的访问。
hadoop 原理Hadoop原理Hadoop是一个开源的分布式计算框架,它能够处理大规模数据集并且能够提供高可靠性、高可扩展性和高效率的计算能力。
本文将详细介绍Hadoop的原理。
一、Hadoop的概述1.1 Hadoop的定义Hadoop是一个基于Java语言编写的分布式计算框架,它由Apache 基金会开发和维护。
1.2 Hadoop的特点- 可以处理大规模数据集- 具有高可靠性、高可扩展性和高效率- 支持多种数据存储方式- 支持多种计算模型和编程语言- 易于部署和管理1.3 Hadoop的组件Hadoop由以下几个组件组成:- HDFS(Hadoop Distributed File System):分布式文件系统,用于存储大规模数据集。
- MapReduce:分布式计算框架,用于对大规模数据进行并行处理。
- YARN(Yet Another Resource Negotiator):资源管理器,用于协调整个集群中各个应用程序之间的资源使用。
二、HDFS原理2.1 HDFS概述HDFS是一个分布式文件系统,它可以在集群中存储大规模数据集。
它采用了主从架构,其中NameNode作为主节点,负责管理整个文件系统的元数据,而DataNode作为从节点,负责存储数据块。
2.2 HDFS文件存储原理HDFS将一个文件分成多个数据块进行存储。
每个数据块的大小默认为128MB,可以通过配置进行修改。
当一个文件被上传到HDFS中时,它会被分成多个数据块,并且这些数据块会被复制到不同的DataNode上进行备份。
2.3 HDFS读写原理当客户端需要读取一个文件时,它会向NameNode发送请求。
NameNode返回包含该文件所在DataNode信息的列表给客户端。
客户端根据这些信息直接与DataNode通信获取所需的数据。
当客户端需要上传一个文件时,它会向NameNode发送请求,并且将该文件分成多个数据块进行上传。
大数据存储方式概述标题:大数据存储方式概述引言概述:随着信息技术的不断发展,大数据已经成为当今社会中一个重要的信息资源。
为了有效管理和利用大数据,各种存储方式应运而生。
本文将就大数据存储方式进行概述,帮助读者更好地了解大数据存储的相关知识。
一、分布式文件系统存储方式1.1 HDFS(Hadoop分布式文件系统):HDFS是Apache Hadoop项目中的一个分布式文件系统,适用于存储大规模数据,并且具有高可靠性和高扩展性。
1.2 GFS(Google文件系统):GFS是Google开发的分布式文件系统,采用主从架构,能够有效地处理大规模数据的存储和访问。
1.3 Ceph:Ceph是一个开源的分布式存储系统,具有高可用性和高性能,支持对象存储、块存储和文件系统存储。
二、NoSQL数据库存储方式2.1 MongoDB:MongoDB是一种面向文档的NoSQL数据库,适用于存储半结构化数据,并且具有高性能和可扩展性。
2.2 Cassandra:Cassandra是一个高度可扩展的NoSQL数据库,适用于分布式存储大规模数据,并且支持高可用性和容错性。
2.3 Redis:Redis是一个开源的内存数据库,适用于缓存和实时数据处理,具有快速的读写速度和高性能。
三、列式数据库存储方式3.1 HBase:HBase是一个基于Hadoop的列式数据库,适用于存储大规模结构化数据,并且支持高可用性和高性能。
3.2 Vertica:Vertica是一种高性能列式数据库,适用于数据仓库和实时分析,具有快速的查询速度和高压缩比。
3.3 ClickHouse:ClickHouse是一个开源的列式数据库,适用于实时分析和数据仓库,具有高性能和可扩展性。
四、云存储方式4.1 AWS S3(Amazon Simple Storage Service):AWS S3是亚马逊提供的云存储服务,适用于存储大规模数据,并且具有高可靠性和安全性。
Hadoop 生态系统介绍Hadoop生态系统是一个开源的大数据处理平台,它由Apache基金会支持和维护,可以在大规模的数据集上实现分布式存储和处理。
Hadoop生态系统是由多个组件和工具构成的,包括Hadoop 核心,Hive、HBase、Pig、Spark等。
接下来,我们将对每个组件及其作用进行介绍。
一、Hadoop核心Hadoop核心是整个Hadoop生态系统的核心组件,它主要由两部分组成,一个是Hadoop分布式文件系统(HDFS),另一个是MapReduce编程模型。
HDFS是一个高可扩展性的分布式文件系统,可以将海量数据存储在数千台计算机上,实现数据的分散储存和高效访问。
MapReduce编程模型是基于Hadoop的针对大数据处理的一种模型,它能够对海量数据进行分布式处理,使大规模数据分析变得容易和快速。
二、HiveHive是一个开源的数据仓库系统,它使用Hadoop作为其计算和存储平台,提供了类似于SQL的查询语法,可以通过HiveQL 来查询和分析大规模的结构化数据。
Hive支持多种数据源,如文本、序列化文件等,同时也可以将结果导出到HDFS或本地文件系统。
三、HBaseHBase是一个开源的基于Hadoop的列式分布式数据库系统,它可以处理海量的非结构化数据,同时也具有高可用性和高性能的特性。
HBase的特点是可以支持快速的数据存储和检索,同时也支持分布式计算模型,提供了易于使用的API。
四、PigPig是一个基于Hadoop的大数据分析平台,提供了一种简单易用的数据分析语言(Pig Latin语言),通过Pig可以进行数据的清洗、管理和处理。
Pig将数据处理分为两个阶段:第一阶段使用Pig Latin语言将数据转换成中间数据,第二阶段使用集合行处理中间数据。
五、SparkSpark是一个快速、通用的大数据处理引擎,可以处理大规模的数据,支持SQL查询、流式数据处理、机器学习等多种数据处理方式。
hadoop的生态体系及各组件的用途
Hadoop是一个生态体系,包括许多组件,以下是其核心组件和用途:
1. Hadoop Distributed File System (HDFS):这是Hadoop的分布式文件系统,用于存储大规模数据集。
它设计为高可靠性和高吞吐量,并能在低成本的通用硬件上运行。
通过流式数据访问,它提供高吞吐量应用程序数据访问功能,适合带有大型数据集的应用程序。
2. MapReduce:这是Hadoop的分布式计算框架,用于并行处理和分析大规模数据集。
MapReduce模型将数据处理任务分解为Map和Reduce两个阶段,从而在大量计算机组成的分布式并行环境中有效地处理数据。
3. YARN:这是Hadoop的资源管理和作业调度系统。
它负责管理集群资源、调度任务和监控应用程序。
4. Hive:这是一个基于Hadoop的数据仓库工具,提供SQL-like查询语言和数据仓库功能。
5. Kafka:这是一个高吞吐量的分布式消息队列系统,用于实时数据流的收集和传输。
6. Pig:这是一个用于大规模数据集的数据分析平台,提供类似SQL的查询语言和数据转换功能。
7. Ambari:这是一个Hadoop集群管理和监控工具,提供可视化界面和集群配置管理。
此外,HBase是一个分布式列存数据库,可以与Hadoop配合使用。
HBase 中保存的数据可以使用MapReduce来处理,它将数据存储和并行计算完美地结合在一起。
Hadoop大数据技术基础 python版随着互联网技术的不断发展和数据量的爆炸式增长,大数据技术成为了当前互联网行业的热门话题之一。
Hadoop作为一种开源的大数据处理评台,其在大数据领域的应用日益广泛。
而Python作为一种简洁、易读、易学的编程语言,也在大数据分析与处理中扮演着不可或缺的角色。
本文将介绍Hadoop大数据技术的基础知识,并结合Python编程语言,分析其在大数据处理中的应用。
一、Hadoop大数据技术基础1. Hadoop简介Hadoop是一种用于存储和处理大规模数据的开源框架,它主要包括Hadoop分布式文件系统(HDFS)和MapReduce计算框架。
Hadoop分布式文件系统用于存储大规模数据,而MapReduce计算框架则用于分布式数据处理。
2. Hadoop生态系统除了HDFS和MapReduce之外,Hadoop生态系统还包括了许多其他组件,例如HBase、Hive、Pig、ZooKeeper等。
这些组件形成了一个完整的大数据处理评台,能够满足各种不同的大数据处理需求。
3. Hadoop集群Hadoop通过在多台服务器上构建集群来实现数据的存储和处理。
集群中的各个计算节点共同参与数据的存储和计算,从而实现了大规模数据的分布式处理。
二、Python在Hadoop大数据处理中的应用1. Hadoop StreamingHadoop Streaming是Hadoop提供的一个用于在MapReduce中使用任意编程语言的工具。
通过Hadoop Streaming,用户可以借助Python编写Map和Reduce的程序,从而实现对大规模数据的处理和分析。
2. Hadoop连接Python除了Hadoop Streaming外,Python还可以通过Hadoop提供的第三方库和接口来连接Hadoop集群,实现对Hadoop集群中数据的读取、存储和计算。
这为Python程序员在大数据处理领域提供了更多的可能性。
1。
技术实现框架1.1大数据平台架构1.1.1大数据库是未来提升业务能力的关键要素以“大数据”为主导的新一波信息化浪潮正席卷全球,成为全球范围内加速企业技术创新、推动政府职能转变、引领社会管理变革的利器。
目前,大数据技术已经从技术研究步入落地实施阶段,数据资源成为未来业务的关键因素。
通过采集和分析数据,我们可以获知事物背后的原因,优化生产/生活方式,预知未来的发展动态。
经过多年的信息化建设,省地税已经积累了丰富的数据资源,为下一步的优化业务、提升管理水平,奠定了坚实的基础.未来的数据和业务应用趋势,大数据才能解决这些问题。
《1.巨杉软件SequoiaDB产品和案例介绍v2》P12 “银行的大数据资产和应用“,说明税务数据和业务分析,需要用大数据解决。
《1。
巨杉软件SequoiaDB产品和案例介绍v2》P14 “大数据与传统数据处理",说明处理模式的差异。
1.1.2大数据平台总体框架大数据平台总体技术框架分为数据源层、数据接口层、平台架构层、分析工具层和业务应用层.如下图所示:(此图要修改,北明)数据源层:包括各业务系统、服务系统以及社会其它单位的结构化数据和非结构化数据;数据接口层:是原始数据进入大数据库的入口,针对不同类型的数据,需要有针对性地开发接口,进行数据的缓冲、预处理等操作;平台架构层:基于大数据系统存储各类数据,进行处理?;分析工具层:提供各种数据分析工具,例如:建模工具、报表开发、数据分析、数据挖掘、可视化展现等工具;业务应用层:根据应用领域和业务需求,建立分析模型,使用分析工具,发现获知事物背后的原因,预知未来的发展趋势,提出优化业务的方法。
例如,寻找服务资源的最佳配置方案、发现业务流程中的短板进行优化等。
1.1.3大数据平台产品选型针对业务需求,我们选择巨杉数据库作为大数据基础平台.1.1.3.1传统数据库与大数据库的差异(丰富一下内容,说明应该选择大数据平台)传统的关系型数据库,只能存储结构化数据,在当前互联网快速发展的时代,僵硬的数据模型已经无法适应快速开发、快速迭代的互联网思维。
海量数据处理技术——Hadoop介绍如今,在数字化时代,数据已经成为企业和组织中最重要的资产之一,因为巨大量的数据给企业和组织带来了更多的挑战,比如如何存储、管理和分析数据。
随着数据越来越庞大,传统方法已经无法胜任。
这正是Hadoop出现的原因——Hadoop是一个开源的、可扩展的海量数据处理工具。
本文将介绍什么是Hadoop、它的架构和基本概念、以及使用的应用场景。
一、什么是HadoopHadoop是一种基于Java的开源框架,它可以将大量数据分布式分割存储在许多不同的服务器中,并能够对这些数据进行处理。
Hadoop最初是由Apache软件基金会开发的,旨在解决海量数据存储和处理的难题。
Hadoop采用了一种分布式存储和处理模式,能够高效地处理PB级别甚至EB级别的数据,使得企业和组织能够在这些大量数据中更快地发现价值,并利用它带来的价值。
二、 Hadoop架构和基本概念Hadoop架构由两个核心组成部分构成:分布式文件系统Hadoop Distributed File System(HDFS)和MapReduce的执行框架。
1. HDFSHDFS以可扩展性为前提,其存储处理是在上面构建的,它在集群内将数据分成块(Block),每个块的大小通常为64MB或128MB,然后将这些块存储在相应的数据节点上。
HDFS架构包含两类节点:一个是namenode,另一个是datanode。
