blocks|key|1497544|text|我找到了一个大学网站，里面有一些MapReduce的练习和解决方案，它们都是在Hadoop上构建的：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1497545|http://www.umiacs.umd.edu/~jimmylin/Cloud9/docs/index.html|offset|length|1497546|此外，还有来自雅虎和谷歌的课程：|1497547|http://developer.yahoo.com/hadoop/tutorial/|1497548|http://code.google.com/edu/parallel/index.html|1497549|所有这些课程都使用普通的Hadoop来回答您的问题。|1497550|entityMap|0|LINK|mutability|MUTABLE|url|1|2^0|0|0|1M|0|0|0|0|17|1|0|0|1A|2|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|X|8|@]|9|@$D|Y|E|Z|1|10]]|A|$]]|$1|F|3|G|5|6|7|11|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|12|8|@]|9|@$D|13|E|14|1|15]]|A|$]]|$1|J|3|K|5|6|7|16|8|@]|9|@$D|17|E|18|1|19]]|A|$]]|$1|L|3|M|5|6|7|1A|8|@]|9|@]|A|$]]|$1|N|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|O|$P|$5|Q|R|S|A|$T|C]]|U|$5|Q|R|S|A|$T|I]]|V|$5|Q|R|S|A|$T|K]]]]

I've found a university site with some exercises and solutions for MapReduce that build only on Hadoop:

<a href="http://www.umiacs.umd.edu/~jimmylin/Cloud9/docs/index.html" rel="noreferrer">http://www.umiacs.umd.edu/~jimmylin/Cloud9/docs/index.html</a>

Additionally there are courses from Yahoo and Google:

<a href="http://developer.yahoo.com/hadoop/tutorial/" rel="noreferrer">http://developer.yahoo.com/hadoop/tutorial/</a>

<a href="http://code.google.com/edu/parallel/index.html" rel="noreferrer">http://code.google.com/edu/parallel/index.html</a>

All these courses work on plain Hadoop, to answer your question.

blocks|key|2472035|text|从初学者级别的普通mapreduce开始。你可以尝试下一级的Pig/Hive/Hbase。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2472036|你将无法欣赏Pig/Hive/Hbase，除非你努力使用简单的map+reduce|2472037|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|$]]

Start with plain mapreduce at beginner level. You can try Pig/Hive/Hbase at the next level.

You will not be able appreciate Pig/Hive/Hbase unless you struggle enough to use plain map reduce

blocks|key|1497559|text|我也推荐umd网站。但是，看起来您对Hadoop完全陌生。我推荐汤姆·怀特的“the+:Hadoop定义指南”这本书。它有点过时[意味着0.18版本，而不是最新的0.20%2B)。阅读它，做例子，你应该在一个更好的地方来判断如何构建你的项目。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1497560|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

I would also recommend the umd site. However it looks like you are completely new to Hadoop. I woudl recommend the book "Hadoop: THe Definant Guide" by Tom White. Its a bit dated [meant for the 0.18 version, rather than the latest 0.20+). Read it, do the examples and you should be at a better place to judge how to structure your project.

blocks|key|3879983|text|我正在尝试使用hadoop来练习一些数据挖掘算法。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3879984|使用运行在Hadoop之上的Apache+mahout。+[http://mahout.apache.org/][1]|offset|length|style|BOLD|3879985|我可以单独使用HDFS来做这件事吗，或者我需要使用hive/hbase/pig这样的子项目吗？|3879986|hadoop是的一个文件系统。它代表Hadoop分布式文件系统。无论您要在Hadoop堆栈中使用什么工具，它们都应该处理分布式环境中的数据。因此，您不能仅使用HDFS来做任何事情。你需要任何计算技术/工具，比如Map+Reduce，Pig，Hive等等。|3879987|希望这能有所帮助！|3879988|entityMap|0|LINK|mutability|MUTABLE|url|http://mahout.apache.org/][1]^0|0|0|S|T|U|0|0|0|7|3C|0|0^^$0|@$1|2|3|4|5|6|7|V|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|W|8|@$D|X|E|Y|F|G]]|9|@$D|Z|E|10|1|11]]|A|$]]|$1|H|3|I|5|6|7|12|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|13|8|@$D|14|E|15|F|G]]|9|@]|A|$]]|$1|L|3|M|5|6|7|16|8|@]|9|@]|A|$]]|$1|N|3|-4|5|6|7|17|8|@]|9|@]|A|$]]]|O|$P|$5|Q|R|S|A|$T|U]]]]

I'm trying to practice some data mining algorithms using hadoop. 

