understanding mapreduce

Although many books describe the use of mapreduce APIs, they seldom describe how to design a MapReduce application. Mapreduce mainly comes from its simplicity. In addition to preparing input data, programmers only need to operate mapper and reducer. In reality, many problems can be solved using this method. In most cases Although many books describe the use of

1. Analyze the MapReduce job running mechanism 1). Typical MapReduce -- MapReduce1.0 There are four independent entities throughout the process Client: Submit MapReduce JobTracker: Coordinates job running TaskTracker: The task after the job is divided. HDFS: used to share job files between other entities The overall running figure is as follows: A. Submit

First, IntroductionAfter writing the MapReduce task, it was always packaged and uploaded to the Hadoop cluster, then started the task through the shell command, then looked at the log log file on each node, and later to improve the development efficiency, You need to find a direct maprreduce task directly to the Hadoop cluster via ecplise. This section describes how users can finally complete the Eclipse price increase task to the

1. mapper and reducerMapReduce processes data in two stages: map stage and reduce stage. The two stages are completed by the user-developed map function and reduce function, they are also called mapper and reducer respectively. Key-value pairs(Key-value pair) is the basic data structure of MapReduce. The data read and output by mapper and reducer are key-value pairs. In MapReduce, keys and values can be bas

Brief introduction Over the past 20 years, the steady increase in computational power has spawned a deluge of data, which in turn has led to a paradigm shift in computing architectures and large data-processing mechanisms. For example, powerful telescopes in astronomy, particle accelerators in physics, and genome sequencing systems in biology have put massive amounts of data into the hands of scientists. Facebook collects 15TB of data every day into a PB-level data warehouse. Demand for large d

From: http://cloud.csdn.net/a/20111117/307657.html One of the reasons for the success of the mapreduce system is that it provides a simple programming mode for writing code that requires large-scale parallel processing. It is inspired by the functional programming features of Lisp and other functional languages. Mapreduce works well with cloud computing. The key feature of

The traditional MapReduce framework was proposed by Google in 2004 in the paper: "Mapreduce:simplified Data processing on Large clusters", The framework simplifies the process of data processing for data-intensive applications into maps and reduce two phases, when users design distributed programs by implementing map () and reduce () two functions, as well as other details such as data fragmentation, task scheduling, machine fault tolerance, communica

Mongodb mapreduce usage summary, mongodbmapreduce This article is from my blog: mongodb mapreduce usage Summary As we all know, mongodb is a non-relational database. That is to say, each table in the mongodb database exists independently and there is no dependency between the table and the table. In mongodb, apart from various CRUD statements, we also provide the aggregation and