namenode是文件系统的管理节点,负责存储所有文件和块的元数据,这些元数据不包括实际数据本身。
datanode是存储节点,负责存储实际的数据块,并向namenode报告其状态。
2. MapReduceMapReduce是一个处理数据的编程模型,它基于两个核心操作:map和reduce。
Map负责将输入数据划分为一些独立的小片段,再把每个小片段映射为一个元组作为输出。
Reduce将Map输出的元组进行合并和过滤,生成最终输出。
Hadoop⽣态圈各个组件简介Hadoop是⼀个能够对⼤量数据进⾏分布式处理的软件框架。
具有可靠、⾼效、可伸缩的特点。
Hadoop的核⼼是HDFS和MapReduce,HDFS还包括YARN。
1.HDFS(hadoop分布式⽂件系统)是hadoop体系中数据存储管理的他是⼀个基础。
它是⼀个⾼度容错的的系统,能检测和应对硬件故障。
client:切分⽂件,访问HDFS,与之交互,获取⽂件位置信息,与DataNode交互,读取和写⼊数据。
namenode:master节点,在hadoop1.x中只有⼀个,管理HDFS的名称空间和数据块映射信息,配置副本策略,处理客户端请求。
DataNode:slave节点,存储实际的数据,汇报存储信息给namenode.secondary namenode:辅助namenode,分担其⼯作量:定期合并fsimage和fsedits,推送给namenode;紧急情况下和辅助恢复namenode,但其并⾮namenode的热备。
2.mapreduce(分布式计算框架)mapreduce是⼀种计算模型,⽤于处理⼤数据量的计算。
其中map对应数据集上的独⽴元素进⾏指定的操作,⽣成键-值对形式中间,reduce则对中间结果中相同的键的所有的值进⾏规约,以得到最终结果。
jobtracker:master节点,只有⼀个管理所有作业,任务/作业的监控,错误处理等,将任务分解成⼀系列任务,并分派给tasktracker. tacktracker:slave节点,运⾏map task和reducetask;并与jobtracker交互,汇报任务状态。
map task:解析每条数据记录,传递给⽤户编写的map()执⾏,将输出结果写⼊到本地磁盘(如果为map-only作业,则直接写⼊HDFS)。
reduce task:从map的执⾏结果中,远程读取输⼊数据,对数据进⾏排序,将数据分组传递给⽤户编写的reduce函数执⾏。
简述hadoop核心组件及功能应用Hadoop是一个开源的分布式计算系统,由Apache组织维护。
它可以处理大量的数据,支持数据的存储、处理和分析。
其核心组件包括HDFS(Hadoop分布式文件系统)、MapReduce计算框架、YARN(资源管理)。
以下是对每个核心组件的简要介绍:1. HDFSHDFS是Hadoop分布式文件系统,它是Hadoop最核心的组件之一。
HDFS是为大数据而设计的分布式文件系统,它可以存储大量的数据,支持高可靠性和高可扩展性。
HDFS的核心目标是以分布式方式存储海量数据,并为此提供高可靠性、高性能、高可扩展性和高容错性。
2. MapReduce计算框架MapReduce是Hadoop中的一种计算框架,它支持分布式计算,是Hadoop的核心技术之一。
MapReduce处理海量数据的方式是将数据拆分成小块,然后在多个计算节点上并行运行Map和Reduce任务,最终通过Shuffle将结果合并。
MapReduce框架大大降低了海量数据处理的难度,让分布式计算在商业应用中得以大规模应用。
3. YARNYARN是Hadoop 2.x引入的新一代资源管理器,它的作用是管理Hadoop集群中的资源。
它支持多种应用程序的并行执行,包括MapReduce和非MapReduce应用程序。
YARN的目标是提供一个灵活、高效和可扩展的资源管理器,以支持各种不同类型的应用程序。
除了以上三个核心组件,Hadoop还有其他一些重要组件和工具,例如Hive(数据仓库)、Pig(数据分析)、HBase(NoSQL数据库)等。
这些组件和工具都是Hadoop生态系统中的重要组成部分,可以帮助用户更方便地处理大数据。
总之,Hadoop是目前最流行的大数据处理框架之一,它的核心组件和工具都为用户提供了丰富的数据处理和分析功能。
hadoop原理及组件Hadoop是一个开源的分布式计算框架,旨在处理大规模数据集。
它提供了一个可靠、高效和可扩展的基础设施,用于存储、处理和分析数据。
本篇文章将详细介绍Hadoop的原理以及其核心组件。
一、Hadoop原理Hadoop的核心原理包括数据分布式存储、数据切分、数据复制和数据计算等。
首先,Hadoop使用HDFS(分布式文件系统)进行数据存储,支持大规模数据的存储和读取。
其次,Hadoop采用了MapReduce 模型对数据进行分布式计算,通过将数据切分为小块进行处理,从而实现高效的计算。
此外,Hadoop还提供了Hive、HBase等组件,以支持数据查询和分析等功能。
二、Hadoop核心组件1.HDFS(Hadoop分布式文件系统)HDFS是Hadoop的核心组件之一,用于存储和读取大规模数据。
它支持多节点集群,能够提供高可用性和数据可靠性。
在HDFS中,数据被分成块并存储在多个节点上,提高了数据的可靠性和可用性。
2.MapReduceMapReduce是Hadoop的另一个核心组件,用于处理大规模数据集。
它采用分而治之的策略,将数据集切分为小块,并分配给集群中的多个节点进行处理。
Map阶段将数据集分解为键值对,Reduce阶段则对键值对进行聚合和处理。
通过MapReduce模型,Hadoop能够实现高效的分布式计算。
3.YARN(资源调度器)YARN是Hadoop的另一个核心组件,用于管理和调度集群资源。
它提供了一个统一的资源管理框架,能够支持多种应用类型(如MapReduce、Spark等)。
YARN通过将资源分配和管理与应用程序解耦,实现了资源的灵活性和可扩展性。
4.HBaseHBase是Hadoop中的一个列式存储系统,用于大规模结构化数据的存储和分析。
它采用分布式架构,支持高并发读写和低延迟查询。
HBase与HDFS紧密集成,能够快速检索和分析大规模数据集。
5.Pig和HivePig和Hive是Hadoop生态系统中的两个重要组件,分别用于数据管道化和数据仓库的构建和管理。
大数据常用数据库汇总随着互联网的快速发展,大数据已经成为了当下炙手可热的话题。
大数据的处理和分析对于企业和组织来说至关重要,它们需要一种高效的数据库来存储和管理海量的数据。
本文将介绍一些常用的大数据数据库,帮助读者了解并选择适合自己需求的数据库。
一、HadoopHadoop 是由Apache基金会开发的一款开源分布式数据处理框架。
它是目前最流行的大数据处理平台之一。
Hadoop 可以将大规模数据分散存储在集群中的多个节点上,实现数据的高可靠性和高可扩展性。
同时,Hadoop 还提供了一个分布式文件系统(HDFS)作为数据存储解决方案。
二、CassandraCassandra 是一款开源的分布式数据库,最初由Facebook开发并开源。
Cassandra 具有高度可扩展性和高容错性,可以在大规模分布式系统中处理大量的数据。
它采用分布式的存储方式,数据可以根据预定义的复制因子进行复制,以实现容错和高可用性。
三、MongoDBMongoDB 是一款开源的文档数据库,旨在简化开发人员的数据存储和查询体验。
它采用了 NoSQL 的思想,数据以 JSON 格式存储,具有灵活的数据模型和强大的查询能力。
MongoDB 可以在分布式环境中部署,提供高可用性和扩展性。
四、HBaseHBase 是 Apache Hadoop 生态系统中的一个分布式列存数据库,它是在 HDFS 上构建的。
HBase 是以 Google 的 Bigtable 为原型设计的,可以在大规模分布式系统中存储和管理海量的结构化数据。
它具有高扩展性和高可靠性,并且可以实现快速的数据读写操作。
五、Spark SQLSpark SQL 是 Apache Spark 生态系统中的一个模块,提供了结构化数据处理和分析的功能。
它支持 SQL 查询和 DataFrame API,可以通过 Spark 的机器学习和图处理功能来进行高级分析。
Spark SQL 可以读取和写入各种数据源,包括关系型数据库、Parquet、Hive等。
hadoop介绍讲解Hadoop是一个由Apache软件基金会开发的开源分布式系统。
它的目标是处理大规模数据集。
Hadoop可以更好地利用一组连接的计算机和硬件来存储和处理海量数据集。
Hadoop主要由Hadoop分布式文件系统(HDFS)和MapReduce两部分组成。
以下是hadoop的详细介绍。
1. Hadoop分布式文件系统(HDFS)HDFS是Hadoop的分布式文件系统。
HDFS将大量数据分成小块并在多个机器上进行存储,从而使数据更容易地管理和处理。
HDFS适合在大规模集群上存储和处理数据。
它被设计为高可靠性,高可用性,并且容错性强。
2. MapReduceMapReduce是Hadoop中的计算框架。
它分为两个阶段:Map和Reduce。
Map阶段将数据分为不同的片段,并将这些片段映射到不同的机器上进行并行处理,Reduce阶段将结果从Map阶段中得到,并将其组合在一起生成最终的结果。
MapReduce框架根据数据的并行处理进行拆分,而输出结果则由Reduce阶段组装而成。
3. Hadoop生态系统Hadoop是一个开放的生态系统,其包含了许多与其相关的项目。
这些项目包括Hive,Pig,Spark等等。
Hive是一个SQL on Hadoop工具,用于将SQL语句转换为MapReduce作业。
Pig是另一个SQL on Hadoop工具,它是一个基于Pig Latin脚本语言的高级并行运算系统,可以用于处理大量数据。
Spark是一个快速通用的大数据处理引擎,它减少了MapReduce 的延迟并提供了更高的数据处理效率。
4. Hadoop的优点Hadoop是一个灵活的、可扩展的与成本优势的平台,它可以高效地处理大规模的数据集。
同时,它的开放式和Modular的体系结构使得其在大数据环境下无论是对数据的处理还是与其他开发者的协作都非常便利。
5. 总结Hadoop是一个很好的大数据处理工具,并且在行业中得到了广泛的应用。
Hadoop基础入门指南Hadoop是一个基于Java的开源分布式计算平台,能够处理大规模数据存储和处理任务。
它是处理大数据的一种解决方案,被广泛应用于各种领域,例如金融、医疗、社交媒体等。
本文将介绍Hadoop的基础知识,帮助初学者快速入门。
一、Hadoop的三大模块Hadoop有三个核心模块,分别是HDFS(Hadoop分布式文件系统)、MapReduce、和YARN。
1. HDFS(Hadoop分布式文件系统)HDFS是Hadoop的存储模块,它可以存储大量的数据,并在多台机器之间进行分布式存储和数据备份。
HDFS将文件切割成固定大小的块,并复制多份副本,存储在不同的服务器上。
如果某个服务器宕机,数据仍然可以从其他服务器中获取,保障数据的安全。
2. MapReduceMapReduce是Hadoop的计算模块,它可以对存储在HDFS上的大量数据进行分布式处理。
MapReduce模型将大数据集划分成小数据块,并行处理这些小数据块,最后将结果归并。
MapReduce模型包含两个阶段:Map阶段和Reduce阶段。
Map阶段:将输入的大数据集划分成小数据块,并将每个数据块分配给不同的Map任务处理。
每个Map任务对数据块进行处理,并生成键值对,输出给Reduce任务。
Reduce阶段:对每个键值对进行归并排序,并将具有相同键的一组值传递给Reduce任务,进行汇总和计算。
3. YARNYARN是Hadoop的资源管理器,它负责分配和管理Hadoop集群中的计算资源。
YARN包含两个关键组件:ResourceManager和NodeManager。
ResourceManager:管理整个集群的资源,包括内存、CPU等。
NodeManager:运行在每个计算节点上,负责监控本地计算资源使用情况,并与ResourceManager通信以请求或释放资源。
二、Hadoop的安装与配置在开始使用Hadoop之前,需要进行安装和配置。
tablejdbcupsertoutputformat -回复使用Table JDBC Upsert Output Format进行数据操作在大数据处理过程中,对于数据的增加、修改和删除操作是非常常见的。
常规的方式是通过JDBC连接数据库进行操作,但在处理大规模数据时,传统的JDBC操作方式会导致性能瓶颈,因此,Hadoop生态系统提供了一种更加高效的数据操作方式,即使用Table JDBC Upsert Output Format。
