blocks|key|1779392|text|Hive是一个分析工具。就像pig一样，它是为利用map+reduce对潜在的海量数据进行临时批处理而设计的。以terrabytes为例。想象一下，在关系数据库中尝试这样做...|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1779393|HBase是基于BigTable的基于列的键值存储。虽然可以通过HBase运行map+reduce作业，但不能执行查询本身。它的主要用例是按键读取行，或扫描行的范围。一个主要的特征是当扫描列的‘族’的行键范围时，能够具有数据局部性。|1779394|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

Hive is an analytics tool. Just like pig, it was designed for ad hoc batch processing of potentially enourmous amounts of data by leveraging map reduce. Think terrabytes. Imagine trying to do that in a relational database...

HBase is a column based key value store based on BigTable. You can't do queries per se, though you can run map reduce jobs over HBase. It's primary use case is fetching rows by key, or scanning ranges of rows. A major feature is being able to have data locality when scanning across ranges of row keys for a 'family' of columns.

blocks|key|1564369|text|从一个角度来看，Hive由五个主要组件组成:类似SQL的语法和解析器、查询规划器、查询执行引擎、元数据存储库和列式存储布局。它的主要关注点是数据仓库风格的分析工作负载，因此不需要通过键低延迟检索值。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1564370|HBase有自己的元数据存储库和列式存储布局。可以在HiveQL表上编写HBase查询，从而允许HBase利用Hive的语法和解析器、查询规划器和查询执行引擎。有关更多详细信息，请参阅http://wiki.apache.org/hadoop/Hive/HBaseIntegration。|offset|length|1564371|entityMap|0|LINK|mutability|MUTABLE|url|http://wiki.apache.org/hadoop/Hive/HBaseIntegration^0|0|2K|1F|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|O|8|@]|9|@$D|P|E|Q|1|R]]|A|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|M]]]]

From one perspective, Hive consists of five main components: a SQL-like grammar and parser, a query planner, a query execution engine, a metadata repository, and a columnar storage layout. Its primary focus is data warehouse-style analytical workloads, so low latency retrieval of values by key is not necessary.

HBase has its own metadata repository and columnar storage layout. It is possible to author HiveQL queries over HBase tables, allowing HBase to take advantage of Hive's grammar and parser, query planner, and query execution engine. See <a href="http://wiki.apache.org/hadoop/Hive/HBaseIntegration" rel="noreferrer">http://wiki.apache.org/hadoop/Hive/HBaseIntegration</a> for more details.

blocks|key|1779519|text|据我所知，蜂巢比猪更有可比性。Hive类似于SQL，而Pig基于脚本。Hive似乎在查询优化和执行引擎方面更加复杂，并且需要最终用户指定模式参数(分区等)。这两种方法都用于处理文本文件或sequenceFiles。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1779520|HBase用于键值数据存储，retrieve...you可以扫描或过滤这些键值对(行)。不能对(key，value)行进行查询。|1779521|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

To my humble knowledge, Hive is more comparable to Pig. Hive is SQL-like and Pig is script based.
Hive seems to be more complicated with query optimization and execution engines as well as requires end user needs to specify schema parameters(partition etc).
Both are intend to process text files, or sequenceFiles.

HBase is for key value data store and retrieve...you can scan or filter on those key value pairs(rows). You can not do queries on (key,value) rows.

blocks|key|1779603|text|在最新的蜂巢版本中，发生了很多变化，需要以Hive+and+HBase+are+now+integrated的身份进行小的更新。这意味着配置单元可以用作HBase数据存储的查询层。现在，如果人们正在寻找替代的HBase接口，Pig也提供了一种非常好的loading+and+storing+HBase+data方式。此外，看起来Cloudera+Impala可能会在HBase之上提供基于Hive的高性能查询。他们声称查询速度比传统的Hive设置快45倍。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1779604|entityMap|0|LINK|mutability|MUTABLE|url|https://cwiki.apache.org/confluence/display/Hive/HBaseIntegration|1|http://pig.apache.org/docs/r0.9.1/api/org/apache/pig/backend/hadoop/hbase/HBaseStorage.html|2|http://blog.cloudera.com/blog/2012/10/cloudera-impala-real-time-queries-in-apache-hadoop-for-real/^0|L|X|0|3H|U|1|4K|F|2|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]|$A|T|B|U|1|V]|$A|W|B|X|1|Y]]|C|$]]|$1|D|3|-4|5|6|7|Z|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]|L|$5|G|H|I|C|$J|M]]|N|$5|G|H|I|C|$J|O]]]]

As of the most recent Hive releases, a lot has changed that requires a small update as <a href="https://cwiki.apache.org/confluence/display/Hive/HBaseIntegration" rel="nofollow">Hive and HBase are now integrated</a>. What this means is that Hive can be used as a query layer to an HBase datastore. Now if people are looking for alternative HBase interfaces, Pig also offers a really nice way of <a href="http://pig.apache.org/docs/r0.9.1/api/org/apache/pig/backend/hadoop/hbase/HBaseStorage.html" rel="nofollow">loading and storing HBase data</a>. Additionally, it looks like <a href="http://blog.cloudera.com/blog/2012/10/cloudera-impala-real-time-queries-in-apache-hadoop-for-real/" rel="nofollow">Cloudera Impala</a> may offer substantial performance Hive based queries on top of HBase. They are claim up to 45x faster queries over traditional Hive setups.

I'm interested in finding out how the recently-released (<a href="http://mirror.facebook.com/facebook/hive/hadoop-0.17/" rel="noreferrer">http://mirror.facebook.com/facebook/hive/hadoop-0.17/</a>) Hive compares to HBase in terms of performance. The SQL-like interface used by Hive is very much preferable to the HBase API we have implemented.

How does Hive compare to HBase?

数据存储

数据仓库

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HBase

Hive

我有兴趣知道最近发布的()蜂巢在性能方面与HBase相比如何。Hive使用的类似SQL的接口比我们实现的HBase应用程序接口要好得多。

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