Use Apache mahout which runs on top of Hadoop.
[<a href="http://mahout.apache.org/][1]" rel="nofollow">http://mahout.apache.org/][1]</a>

Can I do this with HDFS alone, or do I need to use the sub-projects like hive/hbase/pig?

HDFS is a file system of hadoop. It stands for Hadoop distributed file system. No matter what tool you are going to use in Hadoop stack, they should process the data which is in the distributed environment. So, you can't do anything just with HDFS. You need any of the computation technicques/tools like Map Reduce, Pig, Hive and etc.

Hope this helps!

blocks|key|1500936|text|您还可以使用Mahout+http://mahout.apache.org/|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1500937|它是一个可以在Hadoop上使用的机器学习和数据挖掘库。|1500938|一般而言，Mahout目前支持(取自Mahout站点)：|1500939|基于模糊K-Means的森林用户和项目协同过滤recommenders|1500940|K-Means，clustering|unordered-list-item|1500941|
|1500942|Mean+Shift+clustering|1500943|1500944|Dirichlet+process+clustering|1500945|1500946|Latent+Dirichlet+Allocation|1500947|1500948|Singular+value+decomposition|1500949|1500950|Parallel+Frequent+Pattern+mining|1500951|1500952|Complementary朴素贝叶斯classifier|1500953|1500954|Random+|1500955|1500956|1500957|1500958|1500959|entityMap|0|LINK|mutability|MUTABLE|url|http://mahout.apache.org/^0|D|P|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|1K|8|@]|9|@$A|1L|B|1M|1|1N]]|C|$]]|$1|D|3|E|5|6|7|1O|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|1P|8|@]|9|@]|C|$]]|$1|H|3|I|5|6|7|1Q|8|@]|9|@]|C|$]]|$1|J|3|K|5|L|7|1R|8|@]|9|@]|C|$]]|$1|M|3|N|5|6|7|1S|8|@]|9|@]|C|$]]|$1|O|3|P|5|L|7|1T|8|@]|9|@]|C|$]]|$1|Q|3|N|5|6|7|1U|8|@]|9|@]|C|$]]|$1|R|3|S|5|L|7|1V|8|@]|9|@]|C|$]]|$1|T|3|N|5|6|7|1W|8|@]|9|@]|C|$]]|$1|U|3|V|5|L|7|1X|8|@]|9|@]|C|$]]|$1|W|3|N|5|6|7|1Y|8|@]|9|@]|C|$]]|$1|X|3|Y|5|L|7|1Z|8|@]|9|@]|C|$]]|$1|Z|3|N|5|6|7|20|8|@]|9|@]|C|$]]|$1|10|3|11|5|L|7|21|8|@]|9|@]|C|$]]|$1|12|3|N|5|6|7|22|8|@]|9|@]|C|$]]|$1|13|3|14|5|L|7|23|8|@]|9|@]|C|$]]|$1|15|3|N|5|6|7|24|8|@]|9|@]|C|$]]|$1|16|3|17|5|L|7|25|8|@]|9|@]|C|$]]|$1|18|3|-4|5|L|7|26|8|@]|9|@]|C|$]]|$1|19|3|-4|5|L|7|27|8|@]|9|@]|C|$]]|$1|1A|3|-4|5|6|7|28|8|@]|9|@]|C|$]]|$1|1B|3|-4|5|6|7|29|8|@]|9|@]|C|$]]|$1|1C|3|-4|5|6|7|2A|8|@]|9|@]|C|$]]]|1D|$1E|$5|1F|1G|1H|C|$1I|1J]]]]

You could also use Mahout <a href="http://mahout.apache.org/" rel="nofollow">http://mahout.apache.org/</a>

It is a machine-learning and data-mining library that can be used on top of Hadoop.

