Distributed Data Processing Network

For large data, the serial processing method is difficult to meet people's requirements, and now mainly uses parallel computing. The existing parallel computing can be divided into two kinds: fine-grained parallel computation. Here the fine granularity is mainly the instruction or process level, because the GPU has more parallel processing power than the CPU, people put some tasks to the GPU parallel processing, some GPU manufacturers also introduced a user-friendly programming model, such as Nvidia launched the Cuda and so on. • Parallel computation of coarse granularity. Here the coarse granularity refers to the term ...

Hadoop is an open source distributed parallel programming framework that realizes the MapReduce computing model, with the help of Hadoop, programmers can easily write distributed parallel program, run it on computer cluster, and complete the computation of massive data. This paper will introduce the basic concepts of MapReduce computing model, distributed parallel computing, and the installation and deployment of Hadoop and its basic operation methods. Introduction to Hadoop Hadoop is an open-source, distributed, parallel programming framework that can be run on a large scale cluster by ...

Hadoop is an open source distributed parallel programming framework that realizes the MapReduce computing model, with the help of Hadoop, programmers can easily write distributed parallel program, run it on computer cluster, and complete the computation of massive data. This paper will introduce the basic concepts of MapReduce computing model, distributed parallel computing, and the installation and deployment of Hadoop and its basic operation methods. Introduction to Hadoop Hadoop is an open-source, distributed, parallel programming framework that can run on large clusters.

Research on distributed processing of network monitoring information flow based on Hadoop Chen Guoliang A new method of distributed cluster processing based on Hadoop cloud computing framework is proposed for the monitoring of information flow of large data sets in intelligent power grid dispatching system. By analyzing the characteristics of information flow in power grid monitoring system, extracting 3 kinds of critical information flow, using the Distributed File system HDFs and Mapping aggregation model Map/reduce, establishing the distributed processing platform of Cluster group, and realizing the high efficient parallel processing of the monitoring data. Taking the data set of section measurement record of a distribution network as an example.

MapReduce: Simple data processing on large cluster

In recent years, with the emergence of new forms of information, represented by social networking sites, location-based services, and the rapid development of cloud computing, mobile and IoT technologies, ubiquitous mobile, wireless sensors and other devices are generating data at all times, Hundreds of millions of users of Internet services are always generating data interaction, the big Data era has come. In the present, large data is hot, whether it is business or individuals are talking about or engaged in large data-related topics and business, we create large data is also surrounded by the big data age. Although the market prospect of big data makes people ...

Now the investment community has focused on projects such as mobile Internet applications, internet finance and smart-wear devices, which appear to be pigs in a number of outlets. Undeniably, in the Internet and traditional industries constantly penetrate into the depth of the present, each of these projects to promote, will change people's life, are an immeasurable blue sea. But the odd thing is that a lot of reserves, when rich in gold, are just flashing in the torrent of time ahead, and then buried by a din and doubt. This gold mine, which I will elaborate and excavate, is a pig that can really fly ——...

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, ...

The scalability of the system is the main reason for promoting the development of NoSQL movement, including distributed system coordination, failover, resource management and many other features. That makes NoSQL sound like a big basket that can be stuffed with anything. Although the NoSQL movement does not bring fundamental technological changes to distributed data processing, it still leads to extensive research and practice on protocols and algorithms. It is through these attempts to gradually summarize some effective database construction methods. In this article, I will focus on the distributed features of the NoSQL database ...

Hadoop is a Java implementation of Google MapReduce. MapReduce is a simplified distributed programming model that allows programs to be distributed automatically to a large cluster of ordinary machines. Just as Java programmers can do without memory leaks, MapReduce's run-time system solves the distribution details of input data, executes scheduling across machine clusters, handles machine failures, and manages communication requests between machines. This ...