Table JDBC Upsert Output Format是Hadoop生态系统中一种数据输出格式,它基于JDBC连接,可以实现对数据库的增、删、改操作。
相比传统的JDBC连接,Table JDBC Upsert Output Format能够充分利用Hadoop集群的分布式计算能力,提高操作性能,并且能够处理海量数据。
下面将一步一步介绍如何使用Table JDBC Upsert Output Format进行数据操作。
步骤一:配置数据库连接首先,需要在Hadoop集群的配置文件中进行数据库连接的配置。
通过修改hadoop-conf目录下的core-site.xml文件,添加如下配置信息:<property><name>hive.metastore.uris</name><value>jdbc:mysql:localhost:3306/mydb</value></property>其中,hive.metastore.uris指定了数据库的连接地址,localhost:3306表示数据库的主机和端口,mydb表示可以访问的数据库名称。
步骤二:创建Table JDBC Upsert Output Format创建Table JDBC Upsert Output Format的过程分为两个步骤:1. 创建输出表的schema通过使用Hive的serde库,可以创建一个与数据库中表结构相对应的schema。
HadoopDB:An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical WorkloadsAzza Abouzeid1,Kamil Bajda-Pawlikowski1,Daniel Abadi1,Avi Silberschatz1,Alexander Rasin21Y ale University,2Brown University{azza,kbajda,dna,avi}@;alexr@ABSTRACTThe production environment for analytical data management ap-plications is rapidly changing.Many enterprises are shifting away from deploying their analytical databases on high-end proprietary machines,and moving towards cheaper,lower-end,commodity hardware,typically arranged in a shared-nothing MPP architecture, often in a virtualized environment inside public or private“clouds”. At the same time,the amount of data that needs to be analyzed is exploding,requiring hundreds to thousands of machines to work in parallel to perform the analysis.There tend to be two schools of thought regarding what tech-nology to use for data analysis in such an environment.Propo-nents of parallel databases argue that the strong emphasis on per-formance and efficiency of parallel databases makes them well-suited to perform such analysis.On the other hand,others argue that MapReduce-based systems are better suited due to their supe-rior scalability,fault tolerance,andflexibility to handle unstructured data.In this paper,we explore the feasibility of building a hybrid system that takes the best features from both technologies;the pro-totype we built approaches parallel databases in performance and efficiency,yet still yields the scalability,fault tolerance,andflexi-bility of MapReduce-based systems.1.INTRODUCTIONThe analytical database market currently consists of$3.98bil-lion[25]of the$14.6billion database software market[21](27%) and is growing at a rate of10.3%annually[25].As business“best-practices”trend increasingly towards basing decisions off data and hard facts rather than instinct and theory,the corporate thirst for systems that can manage,process,and granularly analyze data is becoming insatiable.Venture capitalists are very much aware of this trend,and have funded no fewer than a dozen new companies in recent years that build specialized analytical data management soft-ware(e.g.,Netezza,Vertica,DATAllegro,Greenplum,Aster Data, Infobright,Kickfire,Dataupia,ParAccel,and Exasol),and continue to fund them,even in pressing economic times[18].At the same time,the amount of data that needs to be stored and processed by analytical database systems is exploding.This is Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment.To copy otherwise,or to republish,to post on servers or to redistribute to lists,requires a fee and/or special permission from the publisher,ACM.VLDB‘09,August24-28,2009,Lyon,FranceCopyright2009VLDB Endowment,ACM000-0-00000-000-0/00/00.partly due to the increased automation with which data can be pro-duced(more business processes are becoming digitized),the prolif-eration of sensors and data-producing devices,Web-scale interac-tions with customers,and government compliance demands along with strategic corporate initiatives requiring more historical data to be kept online for analysis.It is no longer uncommon to hear of companies claiming to load more than a terabyte of structured data per day into their analytical database system and claiming data warehouses of size more than a petabyte[19].Given the exploding data problem,all but three of the above mentioned analytical database start-ups deploy their DBMS on a shared-nothing architecture(a collection of independent,possibly virtual,machines,each with local disk and local main memory, connected together on a high-speed network).This architecture is widely believed to scale the best[17],especially if one takes hardware cost into account.Furthermore,data analysis workloads tend to consist of many large scan operations,multidimensional ag-gregations,and star schema joins,all of which are fairly easy to parallelize across nodes in a shared-nothing network.Analytical DBMS vendor leader,Teradata,uses a shared-nothing architecture. Oracle and Microsoft have recently announced shared-nothing an-alytical DBMS products in their Exadata1and Madison projects, respectively.For the purposes of this paper,we will call analytical DBMS systems that deploy on a shared-nothing architecture paral-lel databases2.Parallel databases have been proven to scale really well into the tens of nodes(near linear scalability is not uncommon).However, there are very few known parallel databases deployments consisting of more than one hundred nodes,and to the best of our knowledge, there exists no published deployment of a parallel database with nodes numbering into the thousands.There are a variety of reasons why parallel databases generally do not scale well into the hundreds of nodes.First,failures become increasingly common as one adds more nodes to a system,yet parallel databases tend to be designed with the assumption that failures are a rare event.Second,parallel databases generally assume a homogeneous array of machines,yet it is nearly impossible to achieve pure homogeneity at scale.Third, until recently,there have only been a handful of applications that re-quired deployment on more than a few dozen nodes for reasonable performance,so parallel databases have not been tested at larger scales,and unforeseen engineering hurdles await.As the data that needs to be analyzed continues to grow,the num-ber of applications that require more than one hundred nodes is be-ginning to multiply.Some argue that MapReduce-based systems 1To be precise,Exadata is only shared-nothing in the storage layer. 