In general Mahout currently supports (taken from Mahout site):

<ul>
<li>Collaborative Filtering</li>
<li>User and Item based recommenders</li>
<li>K-Means, Fuzzy K-Means clustering</li>
<li>Mean Shift clustering</li>
<li>Dirichlet process clustering</li>
<li>Latent Dirichlet Allocation</li>
<li>Singular value decomposition</li>
<li>Parallel Frequent Pattern mining</li>
<li>Complementary Naive Bayes classifier</li>
<li>Random forest decision tree based classifier</li>
</ul>

blocks|key|3879949|text|Hadoop是一个用于分布式/并行数据处理的工具。Mahout是一个数据挖掘/机器学习框架，可以在独立模式下工作，也可以在Hadoop分发环境中工作。是将其作为独立数据使用还是与Hadoop一起使用，归根结底取决于需要挖掘的历史数据的大小。如果数据大小约为the和If，则通常将Mahout与Hadoop一起使用。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3879950|Mahout支持3种机器学习算法，推荐，聚类和分类。曼宁所著的Mahout+in+action一书很好地解释了这一点。Weka是另一个类似的开源项目。所有这些都属于一个称为机器学习框架的类别。|offset|length|style|BOLD|3879951|请参考博客，其中讨论了一个关于如何使用Mahout+and+Hadoop+distributed+file+system+works的用例？作为先驱，在Hadoop+/Mahout生态系统中还有一个博客，介绍如何将这些工具中的每一个组合在一起来解决数据挖掘问题。|3879952|entityMap|0|LINK|mutability|MUTABLE|url|https://krishnasblog.com/2012/06/29/implementing-a-recommender-engine-using-hadoop-and-mahout/^0|0|V|G|0|J|1B|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|S|8|@$D|T|E|U|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|V|8|@]|9|@$D|W|E|X|1|Y]]|A|$]]|$1|J|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]]]

Hadoop is a tool for Distributed/parallel data processing. Mahout is a data mining/ machine learning framework that can work standalone mode as well as in Hadoop distribution environment. The decision to use it as standalone or with Hadoop boils down to the size of the historical data that needs to be mined. If the data size is of the order of Terabytes and Petabytes, you typically use Mahout with Hadoop.

Mahout supports 3 machine Learning algorithms, recommendation, clustering and classification. Mahout in action book by manning does a very good job of explaining this. Weka is another similar open source projects. All these come under a category called machine learning frameworks. 

Refer to the blog which talks about a use case about how <a href="https://krishnasblog.com/2012/06/29/implementing-a-recommender-engine-using-hadoop-and-mahout/" rel="nofollow noreferrer">Mahout and Hadoop distributed file system works</a>? As a precursor to this, there is also a blog on <a href="https://krishnasblog.com/2012/06/24/hadoop-and-mahout-in-data-mining/" rel="nofollow noreferrer">Component architecture</a> of how each of these tools fit together for a data mining problem in Hadoop /Mahout ecosystem.

blocks|key|1497643|text|你可以一起使用R，Spark+Hadoop作为完整的开源解决方案。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1497644|1497645|R-提供许多开箱即用的库的统计语言。|ordered-list-item|1497646|Spark-数据处理速度比MR更快的框架，具有机器学习algorithms.|1497647|Hadoop-数据存储，可基于商用硬件进行扩展和健壮。|1497648|1497649|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|N|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|O|8|@]|9|@]|A|$]]|$1|F|3|G|5|E|7|P|8|@]|9|@]|A|$]]|$1|H|3|I|5|E|7|Q|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|R|8|@]|9|@]|A|$]]|$1|K|3|-4|5|6|7|S|8|@]|9|@]|A|$]]]|L|$]]

You can use R, Spark Hadoop together as complete open source solution. 

