This article covers some JVM principles and Java bytecode Directives, recommend interested readers to read a classic book on the JVM, Deep Java Virtual Machine (2nd edition), and compare it with the IL assembly directives I described in ". NET 4.0 object-oriented Programming". Believe that readers will have some inspiration. It is one of the most effective learning methods to compare the similarities and differences of two similar things carefully. In the future, I will also release other articles on personal blog, hoping to help readers of the book broaden their horizons, inspire thinking, we discuss technology together ...
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 ...
Knowing how the MapReduce program works, the next step is to implement it through code. We need three things: a map function, a reduce function, and some code to run the job. The map function is represented by the Mapper interface implementation, which declares a map () method. Example 2-3 shows our map function implementation. Example 2-3. Find the highest temperature of the mapper import java.io.IOException; &http ...
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.
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 ...
Foreword in the first article of this series: using Hadoop for distributed parallel programming, part 1th: Basic concepts 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 a parallel program based on Hadoop in a stand-alone and pseudo distributed environment (with multiple process simulations on a single machine). In the second article of this series: using Hadoop for distributed parallel programming, ...
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