2This is slightly different than textbook definitions of parallel databases which sometimes include shared-memory and shared-disk architectures as well.[8]are best suited for performing analysis at this scale since they were designed from the beginning to scale to thousands of nodes in a shared-nothing architecture,and have had proven success in Google’s internal operations and on the TeraSort benchmark[7]. Despite being originally designed for a largely different application (unstructured text data processing),MapReduce(or one of its pub-licly available incarnations such as open source Hadoop[1])can nonetheless be used to process structured data,and can do so at tremendous scale.For example,Hadoop is being used to manage Facebook’s2.5petabyte data warehouse[20].Unfortunately,as pointed out by DeWitt and Stonebraker[9], MapReduce lacks many of the features that have proven invaluable for structured data analysis workloads(largely due to the fact that MapReduce was not originally designed to perform structured data analysis),and its immediate gratification paradigm precludes some of the long term benefits offirst modeling and loading data before processing.These shortcomings can cause an order of magnitude slower performance than parallel databases[23].Ideally,the scalability advantages of MapReduce could be com-bined with the performance and efficiency advantages of parallel databases to achieve a hybrid system that is well suited for the an-alytical DBMS market and can handle the future demands of data intensive applications.In this paper,we describe our implementa-tion of and experience with HadoopDB,whose goal is to serve as exactly such a hybrid system.The basic idea behind HadoopDB is to use MapReduce as the communication layer above multiple nodes running single-node DBMS instances.Queries are expressed in SQL,translated into MapReduce by extending existing tools,and as much work as possible is pushed into the higher performing sin-gle node databases.One of the advantages of MapReduce relative to parallel databases not mentioned above is cost.There exists an open source version of MapReduce(Hadoop)that can be obtained and used without cost.Yet all of the parallel databases mentioned above have a nontrivial cost,often coming with sevenfigure price tags for large installations.Since it is our goal to combine all of the advantages of both data analysis approaches in our hybrid system, we decided to build our prototype completely out of open source components in order to achieve the cost advantage as well.Hence, we use PostgreSQL as the database layer and Hadoop as the communication layer,Hive as the translation layer,and all code we add we release as open source[2].One side effect of such a design is a shared-nothing version of PostgreSQL.We are optimistic that our approach has the potential to help transform any single-node DBMS into a shared-nothing par-allel database.Given our focus on cheap,large scale data analysis,our tar-get platform is virtualized public or private“cloud computing”deployments,such as Amazon’s Elastic Compute Cloud(EC2) or VMware’s private VDC-OS offering.Such deployments significantly reduce up-front capital costs,in addition to lowering operational,facilities,and hardware costs(through maximizing current hardware utilization).Public cloud offerings such as EC2 also yield tremendous economies of scale[14],and pass on some of these savings to the customer.All experiments we run in this paper are on Amazon’s EC2cloud offering;however our techniques are applicable to non-virtualized cluster computing grid deployments as well.In summary,the primary contributions of our work include:•We extend previous work[23]that showed the superior per-formance of parallel databases relative to Hadoop.While this previous work focused only on performance in an ideal set-ting,we add fault tolerance and heterogeneous node experi-ments to demonstrate some of the issues with scaling parallel databases.•We describe the design of a hybrid system that is designed to yield the advantages of both parallel databases and MapRe-duce.This system can also be used to allow single-node databases to run in a shared-nothing environment.