<ol>
<li>R- Statistical language which provides many libraries out of box. </li>
<li>Spark- framework for data processing faster then MR with machine learning algorithms.</li>
<li>Hadoop- Data storage which is scalable and robust based on commodity hardware.</li>
</ol>

blocks|key|754812|text|您必须在Hadoop生态系统中使用不同的工具，这取决于它们的优势。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|754813|Hive和Hbase很适合处理结构化数据|offset|length|754814|Sqoop用于从传统的关系型数据库Oracle、SQL+Server等导入结构化数据。|754815|Flume用于处理非结构化数据。|754816|您可以使用内容管理系统来处理非结构化数据&半结构化数据-+to或Peta字节的数据。如果您正在存储非结构化数据，我更喜欢将数据存储在内容管理系统中，并使用NoSQL数据库中的元数据信息，如HBASE+(例如Image+Id+,+MD5SUM+of+image)|style|BOLD|CODE|754817|要处理大数据流，您可以使用Pig|754818|Spark是一个用于Hadoop数据的快速通用计算引擎。Spark提供了一个简单且富有表现力的编程模型，该模型支持广泛的应用程序，包括ETL、机器学习、流处理和图形计算|754819|了解一下Hadoop中的Structured+Data和Un-Structured+data处理|754820|看看完整的hadoop+ecosystem和这个SE+question|754821|entityMap|0|LINK|mutability|MUTABLE|url|https://hive.apache.org/|1|https://hbase.apache.org/|2|http://sqoop.apache.org/|3|https://flume.apache.org/|4|https://pig.apache.org/|5|http://spark.apache.org/|6|http://www.dummies.com/how-to/content/structured-data-storage-and-processing-in-hadoop.html|7|http://www.dummies.com/how-to/content/unstructured-data-in-a-big-data-environment.html|8|https://hadoopecosystemtable.github.io/|9|https://stackoverflow.com/questions/13911501/when-to-use-hadoop-hbase-hive-and-pig/33433532#33433532^0|0|0|4|0|5|5|1|0|0|5|2|0|0|5|3|0|25|5|2M|5|2V|Q|0|D|3|4|0|0|5|5|0|C|F|6|S|I|7|0|C|9|8|O|B|9|0^^$0|@$1|2|3|4|5|6|7|1M|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1N|8|@]|9|@$D|1O|E|1P|1|1Q]|$D|1R|E|1S|1|1T]]|A|$]]|$1|F|3|G|5|6|7|1U|8|@]|9|@$D|1V|E|1W|1|1X]]|A|$]]|$1|H|3|I|5|6|7|1Y|8|@]|9|@$D|1Z|E|20|1|21]]|A|$]]|$1|J|3|K|5|6|7|22|8|@$D|23|E|24|L|M]|$D|25|E|26|L|M]|$D|27|E|28|L|N]]|9|@]|A|$]]|$1|O|3|P|5|6|7|29|8|@]|9|@$D|2A|E|2B|1|2C]]|A|$]]|$1|Q|3|R|5|6|7|2D|8|@]|9|@$D|2E|E|2F|1|2G]]|A|$]]|$1|S|3|T|5|6|7|2H|8|@]|9|@$D|2I|E|2J|1|2K]|$D|2L|E|2M|1|2N]]|A|$]]|$1|U|3|V|5|6|7|2O|8|@]|9|@$D|2P|E|2Q|1|2R]|$D|2S|E|2T|1|2U]]|A|$]]|$1|W|3|-4|5|6|7|2V|8|@]|9|@]|A|$]]]|X|$Y|$5|Z|10|11|A|$12|13]]|14|$5|Z|10|11|A|$12|15]]|16|$5|Z|10|11|A|$12|17]]|18|$5|Z|10|11|A|$12|19]]|1A|$5|Z|10|11|A|$12|1B]]|1C|$5|Z|10|11|A|$12|1D]]|1E|$5|Z|10|11|A|$12|1F]]|1G|$5|Z|10|11|A|$12|1H]]|1I|$5|Z|10|11|A|$12|1J]]|1K|$5|Z|10|11|A|$12|1L]]]]

You have to use different tools in Hadoop ecosystem depending on their strengths. 

<a href="https://hive.apache.org/" rel="nofollow noreferrer">Hive</a> and <a href="https://hbase.apache.org/" rel="nofollow noreferrer">Hbase</a> are good to handle structured data

<a href="http://sqoop.apache.org/" rel="nofollow noreferrer">Sqoop</a> is used to import structured data from traditional RDBMS database Oracle, SQL Server etc.

<a href="https://flume.apache.org/" rel="nofollow noreferrer">Flume</a> is used for processing Un-structured data. 

