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
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
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
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
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.
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
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
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
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
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
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
Original podcast. If you need to reprint it, please indicate the source. Address: http://www.cnblogs.com/crawl/p/7687120.html
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A large number of
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
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
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
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
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
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
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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