•We evaluate this hybrid system on a previously published benchmark to determine how close it comes to parallel DBMSs in performance and Hadoop in scalability.2.RELATED WORKThere has been some recent work on bringing together ideas from MapReduce and database systems;however,this work focuses mainly on language and interface issues.The Pig project at Yahoo [22],the SCOPE project at Microsoft[6],and the open source Hive project[11]aim to integrate declarative query constructs from the database community into MapReduce-like software to allow greater data independence,code reusability,and automatic query optimiza-tion.Greenplum and Aster Data have added the ability to write MapReduce functions(instead of,or in addition to,SQL)over data stored in their parallel database products[16].Although thesefive projects are without question an important step in the hybrid direction,there remains a need for a hybrid solu-tion at the systems level in addition to at the language and interface levels.This paper focuses on such a systems-level hybrid.3.DESIRED PROPERTIESIn this section we describe the desired properties of a system de-signed for performing data analysis at the(soon to be more com-mon)petabyte scale.In the following section,we discuss how par-allel database systems and MapReduce-based systems do not meet some subset of these desired properties.Performance.Performance is the primary characteristic that com-mercial database systems use to distinguish themselves from other solutions,with marketing literature oftenfilled with claims that a particular solution is many times faster than the competition.A factor of ten can make a big difference in the amount,quality,and depth of analysis a system can do.High performance systems can also sometimes result in cost sav-ings.Upgrading to a faster software product can allow a corporation to delay a costly hardware upgrade,or avoid buying additional com-pute nodes as an application continues to scale.On public cloud computing platforms,pricing is structured in a way such that one pays only for what one uses,so the vendor price increases linearly with the requisite storage,network bandwidth,and compute power. Hence,if data analysis software product A requires an order of mag-nitude more compute units than data analysis software product B to perform the same task,then product A will cost(approximately) an order of magnitude more than B.Efficient software has a direct effect on the bottom line.Fault Tolerance.Fault tolerance in the context of analytical data workloads is measured differently than fault tolerance in the con-text of transactional workloads.For transactional workloads,a fault tolerant DBMS can recover from a failure without losing any data or updates from recently committed transactions,and in the con-text of distributed databases,can successfully commit transactions and make progress on a workload even in the face of worker node failures.For read-only queries in analytical workloads,there are neither write transactions to commit,nor updates to lose upon node failure.Hence,a fault tolerant analytical DBMS is simply one thatdoes not have to restart a query if one of the nodes involved in query processing fails.Given the proven operational benefits and resource consumption savings of using cheap,unreliable commodity hardware to build a shared-nothing cluster of machines,and the trend towards extremely low-end hardware in data centers[14],the probability of a node failure occurring during query processing is increasing rapidly.This problem only gets worse at scale:the larger the amount of data that needs to be accessed for analytical queries,the more nodes are required to participate in query processing.This further increases the probability of at least one node failing during query execution.Google,for example,reports an average of1.2 failures per analysis job[8].If a query must restart each time a node fails,then long,complex queries are difficult to complete. Ability to run in a heterogeneous environment.As described above,there is a strong trend towards increasing the number of nodes that participate in query execution.It is nearly impossible to get homogeneous performance across hundreds or thousands of compute nodes,even if each node runs on identical hardware or on an identical virtual machine.Part failures that do not cause com-plete node failure,but result in degraded hardware performance be-come more common at scale.Individual node disk fragmentation and software configuration errors can also cause degraded perfor-mance on some nodes.Concurrent queries(or,in some cases,con-current processes)further reduce the homogeneity of cluster perfor-mance.On virtualized machines,concurrent activities performed by different virtual machines located on the same physical machine can cause2-4%variation in performance[5].If the amount of work needed to execute a query is equally di-vided among the nodes in a shared-nothing cluster,then there is a danger that the time to complete the query will be approximately equal to time for the slowest compute node to complete its assigned task.A node with degraded performance would thus have a dis-proportionate effect on total query time.A system designed to run in a heterogeneous environment must take appropriate measures to prevent this from occurring.Flexible query interface.There are a variety of customer-facing business intelligence tools that work with database software and aid in the visualization,query generation,result dash-boarding,and advanced data analysis.These tools are an important part of the analytical data management picture since business analysts are of-ten not technically advanced and do not feel comfortable interfac-ing with the database software directly.Business Intelligence tools typically connect to databases using ODBC or JDBC,so databases that want to work with these tools must accept SQL queries through these interfaces.Ideally,the data analysis system should also have a robust mech-anism for allowing the user to write user defined functions(UDFs) and queries that utilize