mapreduce concepts

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Hadoop Hdfs&mapreduce Core Concepts

1. HDFS (Distributed File system system)1.1, NameNode: (Name node)HDFs DaemonHow the record files are partitioned into chunks, and on which nodes the data blocks are storedCentralized management of memory and I/Ois a single point, failure will cause the cluster to crash1.2, Secondarynamenode (auxiliary name node): Failure to manually set up to achieve cluster crash problemAuxiliary daemon for monitoring HDFs statusEach cluster has aCommunicate with Namenode to save HDFs metadata snapshots on a r

Data-intensive Text Processing with mapreduce chapter 2nd: mapreduce BASICS (1)

executes the processing program on the node, improving the efficiency. This chapter mainly introduces the mapreduce programming model and distributed file system.. 2.1SectionIntroduce functional programming FP (functional programming), which is inspired by mapreduce design; 2.2SectionDescribes the basic programming models of Mapper, reducer, and mapreduce; 2.3Se

HDFs design ideas, HDFs use, view cluster status, Hdfs,hdfs upload files, HDFS download files, yarn Web management Interface Information view, run a mapreduce program, MapReduce Demo

26 Preliminary use of clusterDesign ideas of HDFsL Design IdeasDivide and Conquer: Large files, large batches of files, distributed on a large number of servers, so as to facilitate the use of divide-and-conquer method of massive data analysis;L role in Big Data systems:For a variety of distributed computing framework (such as: Mapreduce,spark,tez, ... ) Provides data storage servicesL Key Concepts: File Cu

Data-intensive Text Processing with mapreduce chapter 3rd: mapreduce Algorithm Design (1)

Great deal. I was supposed to update it yesterday. As a result, I was too excited to receive my new focus phone yesterday and forgot my business. Sorry! Directory address for this book Note: http://www.cnblogs.com/mdyang/archive/2011/06/29/data-intensive-text-prcessing-with-mapreduce-contents.htmlIntroduction Mapreduce is very powerful because of its simplicity. Programmers only need to prepare the followin

MapReduce programming Series 7 MapReduce program log view, mapreduce log

MapReduce programming Series 7 MapReduce program log view, mapreduce log First, to print logs without using log4j, you can directly use System. out. println. The log information output to stdout can be found at the jobtracker site. Second, if you use System. out. println to print the log when the main function is started, you can see it directly on the console.

The work flow of MapReduce and the next generation of Mapreduce--yarn

Learn the difference between mapreduceV1 (previous mapreduce) and mapreduceV2 (YARN) We need to understand MapreduceV1 's working mechanism and design ideas first.First, take a look at the operation diagram of the MapReduce V1The components and functions of the MapReduce V1 are:Client: Clients, responsible for writing MapRedu

Data-intensive Text Processing with mapreduce chapter 2nd: mapreduce BASICS (2)

Directory address for this book Note: http://www.cnblogs.com/mdyang/archive/2011/06/29/data-intensive-text-prcessing-with-mapreduce-contents.html2.3 execution framework The greatest thing about mapreduce is that it separates parallel algorithm writing.WhatAndHow(You only need to write a program without worrying about how to execute it)The execution framework makes great contributions to this point: it handl

Data-intensive Text Processing with mapreduce Chapter 3 (6)-mapreduce algorithm design-3.5 relational joins)

user data. After years of development, hadoop has become a popular data warehouse. Hammerbacher [68], talked about Facebook's building of business intelligence applications on Oracle databases, and later gave up, because he liked to use his own hadoop-based hive (now an open-source project ). Pig [114] is a platform built with hadoop for massive data analysis and can process structured data like semi-structured data. It was originally developed by Yahoo, but now it is an open-source project. If

Data-intensive Text Processing with mapreduce Chapter 3 (2)-mapreduce algorithm design-3.1 partial aggregation

3.1 local Aggregation) In a data-intensive distributed processing environment, interaction of intermediate results is an important aspect of synchronization from processes that generate them to processes that consume them at the end. In a cluster environment, except for the embarrassing parallel problem, data must be transmitted over the network. In addition, in hadoop, the intermediate result is first written to the local disk and then sent over the network. Because network and disk factors ar

[MapReduce] Google Troika: Gfs,mapreduce and BigTable

  Disclaimer: This article is reproduced from the blog Development team Blog, respect for the original work. This article is suitable for the study of distributed systems, as a background introduction to read. When it comes to distributed systems, you have to mention Google's Troika: Google Fs[1],mapreduce[2],bigtable[3].Although Google did not release the source code for the three products, he released detailed design papers for the three products. I

Data-intensive Text Processing with mapreduce chapter 2nd: mapreduce BASICS (3)

Directory address for this book Note: http://www.cnblogs.com/mdyang/archive/2011/06/29/data-intensive-text-prcessing-with-mapreduce-contents.html 2.5 Distributed File System HDFSTraditional large-scale data processing problems from the perspective of data placementPrevious focusProcessing. However, if there is no data, there is no way to deal with it.In traditional cluster architecture (such as HPC), computing and storage are two separate components..

