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This article is my second time reading Hadoop 0.20.2 notes, encountered many problems in the reading process, and ultimately through a variety of ways to solve most of the. 　　Hadoop the whole system is well designed, the source code is worth learning distributed students read, will be all notes one by one post, hope to facilitate reading Hadoop source code, less detours. 1 serialization core Technology The objectwritable in 0.20.2 version Hadoop supports the following types of data format serialization: Data type examples say ...

After completing the four basic learning steps of Apache Cassandra, you can try the actual code. If necessary, it is recommended to briefly review: Apache Cassandra Learning Step (1) Apache Cassandra Learning Ste ...

Objective This tutorial provides a comprehensive overview of all aspects of the Hadoop map-reduce framework from a user perspective. Prerequisites First make sure that Hadoop is installed, configured, and running correctly. See more information: Hadoop QuickStart for first-time users. Hadoop clusters are built on large-scale distributed clusters. Overview Hadoop Map-reduce is a simple software framework, based on which applications are written to run on large clusters of thousands of commercial machines, and with a reliable fault tolerance ...

Objective This tutorial provides a comprehensive overview of all aspects of the Hadoop map/reduce framework from a user perspective. Prerequisites First make sure that Hadoop is installed, configured, and running correctly. See more information: Hadoop QuickStart for first-time users. Hadoop clusters are built on large-scale distributed clusters. Overview Hadoop Map/reduce is a simple software framework, based on which applications can be run on a large cluster of thousands of commercial machines, and with a reliable fault-tolerant ...

program example and Analysis Hadoop is an open source distributed parallel programming framework that realizes the MapReduce computing model, with the help of Hadoop, programmers can easily write a distributed parallel program, run it on a computer cluster, and complete the computation of massive data. In this article, we detail how to write a program based on Hadoop for a specific parallel computing task, and how to compile and run the Hadoop program in the ECLIPSE environment using IBM MapReduce Tools. Preface ...

In the past, we have introduced some principles of software development, such as the 10 commandments of high quality code and the UNIX design principles described in the UNIX legend (next article). I believe that you can learn from middle school some knowledge of design principles, as I said in the "How Do I Recruit procedures", a good programmer usually consists of its operational skills, knowledge level, experience level and ability four aspects. Here I would like to talk about some of the principles of design, I think these things belong to the long-term experience summed up knowledge. These principles should be understood by every programmer. But...

Foreword in an article: "Using Hadoop for distributed parallel programming the first part of the basic concept and installation Deployment", introduced the MapReduce computing model, Distributed File System HDFS, distributed parallel Computing and other basic principles, and detailed how to install Hadoop, how to run based on A parallel program for Hadoop. In this article, we will describe how to write parallel programs based on Hadoop and how to use the Hadoop ecli developed by IBM for a specific computing task.

Our demand is to count the number of occurrences of each word in a file after the IK participle, and then to sort by descending the number of occurrences. 　　That is, high-frequency word statistics. Because Hadoop cannot do anything with the result after reduce, it can only be divided into two jobs, the first job count, and the second job to sort the results of the first job. The first job is the simplest example of Hadoop countwords, I would say is to use Hadoop to sort the results. Suppose the results of the first job are output as follows: ...

In MapReduce, shuffle is more like the inverse process of shuffling, which refers to "disrupting" the random output of the map end according to the specified rules into data with certain rules so that the reduce end can receive and process it.