You can use Content Management System to process Un-structured data &amp; Semi-Structured data - Tera Or Peta bytes of data. If you are storing un-structured data, I prefer to store the data in CMS and use meta data information in NoSQL database like HBASE (e.g. <code>Image Id , MD5SUM of image</code>)

To process Big data streaming, you can use <a href="https://pig.apache.org/" rel="nofollow noreferrer">Pig</a>

<a href="http://spark.apache.org/" rel="nofollow noreferrer">Spark</a> is a fast and general compute engine for Hadoop data. Spark provides a simple and expressive programming model that supports a wide range of applications, including ETL, machine learning, stream processing, and graph computation

Have a look at <a href="http://www.dummies.com/how-to/content/structured-data-storage-and-processing-in-hadoop.html" rel="nofollow noreferrer">Structured Data</a> and <a href="http://www.dummies.com/how-to/content/unstructured-data-in-a-big-data-environment.html" rel="nofollow noreferrer">Un-Structured data</a> handling in Hadoop

Have a look at complete hadoop <a href="https://hadoopecosystemtable.github.io/" rel="nofollow noreferrer">ecosystem</a> and this <a href="https://stackoverflow.com/questions/13911501/when-to-use-hadoop-hbase-hive-and-pig/33433532#33433532">SE question</a>

blocks|key|1501098|text|这取决于您的应用程序。您需要了解hive、pig、hbase的用途，然后才能找出它们在您的应用程序中的确切位置。这些都是创建一个特定的原因，你需要理解简单的谷歌会得到结果|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1501099|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

depends on your application. you need to understand purpose of hive,pig, hbase and then you can figure out where exactly they fit in your application. these are created a specific reasons that you need to understand simple google will get the results

blocks|key|1497708|text|HDFS是一个分布式存储系统，用于转储数据以供进一步分析。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1497709|蜂巢/小猪/MR/Spark/Scala等....是用于分析数据的工具。实际上，你可以用其中的任何一个来写你的算法。你不能仅仅通过Pig/Hive/Hbase就达到100%25。你应该知道如何编写Map+Reduce算法，并且需要将它们导入到Hive/Pig中。|1497710|1497711|+ETL工具：+Pig+(脚本语言)+Hive+(类似SQl的结构化数据查询语言)+HBASE对于非结构化数据，可以实现实时数据分析。MapReduce是按部就班地操作，而Spark则是一举操作整个数据集。hadoop+:从RDDMS导入/导出数据Flume:将流数据导入hadoop：机器学习算法工具|blockquote|offset|length|style|BOLD|1497712|1497713|1497714|Hadoop权威指南对于初学者来说是很好的入门指南。|1497715|entityMap^0|0|0|0|1|6|3G|6|0|0|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|S|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|T|8|@]|9|@]|A|$]]|$1|E|3|F|5|G|7|U|8|@$H|V|I|W|J|K]|$H|X|I|Y|J|K]]|9|@]|A|$]]|$1|L|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|10|8|@]|9|@]|A|$]]|$1|N|3|O|5|6|7|11|8|@]|9|@]|A|$]]|$1|P|3|-4|5|6|7|12|8|@]|9|@]|A|$]]]|Q|$]]

HDFS is a distributed storage system to dump your data for further analytics. 

Hive/Pig/MR/Spark/Scala etc.... are tools for analyzing the data. You actually write your algorithms in any of these. You can't achieve 100% just only by Pig/Hive/Hbase. You should know how to write Map Reduce algorithms and need to import these into Hive/Pig.

<blockquote>
 ETL tools: 
 Pig (Scripting language)
 Hive (SQl like query language for structured data)
 HBASE for Unstructured data you can achieve real time data analyzation. While MapReduce operates in steps, Spark operates on the
 whole data set in one fell swoop.
 Sqoop : Import/Export data from RDDMS
 Flume: Import streaming data to hadoop
 Mahout: Machine learning algorith tool
</blockquote>

Hadoop Definitive guide is good to start for beginners.

I'm trying to practice some data mining algorithms using hadoop. Can I do this with HDFS alone, or do I need to use the sub-projects like hive/hbase/pig?

Hadoop beginners

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没有提供实际的解决方法或示例。

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我正在尝试使用hadoop来练习一些数据挖掘算法。我可以单独使用HDFS来做这件事吗，或者我需要使用hive/hbase/pig这样的子项目吗？

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