Cluster Setup:. This approach has one more advantage. In contrast, MapReduce, Hive, and Pig run on the Hadoop framework, which relies more on local disk is more reliable for batch processing workloads. Hive is an open-source engine with a vast community: 1). Hive使用Orc格式的内部表;Impala使用Hive上的Parquet格式数据;Presto使用Hive上的Orc格式数据;Hawq建立内部表使用默认Txt格式;Clickhouse使用Log表引擎分布式建表。 测试组件介绍 2.1 SparkSql Spark SQL 是 Spark 处理结构化数据的程序模块。 Hive将SQL语言映射为MapReduce进而实现查询,但往往相应较慢,在实时性上有欠缺。而Cloudera公司的Impala和Facebook提出的Presto同样支持SQL语言,但都没有使用MapReduce框架,查询的实时性很好。 我想请问一下Impala和Presto工作的具体 Spark,Hive,Impala和Presto是基于SQL的引擎,Impala由Cloudera开发和交付。在选择这些数据库来管理数据库时,许多Hadoop用户会感到困惑。Presto是一个开放源代码的分布式SQL查询引擎,旨在运行甚至PB级的SQL查询… Alluxio provides Hadoop Distributed File System (HDFS) and S3 API compatibility for compute frameworks like Apache Spark, Presto and Hive that execute on top of Alluxio. 虽然Impala、Spark SQL、Drill、Hawq 和Presto 一直在运行性能、并发量和吞吐量上击败Hive,但是Hive 仍然是最流行的(至少根据DB-Engines 的标准)。原因有3个: Hive 是Hadoop 的默认SQL 选项,每个版本都支持。而其他的要求特定的供应商和合适的 大数据本身是个很宽泛的概念,Hadoop生态圈(或者泛生态圈)基本上都是为了处理超过单机尺度的数据处理而诞生的。你可以把它比作一个厨房所以需要的各种工具。锅碗瓢盆,各有各的用处,互相之间又有重合。你可以用汤锅直接当碗吃饭喝汤,你可以用小刀或者刨子去皮。但是每个工具有自己的特性,虽然奇怪的组合也能工作,但是未必是最佳选择。, 传统的文件系统是单机的,不能横跨不同的机器。HDFS(Hadoop Distributed FileSystem)的设计本质上是为了大量的数据能横跨成百上千台机器,但是你看到的是一个文件系统而不是很多文件系统。比如你说我要获取/hdfs/tmp/file1的数据,你引用的是一个文件路径,但是实际的数据存放在很多不同的机器上。你作为用户,不需要知道这些,就好比在单机上你不关心文件分散在什么磁道什么扇区一样。HDFS为你管理这些数据。, 存的下数据之后,你就开始考虑怎么处理数据。虽然HDFS可以为你整体管理不同机器上的数据,但是这些数据太大了。一台机器读取成T上P的数据(很大的数据哦,比如整个东京热有史以来所有高清电影的大小甚至更大),一台机器慢慢跑也许需要好几天甚至好几周。对于很多公司来说,单机处理是不可忍受的,比如微博要更新24小时热博,它必须在24小时之内跑完这些处理。那么我如果要用很多台机器处理,我就面临了如何分配工作,如果一台机器挂了如何重新启动相应的任务,机器之间如何互相通信交换数据以完成复杂的计算等等。这就是MapReduce / Tez / Spark的功能。MapReduce是第一代计算引擎,Tez和Spark是第二代。MapReduce的设计,采用了很简化的计算模型,只有Map和Reduce两个计算过程(中间用Shuffle串联),用这个模型,已经可以处理大数据领域很大一部分问题了。, 考虑如果你要统计一个巨大的文本文件存储在类似HDFS上,你想要知道这个文本里各个词的出现频率。你启动了一个MapReduce程序。Map阶段,几百台机器同时读取这个文件的各个部分,分别把各自读到的部分分别统计出词频,产生类似(hello, 12100次),(world,15214次)等等这样的Pair(我这里把Map和Combine放在一起说以便简化);这几百台机器各自都产生了如上的集合,然后又有几百台机器启动Reduce处理。Reducer机器A将从Mapper机器收到所有以A开头的统计结果,机器B将收到B开头的词汇统计结果(当然实际上不会真的以字母开头做依据,而是用函数产生Hash值以避免数据串化。因为类似X开头的词肯定比其他要少得多,而你不希望数据处理各个机器的工作量相差悬殊)。然后这些Reducer将再次汇总,(hello,12100)+(hello,12311)+(hello,345881)= (hello,370292)。每个Reducer都如上处理,你就得到了整个文件的词频结果。, Map+Reduce的简单模型很黄很暴力,虽然好用,但是很笨重。第二代的Tez和Spark除了内存Cache之类的新feature,本质上来说,是让Map/Reduce模型更通用,让Map和Reduce之间的界限更模糊,数据交换更灵活,更少的磁盘读写,以便更方便地描述复杂算法,取得更高的吞吐量。, 有了MapReduce,Tez和Spark之后,程序员发现,MapReduce的程序写起来真麻烦。他们希望简化这个过程。这就好比你有了汇编语言,虽然你几乎什么都能干了,但是你还是觉得繁琐。你希望有个更高层更抽象的语言层来描述算法和数据处理流程。于是就有了Pig和Hive。Pig是接近脚本方式去描述MapReduce,Hive则用的是SQL。它们把脚本和SQL语言翻译成MapReduce程序,丢给计算引擎去计算,而你就从繁琐的MapReduce程序中解脱出来,用更简单更直观的语言去写程序了。, 有了Hive之后,人们发现SQL对比Java有巨大的优势。一个是它太容易写了。刚才词频的东西,用SQL描述就只有一两行,MapReduce写起来大约要几十上百行。