UDFs should automatically be parallelized across the processing nodes in the shared-nothing cluster.Thus, both SQL and non-SQL interface languages are desirable.4.BACKGROUND AND SHORTFALLS OFA V AILABLE APPROACHESIn this section,we give an overview of the parallel database and MapReduce approaches to performing data analysis,and list the properties described in Section3that each approach meets.4.1Parallel DBMSsParallel database systems stem from research performed in the late1980s and most current systems are designed similarly to the early Gamma[10]and Grace[12]parallel DBMS research projects.These systems all support standard relational tables and SQL,and implement many of the performance enhancing techniques devel-oped by the research community over the past few decades,in-cluding indexing,compression(and direct operation on compressed data),materialized views,result caching,and I/O sharing.Most (or even all)tables are partitioned over multiple nodes in a shared-nothing cluster;however,the mechanism by which data is parti-tioned is transparent to the end-user.Parallel databases use an op-timizer tailored for distributed workloads that turn SQL commands into a query plan whose execution is divided equally among multi-ple nodes.Of the desired properties of large scale data analysis workloads described in Section3,parallel databases best meet the“perfor-mance property”due to the performance push required to compete on the open market,and the ability to incorporate decades worth of performance tricks published in the database research commu-nity.Parallel databases can achieve especially high performance when administered by a highly skilled DBA who can carefully de-sign,deploy,tune,and maintain the system,but recent advances in automating these tasks and bundling the software into appliance (pre-tuned and pre-configured)offerings have given many parallel databases high performance out of the box.Parallel databases also score well on theflexible query interface property.Implementation of SQL and ODBC is generally a given, and many parallel databases allow UDFs(although the ability for the query planner and optimizer to parallelize UDFs well over a shared-nothing cluster varies across different implementations). However,parallel databases generally do not score well on the fault tolerance and ability to operate in a heterogeneous environ-ment properties.Although particular details of parallel database implementations vary,their historical assumptions that failures are rare events and“large”clusters mean dozens of nodes(instead of hundreds or thousands)have resulted in engineering decisions that make it difficult to achieve these properties.Furthermore,in some cases,there is a clear tradeoff between fault tolerance and performance,and parallel databases tend to choose the performance extreme of these tradeoffs.For example, frequent check-pointing of completed sub-tasks increase the fault tolerance of long-running read queries,yet this check-pointing reduces performance.In addition,pipelining intermediate results between query operators can improve performance,but can result in a large amount of work being lost upon a failure.4.2MapReduceMapReduce was introduced by Dean et.al.in2004[8]. Understanding the complete details of how MapReduce works is not a necessary prerequisite for understanding this paper.In short, MapReduce processes data distributed(and replicated)across many nodes in a shared-nothing cluster via three basic operations. First,a set of Map tasks are processed in parallel by each node in the cluster without communicating with other nodes.Next,data is repartitioned across all nodes of the cluster.Finally,a set of Reduce tasks are executed in parallel by each node on the partition it receives.This can be followed by an arbitrary number of additional Map-repartition-Reduce cycles as necessary.MapReduce does not create a detailed query execution plan that specifies which nodes will run which tasks in advance;instead,this is determined at runtime.This allows MapReduce to adjust to node failures and slow nodes on thefly by assigning more tasks to faster nodes and reassigning tasks from failed nodes.MapReduce also checkpoints the output of each Map task to local disk in order to minimize the amount of work that has to be redone upon a failure.Of the desired properties of large scale data analysis workloads,MapReduce best meets the fault tolerance and ability to operate in heterogeneous environment properties.It achieves fault tolerance by detecting and reassigning Map tasks of failed nodes to other nodes in the cluster(preferably nodes with replicas of the input Map data).It achieves the ability to operate in a heterogeneous environ-ment via redundant task execution.Tasks that are taking a long time to complete on slow nodes get redundantly executed on other nodes that have completed their assigned tasks.The time to complete the task becomes equal to the time for the fastest node to complete the redundantly executed task.By breaking tasks into small,granular tasks,the effect of faults and“straggler”nodes can be minimized. MapReduce has aflexible query interface;Map and Reduce func-tions are just arbitrary computations written in a general-purpose language.Therefore,it is possible for each task to do anything on its input,just as long as its output follows the conventions defined by the model.In general,most MapReduce-based systems(such as Hadoop,which directly implements the