Java Return Multiple Values From Method

This paper is an excerpt from the book "The Authoritative Guide to Hadoop", published by Tsinghua University Press, which is the author of Tom White, the School of Data Science and engineering, East China Normal University. This book begins with the origins of Hadoop, and integrates theory and practice to introduce Hadoop as an ideal tool for high-performance processing of massive datasets. The book consists of 16 chapters, 3 appendices, covering topics including: Haddoop;mapreduce;hadoop Distributed file system; Hadoop I/O, MapReduce application Open ...

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 ...

The road to computer science is littered with things that will become "the next big thing". Although many niche languages do find some place in scripts or specific applications, C (and its derivatives) and Java languages are hard to replace. But Red Hat's Ceylon seems to be an interesting combination of some language features, using the well-known C-style syntax, but it also provides object-oriented and some useful functional support in addition to simplicity. Take a look at Ceylon and see this future VM ...

To use Hadoop, data consolidation is critical and hbase is widely used. In general, you need to transfer data from existing types of databases or data files to HBase for different scenario patterns. The common approach is to use the Put method in the HBase API, to use the HBase Bulk Load tool, and to use a custom mapreduce job. The book "HBase Administration Cookbook" has a detailed description of these three ways, by Imp ...

This is the second of the Hadoop Best Practice series, and the last one is "10 best practices for Hadoop administrators." Mapruduce development is slightly more complicated for most programmers, and running a wordcount (the Hello Word program in Hadoop) is not only familiar with the Mapruduce model, but also the Linux commands (though there are Cygwin, But it's still a hassle to run mapruduce under windows ...

About pi Everyone is familiar with: we learn from the textbook to as early as more than 1000 years ago, zu Pi to 3.1415926 to 3.1415927 ... After the birth of the computer, calculate pi is used to detect computer hardware performance, day and night burning CPU to see if there is a problem ... Others also want to see if there is a rule behind this mysterious figure of infinite extension, to discover some cosmic secrets ... Mention PI, cannot mention Fabrice Bellard, he is considered a computer genius, in the industry has ...

The most interesting place for Hadoop is the job scheduling of Hadoop, and it is necessary to have a thorough understanding of Hadoop's job scheduling before formally introducing how to build Hadoop. We may not be able to use Hadoop, but if the principle of the distributed scheduling is fluent Hadoop, you may not be able to write a mini hadoop~ when you need it: Start Map/reduce is a part for large-scale data processing ...

Hadoop MapReduce is a programming model for data processing that is simple but powerful enough to be designed for parallel processing of big data.

Today, some of the most successful companies gain a strong business advantage by capturing, analyzing, and leveraging a large variety of "big data" that is fast moving. This article describes three usage models that can help you implement a flexible, efficient, large data infrastructure to gain a competitive advantage in your business. This article also describes Intel's many innovations in chips, systems, and software to help you deploy these and other large data solutions with optimal performance, cost, and energy efficiency. Big Data opportunities People often compare big data to tsunamis. Currently, the global 5 billion mobile phone users and nearly 1 billion of Facebo ...

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, ...