而更重要的是,非计算机背景的用户终于感受到了爱:我也会写SQL!于是数据分析人员终于从乞求工程师帮忙的窘境解脱出来,工程师也从写奇怪的一次性的处理程序中解脱出来。大家都开心了。Hive逐渐成长成了大数据仓库的核心组件。甚至很多公司的流水线作业集完全是用SQL描述,因为易写易改,一看就懂,容易维护。. 于是Impala,Presto,Drill诞生了(当然还有无数非著名的交互SQL引擎,就不一一列举了)。三个系统的核心理念是,MapReduce引擎太慢,因为它太通用,太强壮,太保守,我们SQL需要更轻量,更激进地获取资源,更专门地对SQL做优化,而且不需要那么多容错性保证(因为系统出错了大不了重新启动任务,如果整个处理时间更短的话,比如几分钟之内)。这些系统让用户更快速地处理SQL任务,牺牲了通用性稳定性等特性。如果说MapReduce是大砍刀,砍啥都不怕,那上面三个就是剔骨刀,灵巧锋利,但是不能搞太大太硬的东西。, 这些系统,说实话,一直没有达到人们期望的流行度。因为这时候又两个异类被造出来了。他们是Hive on Tez / Spark和SparkSQL。它们的设计理念是,MapReduce慢,但是如果我用新一代通用计算引擎Tez或者Spark来跑SQL,那我就能跑的更快。而且用户不需要维护两套系统。这就好比如果你厨房小,人又懒,对吃的精细程度要求有限,那你可以买个电饭煲,能蒸能煲能烧,省了好多厨具。, 上面的介绍,基本就是一个数据仓库的构架了。底层HDFS,上面跑MapReduce/Tez/Spark,在上面跑Hive,Pig。或者HDFS上直接跑Impala,Drill,Presto。这解决了中低速数据处理的要求。, 如果我是一个类似微博的公司,我希望显示不是24小时热博,我想看一个不断变化的热播榜,更新延迟在一分钟之内,上面的手段都将无法胜任。于是又一种计算模型被开发出来,这就是Streaming(流)计算。Storm是最流行的流计算平台。流计算的思路是,如果要达到更实时的更新,我何不在数据流进来的时候就处理了?比如还是词频统计的例子,我的数据流是一个一个的词,我就让他们一边流过我就一边开始统计了。流计算很牛逼,基本无延迟,但是它的短处是,不灵活,你想要统计的东西必须预先知道,毕竟数据流过就没了,你没算的东西就无法补算了。因此它是个很好的东西,但是无法替代上面数据仓库和批处理系统。, 还有一个有些独立的模块是KV Store,比如Cassandra,HBase,MongoDB以及很多很多很多很多其他的(多到无法想象)。所以KV Store就是说,我有一堆键值,我能很快速滴获取与这个Key绑定的数据。比如我用身份证号,能取到你的身份数据。这个动作用MapReduce也能完成,但是很可能要扫描整个数据集。而KV Store专用来处理这个操作,所有存和取都专门为此优化了。从几个P的数据中查找一个身份证号,也许只要零点几秒。这让大数据公司的一些专门操作被大大优化了。比如我网页上有个根据订单号查找订单内容的页面,而整个网站的订单数量无法单机数据库存储,我就会考虑用KV Store来存。KV Store的理念是,基本无法处理复杂的计算,大多没法JOIN,也许没法聚合,没有强一致性保证(不同数据分布在不同机器上,你每次读取也许会读到不同的结果,也无法处理类似银行转账那样的强一致性要求的操作)。但是丫就是快。极快。, 每个不同的KV Store设计都有不同取舍,有些更快,有些容量更高,有些可以支持更复杂的操作。必有一款适合你。, 除此之外,还有一些更特制的系统/组件,比如Mahout是分布式机器学习库,Protobuf是数据交换的编码和库,ZooKeeper是高一致性的分布存取协同系统,等等。, 有了这么多乱七八糟的工具,都在同一个集群上运转,大家需要互相尊重有序工作。所以另外一个重要组件是,调度系统。现在最流行的是Yarn。你可以把他看作中央管理,好比你妈在厨房监工,哎,你妹妹切菜切完了,你可以把刀拿去杀鸡了。只要大家都服从你妈分配,那大家都能愉快滴烧菜。, 你可以认为,大数据生态圈就是一个厨房工具生态圈。为了做不同的菜,中国菜,日本菜,法国菜,你需要各种不同的工具。而且客人的需求正在复杂化,你的厨具不断被发明,也没有一个万用的厨具可以处理所有情况,因此它会变的越来越复杂。, 对于hbase当前noSql数据库的一种,最常见的应用场景就是采集的网页数据的存储,由于是key-value型数据库,可以再扩展到各种key-value应用场景,如日志信息的存储,对于内容信息不需要完全结构化出来的类CMS应用等。注意hbase针对的仍然是OLTP应用为主。, OLTP即联机事务处理,就是我们经常说的关系数据库,意即记录即时的增、删、改、查,就是我们经常应用的东西,这是数据库的基础;, OLAP即联机分析处理,是数据仓库的核心部心,所谓数据仓库是对于大量已经由OLTP形成的数据的一种分析型的数据库,用于处理商业智能、决策支持等重要的决策信息;数据仓库是在数据库应用到一定程序之后而对历史数据的加工与分析;是处理两种不同用途的工具而已。, https://www.cnblogs.com/jins-note/p/9513445.html. (Note: all Qubole Clusters – Hadoop, Spark and Presto auto-scale) as the load on the old cluster drains – it is automatically scaled down; once the old cluster is idle – it is automatically terminated; both clusters continue to be monitored and healed. 作者:Xiaoyu Ma ,大数据工程师 大数据本身是个很宽泛的概念,Hadoop生态圈(或者泛生态圈)基本上都是为了处理超过单机尺度的数据处理而诞生的。你可以把它比作一个厨房所以需要的各种工具 … AtScale recently performed benchmark tests on the Hadoop engines Spark, Impala, Hive, and Presto. Hadoop、Hive、Spark、Presto、Mapreduce...ビッグデータ周りはこういった用語が多いです。 