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Tags: MONGO hadoop Distributed File System hive ETLReference article: Mongo Hadoop connector Https://github.com/mongodb/mongo-hadoop wiki https://github.com/mongodb/mongo-hadoop/ Wiki ppt http://www.slideshare.net/
An interesting trend have been developing in the IT landscape over the past few years. Many new technologies develop and immediately latch onto the "Big Data" buzzword. And as older technologies add "Big Data" features in an attempt to keep up with the Joneses, we is seeing a blurring of t He boundaries between various technologies. Say you have search engines such as ElasticSearch or SOLR storing JSON documents, MongoDB storing JSON documents, or a P
There has been an interesting phenomenon in the IT community over the past few years. Many new technologies have emerged and embraced "big data" immediately. A little bit older technology will also add big data to their own features, to avoid falling too far, we see the different technologies of the marginal ambiguity. If you have search engines such as Elasticsearch or SOLR, they store JSON documents, MongoDB has JSON documents, or a bunch of JSON do
/08/01 10:50:17 INFO mapred.JobClient: Job complete: job_201408010921_000814/08/01 10:50:17 INFO mapred.JobClient: Counters: 29......7) View the results of the output[[emailprotected] CompanyJoinAddress]$ hadoop fs -ls CompanyJoinAddress/outputFound 3 items-rw-r--r-- 1 hadoop supergroup 0 2014-08-01 10:50 /user/hadoop/CompanyJoinAddress/output/_SUCCESSdrwxr-xr-
/08/01 10:50:17 INFO mapred.JobClient: Job complete: job_201408010921_000814/08/01 10:50:17 INFO mapred.JobClient: Counters: 29......7) View the results of the output[[emailprotected] CompanyJoinAddress]$ hadoop fs -ls CompanyJoinAddress/outputFound 3 items-rw-r--r-- 1 hadoop supergroup 0 2014-08-01 10:50 /user/hadoop/CompanyJoinAddress/output/_SUCCESSdrwxr-xr-
-15 11:10 /user/hadoop/wordcount/output/_logs-rw-r--r-- 1 hadoop supergroup 41 2014-09-15 11:11 /user/hadoop/wordcount/output/part-r-00000使用 hadoop fs –cat wordcount/output/part-r-00000命令查看输出结果，如下所示：#查看结果输出文件内容[[emailprotected] WordCount]$ hadoop fs -cat wordcount/output/p
There has been an interesting phenomenon in the IT community over the past few years. Many new technologies have emerged and embraced "big data" immediately. A little bit older technology will also add big data to their own features, to avoid falling too far, we see the different technologies of the marginal ambiguity. If you have search engines such as Elasticsearch or SOLR, they store JSON documents, MongoDB has JSON documents, or a bunch of JSON do
Recently consider using Hadoop mapreduce to analyze the data on MongoDB, from the Internet to find some demo, patchwork, finally run a demo, the following process to show youEnvironment Ubuntu 14.04 64bit Hadoop 2.6.4 MongoDB 2.4.9 Java 1.8 Mongo-hadoop
) View HDFs system[[emailprotected] ~] $ hadoop fs -ls /View the Hadoop HDFs file management system through Hadoop fs-ls/commands, as shown in the Linux file system directory. The results shown above indicate that the Hadoop standalone installation was successful. So far, we have not made any changes to the
Hadoop has always been the technology I want to learn, just as the recent project team to do e-mall, I began to study Hadoop, although the final identification of Hadoop is not suitable for our project, but I will continue to study, more and more do not press.The basic Hadoop tutor
To do well, you must first sharpen your tools. This article has built a hadoop standalone version and a pseudo-distributed development environment starting from scratch. It is illustrated in the following figures and involves: 1. Develop basic software required by hadoop; 2. Install each software; 3. Configure the hadoop standalone mode and run the wordco
Reprinted from http://blessht.iteye.com/blog/2095675Hadoop has always been the technology I want to learn, just as the recent project team to do e-mall, I began to study Hadoop, although the final identification of Hadoop is not suitable for our project, but I will continue to study, more and more do not press.The basic Hadoop
on the RDD, such as the classic WordCount program, which operates as shown in the Spark programming model: You can see that spark first abstracted from the file system RDD1, and then by RDD1 through the flatmap operator to RDD2,RDD2 then Reducebykey operator to get RDD3, finally the data in the RDD3 back to the file system, all operations are based on RDD.Iii. Ideas and architectureAfter a lot of thinking, the final decision based on spark technology to build and implement the hospital clinica
on the RDD, such as the classic WordCount program, which operates as shown in the Spark programming model: You can see that spark first abstracted from the file system RDD1, and then by RDD1 through the flatmap operator to RDD2,RDD2 then Reducebykey operator to get RDD3, finally the data in the RDD3 back to the file system, all operations are based on RDD.Iii. Ideas and architectureAfter a lot of thinking, the final decision based on spark technology to build and implement the hospital clinica
MongoDB itself can do some simple statistical work, including its built-in JavaScript-based MapReduce framework, as well as the new statistical framework introduced in the MongoDB 2.2 version. In addition, MongoDB also provides an interface for external statistical tools, which is the Mongodb-
1. Download Hadoop source codeSource code of each Hadoop Member: Just pull it out. Note that only the contents in the trunk directory on SVN are checked-out, for example:Http://svn.apache.org/repos/asf/hadoop/common/trunk,Instead of http://svn.apache.org/repos/asf/hadoop/common,The reason is that the http://svn.apache.
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