MapReduce is one of the first steps to achieve Word Frequency Statistics, mapreduce Word Frequency

MapReduce is one of the first steps to achieve Word Frequency Statistics, mapreduce Word Frequency Original podcast. If you need to reprint it, please indicate the source. Address: http://www.cnblogs.com/crawl/p/7687120.html Certificate ---------------------------------------------------------------------------------------------------------------------------------------------------------- A large number of

Detailed description of the work principle of mapreduce

be used as a reducing function to return the sum of the values of the input data list.Put them together in the MapReduce.The MapReduce framework for Hadoop uses the above concepts to handle large-scale data information. The MapReduce program has two components: one implements the Mapper and the other implements the reducer. The mapper and reducer terms described

MapReduce understanding-in-depth understanding of MapReduce

The previous blogs focused on Hadoop's storage HDFs, followed by a few blogs about Hadoop's computational framework MapReduce. This blog mainly explains the specific implementation process of the MapReduce framework, as well as the shuffle process, of course, this technical blog has been particularly numerous and written very good, I wrote a blog before the relevant reading, benefited. The references to som

Hadoop technology Insider: in-depth analysis of mapreduce Architecture Design and Implementation Principles

mapreduce design goals/282.3 mapreduce programming model Overview/292.3.1 mapreduce Programming model Overview/292.3.2 mapreduce programming instance/312.4 hadoop basic architecture/322.4.1 HDFS architecture/332.4.2 hadoop mapreduce architecture/342.5 hadoop

Python Development MapReduce Series (ii) Python implementation of MapReduce buckets

line, and the previous part is key, after which it is value. If a "\ t" character is not there, the entire line is treated as a key.2. The sort and partition phases of the MapReduce Shuffler processThe mapper phase, in addition to user code, is most important for the shuffle process, which is the main place where MapReduce takes time and consumes resources because it involves operations such as Disk writes

Data-intensive Text Processing with mapreduce Chapter 3 (3)-mapreduce algorithm design-3.2 pairs (pairs) and stripes (stripes)

3.2 pairs (pair) and stripes (stripe) A common practice of synchronization in mapreduce programs is to adapt data to the execution framework by building complex keys and values. We have covered this technology in the previous chapter, that is, "package" the total number and count into a composite value (for example, pair) from Mapper to combiner and then to Cer. Based on previous publications (54,94), this section describes two common design patterns

Data-intensive Text Processing with mapreduce chapter 3rd: mapreduce Algorithm Design (4)

Directory address for this book Note: http://www.cnblogs.com/mdyang/archive/2011/06/29/data-intensive-text-prcessing-with-mapreduce-contents.html 3.4 secondary sorting Before intermediate results enter CER, mapreduce first sorts these intermediate results and then distributes them. This mechanism is very convenient for reduce operations that depend on the input sequence of intermediate results (in the o

Eclipse Local Run MapReduce console print MapReduce execution progress

In the process of local mapreduce development, it was found that the Eclipse console could not print the progress of the MapReduce job I wanted to see and some parameters before guessing it might have been a log4j problem, and had indeed reported Log4j's warning, and then tried it, It's really a log4j problem.Mainly because I did not configure Log4j.properties, the first new file in the SRC directory, and t

[Spring Data MongoDB] learning notes -- MapReduce, mongodb -- mapreduce

[Spring Data MongoDB] learning notes -- MapReduce, mongodb -- mapreduce Mongodb MapReduce mainly includes two methods: map and reduce. For example, assume that the following three records exist: { "_id" : ObjectId("4e5ff893c0277826074ec533"), "x" : [ "a", "b" ] }{ "_id" : ObjectId("4e5ff893c0277826074ec534"), "x" : [ "b", "c" ] }{ "_id" : ObjectId("4e5ff893c02778

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