systems-level details of the MapReduce paper)do not accept declarative SQL.However,there are some exceptions(such as Hive).As shown in previous work,the biggest issue with MapReduce is performance[23].By not requiring the user tofirst model and load data before processing,many of the performance enhancing tools listed above that are used by database systems are not possible. Traditional business data analytical processing,that have standard reports and many repeated queries,is particularly,poorly suited for the one-time query processing model of MapReduce.Ideally,the fault tolerance and ability to operate in heterogeneous environment properties of MapReduce could be combined with the performance of parallel databases systems.In the following sec-tions,we will describe our attempt to build such a hybrid system.5.HADOOPDBIn this section,we describe the design of HadoopDB.The goal of this design is to achieve all of the properties described in Section3. The basic idea behind HadoopDB is to connect multiple single-node database systems using Hadoop as the task coordinator and network communication layer.Queries are parallelized across nodes using the MapReduce framework;however,as much of the single node query work as possible is pushed inside of the corresponding node databases.HadoopDB achieves fault tolerance and the ability to operate in heterogeneous environments by inheriting the scheduling and job tracking implementation from Hadoop,yet it achieves the performance of parallel databases by doing much of the query processing inside of the database engine.5.1Hadoop Implementation BackgroundAt the heart of HadoopDB is the Hadoop framework.Hadoop consits of two layers:(i)a data storage layer or the Hadoop Dis-tributed File System(HDFS)and(ii)a data processing layer or the MapReduce Framework.HDFS is a block-structuredfile system managed by a central NameNode.Individualfiles are broken into blocks of afixed size and distributed across multiple DataNodes in the cluster.The NameNode maintains metadata about the size and location of blocks and their replicas.The MapReduce Framework follows a simple master-slave ar-chitecture.The master is a single JobTracker and the slaves or worker nodes are TaskTrackers.The JobTracker handles the run-time scheduling of MapReduce jobs and maintains information on each TaskTracker’s load and available resources.Each job is bro-ken down into Map tasks based on the number of data blocks that require processing,and Reduce tasks.The JobTracker assigns tasks to TaskTrackers based on locality and load balancing.It achievesFigure1:The Architecture of HadoopDBlocality by matching a TaskTracker to Map tasks that process data local to it.It load-balances by ensuring all available TaskTrackers are assigned tasks.TaskTrackers regularly update the JobTracker with their status through heartbeat messages.The InputFormat library represents the interface between the storage and processing layers.InputFormat implementations parse text/binaryfiles(or connect to arbitrary data sources)and transform the data into key-value pairs that Map tasks can process.Hadoop provides several InputFormat implementations including one that allows a single JDBC-compliant database to be accessed by all tasks in one job in a given cluster.5.2HadoopDB’s ComponentsHadoopDB extends the Hadoop framework(see Fig.1)by pro-viding the following four components:5.2.1Database ConnectorThe Database Connector is the interface between independent database systems residing on nodes in the cluster and TaskTrack-ers.It extends Hadoop’s InputFormat class and is part of the Input-Format Implementations library.Each MapReduce job supplies the Connector with an SQL query and connection parameters such as: which JDBC driver to use,query fetch size and other query tuning parameters.The Connector connects to the database,executes the SQL query and returns results as key-value pairs.The Connector could theoretically connect to any JDBC-compliant database that resides in the cluster.However,different databases require different read query optimizations.We implemented connectors for MySQL and PostgreSQL.In the future we plan to integrate other databases including open-source column-store databases such as MonetDB and InfoBright.By extending Hadoop’s InputFormat,we integrate seamlessly with Hadoop’s MapReduce Framework.To the frame-work,the databases are data sources similar to data blocks in HDFS.5.2.2CatalogThe catalog maintains metainformation about the databases.This includes the following:(i)connection parameters such as database location,driver class and credentials,(ii)metadata such as data sets contained in the cluster,replica locations,and data partition-ing properties.The current implementation of the HadoopDB catalog stores its metainformation as an XMLfile in HDFS.Thisfile is accessed by the JobTracker and TaskTrackers to retrieve information necessaryto schedule tasks and process data needed by a query.In the future, we plan to deploy the catalog as a separate service that would work in a way similar to Hadoop’s NameNode.5.2.3Data LoaderThe Data Loader is responsible for(i)globally repartitioning data on a given partition key upon loading,(ii)breaking apart single node data into multiple smaller partitions or chunks and(iii)finally bulk-loading the single-node databases with the chunks.The Data Loader consists of two main components:Global Hasher and Local Hasher.The Global Hasher executes a custom-made MapReduce