自分は初めてこれらに触れたときに訳がわからなくて泣きそうになったのでざっくりとまとめました。 One of the most confusing aspects when starting Presto is the Hive connector. Hive tables are defined as external what gives us ability to keep log files in place in /flume/events folder. Now, thanks to a number of open source projects, big data analytics with Hadoop has become much more affordable and mainstream. But it is not easy to build a data platform being composed of hadoop, hadoop ecosystems like hive, hbase, pig, etc., and other components like spark, kafka, etc., because there are many things to do, for instance, components compatibility, and configuration tunning, optimizations, security, etc. 虚拟机上安装 hadoop+hive+presto 系统环境 在个人笔记本上使用virtualbox虚拟机 os:centos -7.x86-64.everything.1611 ,内核 3.10.0-514.el7.x86_64 Hive Pros: Hive Cons: 1). Impala queries are not translated to MapReduce jobs, instead, they are executed natively. It is a stable query engine : 2). TL;DR: The Hive connector is what you use in Presto for reading data from object storage that is organized according to the rules laid out by Hive, without using the Hive runtime code. The tables are defined in Hive but all the SQL queries are executed with PrestoDB. Data locality with intelligent tiering. It is also compatible with storage systems integrated below Alluxio. Hadoop, Hive, Spark에 대해 자세히 알아보기 3 minute read On this page 데이터의 종류 열 지향 스토리지로의 변환 Hadoop Ecosystem 구성 요소 대화형 쿼리 엔진 Presto 쿼리 엔진 활용 방법 Hive 효율적으로 사용하기 Spark Here's a look at how three open source projects—Hive, Spark, and Presto—have transformed the … They will be able to independently load and transform huge datasets with the help of technologies like Hive, Spark, Sqoop, Kafka and Oozie. 自从数据分析人员开始用Hive分析数据之后,它们发现,Hive在MapReduce上跑,真鸡巴慢!流水线作业集也许没啥关系,比如24小时更新的推荐,反正24小时内跑完就算了。但是数据分析,人们总是希望能跑更快一些。比如我希望看过去一个小时内多少人在充气娃娃页面驻足,分别停留了多久,对于一个巨型网站海量数据下,这个处理过程也许要花几十分钟甚至很多小时。而这个分析也许只是你万里长征的第一步,你还要看多少人浏览了跳蛋多少人看了拉赫曼尼诺夫的CD,以便跟老板汇报,我们的用户是猥琐男闷骚女更多还是文艺青年/少女更多。你无法忍受等待的折磨,只能跟帅帅的工程师蝈蝈说,快,快,再快一点! ビッグデータ周りはこういった用語が多いです。 自分は初めてこれらに触れたときに訳がわからなくて泣きそうになったのでざっくりとまとめました。 Spark, Hive, Impala and Presto are SQL based engines results, and discover which might! 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Spark, Hive, Impala and Presto are SQL based engines to MapReduce jobs, instead, are... /Flume/Events folder a metadata reference store are executed with PrestoDB are part of trend...... ビッグデータ周りはこういった用語が多いです。 自分は初めてこれらに触れたときに訳がわからなくて泣きそうになったのでざっくりとまとめました。 Spark, Hive, Impala and Presto are SQL based.... Are not translated to MapReduce jobs, instead, they are executed natively analytics with Hadoop has become more! Us ability to keep log files in place in /flume/events folder of the most important technologies from Hadoop.