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Aster Data与Hadoop / Hive之间的区别

[英]Difference between Aster Data and Hadoop/Hive

All the components in Aster Data seems to have a similar component in Hadoop stack. Aster Data中的所有组件似乎在Hadoop堆栈中都具有相似的组件。

AFS => HDFS AFS => HDFS

SQL-MR => Hive SQL-MR =>配置单元

AMC => Ambari AMC =>安巴里

ACT => beeline/hive terminal ACT =>直线/蜂巢终端

Postgres for storing metadata => Hive can be configured to store metadata in any RDBMS 用于存储元数据的Postgres => Hive可以配置为在任何RDBMS中存储元数据

Queen/Worker => NameNode/Datanode 皇后/工人=> NameNode / Datanode

SQL-GR => Giraph SQL-GR =>奇ira

Apart from providing a package of pre-built functions, is there anything that is strikingly different and not available in Hadoop? 除了提供一整套预建功能之外,还有什么与众不同的东西并且在Hadoop中不可用吗?

Your question is not bad, it's wrong. 您的问题还不错,这是错误的。 Likely coming from the Hadoop side you made the question using Hadoop architecture which is assembly of layered and/or integrated somewhat independent components, each with its own functional spec, configuration and execution environment, etc. 可能来自Hadoop方面,您使用Hadoop体系结构提出了问题,该体系结构是分层和/或集成的,具有一定独立性的组件的组装,每个组件都有自己的功能规格,配置和执行环境等。

Why is that wrong? 为什么会这样呢? Because you wouldn't ask the same question if comparing Hadoop and Oracle or SAP databases, right? 因为如果比较Hadoop和Oracle或SAP数据库,您不会问相同的问题,对吗?

Yes, each counterpart on Aster side matches up with Hadoop stack offering - the difference is with Aster there is no such stack (at least as of 6.x yet). 是的,Aster方面的每个对手都与Hadoop堆栈产品相匹配-不同之处在于Aster尚无此类堆栈(至少从6.x版本开始)。 Aster is database and analytical engine matching Hadoop stack functions but not components. Aster是与Hadoop堆栈功能匹配但与组件不匹配的数据库和分析引擎。

For example, ACT is a command-line utility similar to SQL*Plus for Oracle operating over client/server interface. 例如,ACT是类似于SQL * Plus的命令行实用程序,用于在客户端/服务器界面上运行的Oracle。 It's nothing like Hive infrastructure on top of Hadoop. 它与Hadoop之上的Hive基础架构完全不同。 Aster File System comes as complex plug-able functional layer integrated into Aster software - not as independent framework and software that are HDFS and Hadoop. Aster File System是集成到Aster软件中的复杂的可插入功能层,而不是HDFS和Hadoop的独立框架和软件。

The most striking difference goes for Aster analytical engine consisting of SQL, SQL/MR and SQL/GR. 最惊人的区别是由SQL,SQL / MR和SQL / GR组成的Aster分析引擎。 What it means is that there is no functional or operational gaps between data storage and its operators (SQL statements an SQL/MR or SQL/GR functions) operating on the data store - they live inside the same environment (configuration, execution, maintenance, support). 这意味着数据存储与其在数据存储上操作的运算符(SQL语句,SQL / MR或SQL / GR函数)之间没有功能或操作上的差距-它们位于相同的环境(配置,执行,维护,支持)。 For example, columnar and row-based tables are completely transparent for any operation performed on the them (barring constraints defined a priori and by design). 例如,基于列的表和基于行的表对于在其上执行的任何操作都是完全透明的(除非先验和设计限制,否则这些限制)。

So, your analogy does explain Hadoop side of the equation without really giving proper due to Aster. 因此,您的类推确实解释了等式的Hadoop方面,而实际上由于Aster而没有给出适当的解释。

I think u unnecessarily went for nitty grity. 我认为您不必要地花了很多精力。 The question was in conceptual terms. 这个问题是概念上的。 Say Netezza and Teradata MPP. 说Netezza和Teradata MPP。 In Basic concept they are same and they do the same kind of work but they are two different physical implementation with their own algo,storage,indexes etc. 在基本概念上,它们是相同的,并且它们执行相同的工作,但是它们是两种不同的物理实现,具有各自的算法,存储,索引等。

On a very high level Hive and Aster has similarity as they both run map reduce on a distributed storage . 在非常高的层次上,Hive和Aster具有相似之处,因为它们在分布式存储上的运行图减少。

The Only difference on very high level is that at very high level Aster can run typical RDMS query as well as implicit map reduce where as Hive is only map reduce. 在非常高的级别上,唯一的区别是,在非常高的级别上,Aster可以运行典型的RDMS查询以及隐式映射减少,而Hive仅是映射减少。

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