job over Hadoop that reads in raw datafiles stored in HDFS and repartitions them into as many parts as the number of nodes in the cluster.The repartitioning job does not incur the sorting overhead of typical MapReduce jobs.The Local Hasher then copies a partition from HDFS into the localfile system of each node and secondarily partitions thefile into smaller sized chunks based on the maximum chunk size setting. The hashing functions used by both the Global Hasher and the Local Hasher differ to ensure chunks are of a uniform size.They also differ from Hadoop’s default hash-partitioning function to en-sure better load balancing when executing MapReduce jobs over the data.5.2.4SQL to MapReduce to SQL(SMS)Planner HadoopDB provides a parallel database front-end to data analysts enabling them to process SQL queries.The SMS planner extends Hive[11].Hive transforms HiveQL,a variant of SQL,into MapReduce jobs that connect to tables stored asfiles in HDFS.The MapReduce jobs consist of DAGs of rela-tional operators(such asfilter,select(project),join,aggregation) that operate as iterators:each operator forwards a data tuple to the next operator after processing it.Since each table is stored as a separatefile in HDFS,Hive assumes no collocation of tables on nodes.Therefore,operations that involve multiple tables usually require most of the processing to occur in the Reduce phase of a MapReduce job.This assumption does not completely hold in HadoopDB as some tables are collocated and if partitioned on the same attribute,the join operation can be pushed entirely into the database layer.To understand how we extended Hive for SMS as well as the dif-ferences between Hive and SMS,wefirst describe how Hive creates an executable MapReduce job for a simple GroupBy-Aggregation query.Then,we describe how we modify the execution plan for HadoopDB by pushing most of the query processing logic into the database layer.Consider the following query:SELECT YEAR(saleDate),SUM(revenue)FROM sales GROUP BY YEAR(saleDate);Hive processes the above SQL query in a series of phases:(1)The parser transforms the query into an Abstract Syntax Tree.(2)The Semantic Analyzer connects to Hive’s internal catalog, the MetaStore,to retrieve the schema of the sales table.It also populates different data structures with meta information such as the Deserializer and InputFormat classes required to scan the table and extract the necessaryfields.(3)The logical plan generator then creates a DAG of relational operators,the query plan.(4)The optimizer restructures the query plan to create a more optimized plan.For example,it pushesfilter operators closer to the table scan operators.A key function of the optimizer is to break up the plan into Map or Reduce phases.In particular,it adds a Repar-tition operator,also known as a Reduce Sink operator,before Join or GroupBy operators.These operators mark the Map and ReduceFigure2:(a)MapReduce job generated by Hive(b)MapReduce job generated by SMS assuming sales is par-titioned by YEAR(saleDate).This feature is still unsup-ported(c)MapReduce job generated by SMS assumingno partitioning of salesphases of a query plan.The Hive optimizer is a simple,na¨ıve,rule-based optimizer.It does not use cost-based optimization techniques. Therefore,it does not always generate efficient query plans.This is another advantage of pushing as much as possible of the query pro-cessing logic into DBMSs that have more sophisticated,adaptive or cost-based optimizers.(5)Finally,the physical plan generator converts the logical query plan into a physical plan executable by one or more MapReduce jobs.Thefirst and every other Reduce Sink operator marks a tran-sition from a Map phase to a Reduce phase of a MapReduce job and the remaining Reduce Sink operators mark the start of new MapRe-duce jobs.The above SQL query results in a single MapReduce job with the physical query plan illustrated in Fig.2(a).The boxes stand for the operators and the arrows represent theflow of data.(6)Each DAG enclosed within a MapReduce job is serialized into an XML plan.The Hive driver then executes a Hadoop job. The job reads the XML plan and creates all the necessary operator objects that scan data from a table in HDFS,and parse and process one tuple at a time.The SMS planner modifies Hive.In particular we intercept the normal Hiveflow in two main areas:(i)Before any query execution,we update the MetaStore with references to our database tables.Hive allows tables to exist exter-nally,outside HDFS.The HadoopDB catalog,Section5.2.2,pro-vides information about the table schemas and required Deserial-izer and InputFormat classes to the MetaStore.We implemented these specialized classes.(ii)After the physical query plan generation and before the ex-ecution of the MapReduce jobs,we perform two passes over the physical plan.In thefirst pass,we retrieve datafields that are actu-ally processed by the plan and we determine the partitioning keys used by the Reduce Sink(Repartition)operators.In the second pass,we traverse the DAG bottom-up from table scan operators to the output or File Sink operator.All operators until thefirst repar-tition operator with a partitioning key different from the database’s key are converted into one or more SQL queries and pushed into the database layer.SMS uses a rule-based SQL generator to recre-ate SQL from the relational operators.The query processing logic that could be pushed into the database layer ranges from none(each。