hadoop mapreduce tutorial

. getnumreducetasks (); jobcontext context = New Jobcontext (job, jobid ); // Check whether the output directory exists. If yes, an error is returned. Org. Apache. hadoop. mapreduce. outputformat // Create the splits for the job Log. debug (" Creating splits "+ FS. makequalified (submitsplitfile )); Int Maps = writenewsplits (context, submitsplitfile ); /// Determine the split Information Job. Set (" Mapred

Configuring MapReduce>configuration>configuration>Plus this.configuration> property > name>Mapreduce.framework.namename> value>Yarnvalue> Property >configuration>And then configure it inside the yarn-site.xml.configuration> -- property > name>Yarn.resourcemanager.hostnamename> value>Hadoop1value> Property > property > name>Yarn.nodemanager.aux-servicesname> value>Mapreduce_shufflevalue>

overload the protected function, such as issplitable (), which is used to determine whether you can slice a block and return it by default to true, indicating that as long as the data block is larger than the HDFS block size, Then it will be sliced.But sometimes you don't want to slice a file, such as when some binary sequence files cannot be sliced, you need to overload the function to return FALSE. when using Fileinputformat, your primary focus should be on the decomposition of data blocks

Hadoop Reading Notes series article: http://blog.csdn.net/caicongyang/article/category/2166855The role of the 1.MapReduce counter statistics map, reduce, and combiner the number of executions, you can easily judge the code execution Flow 2. MapReduce comes with a counter14/11/26 22:28:51 INFO mapred. JOBCLIENT:COUNTERS:1914/11/26 22:28:51 INFO mapred. Jobclient:f

The design idea of MapReduceThe main idea is divide and conquer (divide and conquer), divide and conquer the algorithm. It is a map process to divide a big problem into small problems and then execute them on each node in the cluster. After the map process is over, there is a ruduce process that brings together the results of all the map phase outputs. Steps to write a mapreduce program: 1. Turn the problem into a

0 Basic Learning Hadoop to get started work line guide beginner: Hive and MapReduce: http://www.aboutyun.com/thread-7567-1-1.htmlMapReduce Learning Catalog SummaryMApreduce Learning Guide and Troubleshooting summary : http://www.aboutyun.com/thread-7091-1-1.htmlWhat is map/reduce:http://www.aboutyun.com/thread-5541-1-1.htmlMapreduce whole working mechanism diagram: http://www.aboutyun.com/thread-5641-1-1.h

As input When the compressed file is MapReduce input, MapReduce will automatically extract the corresponding codec from the extension. As output When the MapReduce output file requires compression, you can change mapred.output.compress to True, Mapped.output.compression.codec the class name for the codec you want to use Yes, of course you can specify in the c

Original article link Mapreduce has gone through a thorough overhaul in the hadoop-0.23, and now we have a new framework called mapreduce2.0 (mrv2) or yarn. The basic concept of mrv2 is to split two main functions (resource management and Job Scheduling/monitoring) in jobtracker into separate daemon processes. The idea is to have a global resourcemaager (RM) and the applicationmaster (AM) corresponding to

, InterruptedException {WordCountMapper mapper = new WordCountMapper();Text value = new Text("hello");org.apache.hadoop.mapreduce.Mapper.Context context = mock(Context.class);mapper.map(null, value, context);verify(context).write(new Text("hello"), new IntWritable(1));}@Testpublic void processResult() throws IOException, InterruptedException {WordCountReducer reducer = new WordCountReducer();Text key = new Text("hello");// {"hello",[1,1,2]}Iterable values = Arrays.asList(new IntWritable(1),new I

Reason:Hadoop-eclipse-plugin-2.7.3.jar compiled JDK versions are inconsistent with the JDK version used by Eclipse startup.Solution One :Modify the Myeclipse.ini file to resolve it. D:/java/myeclipse/common/binary/com.sun.java.jdk.win32.x86_1.6.0.013/jre/bin/client/jvm.dll to: D:/Program Files ( x86)/java/jdk1.7.0_45/jre/bin/client/jvm.dlljdk1.7.0_45 version of the JDK for your own installationIf it is not valid, check that the Hadoop version set in t

Query source, you can draw Hadoop 1.x mapreduce default driver configuration: Package org.dragon.hadoop.mr; Import org.apache.hadoop.conf.Configuration; Import Org.apache.hadoop.fs.Path; Import org.apache.hadoop.io.LongWritable; Import Org.apache.hadoop.io.Text; Import Org.apache.hadoop.mapreduce.Job; Import Org.apache.hadoop.mapreduce.Mapper; Import Org.apache.hadoop.mapreduce.Reducer; Import Org.apache.h

The example Terasort in Hadoop is an example of sorting using Mapredue. This article references and simplifies this example: The basic idea of sequencing is to take advantage of the automatic sequencing of MapReduce, in Hadoop, from the map to the reduce phase, the map structure will be assigned to each key according to the hash value of each reduce, wherein in r

HDFs = Mypath.getfilesystem (conf);//Get File systemif (Hdfs.isdirectory (MyPath)){//If this output path exists in the file system, delete theHdfs.delete (MyPath, true);} Job Wcjob = new Job (conf, "WC");//Build a Job object named TestanagramSet the jar package for the classes that are used by the entire jobWcjob.setjarbyclass (Wcrunner.class);Mapper and reducer classes used by this jobWcjob.setmapperclass (Wcmapper.class);Wcjob.setreducerclass (Wcreducer.class);Specify the output data kv type

Configuring MapReduceconfiguration>configuration>Plus this.configuration> property > name>Mapreduce.framework.namename> value>Yarnvalue> Property >configuration>And then configure it inside the yarn-site.xml.configuration> -- property > name>Yarn.resourcemanager.hostnamename> value>Hadoop1value> Property > property > name>Yarn.nodemanager.aux-servicesname> value>Mapreduce_shufflevalue> Property > property > name>Yarn.n

based on the recommended configuration of Horntonworks, a common memory allocation scheme for various components on Hadoop cluster is given. The right-most column of the scenario is a 8G VM allocation scheme that reserves 1-2g memory to the operating system, assigns 4G to Yarn/mapreduce, and of course includes hive, and the remaining 2-3g is reserved for hbase when it is necessary to use HBase.

Hadoop Reading Notes series article:http://blog.csdn.net/caicongyang/article/category/2166855 (series of articles will be gradually trimmed to complete, add data file format expected related comments)1. Description:From the given file, find the maximum of 100 values, given the data file format as follows:5331656517800292911374982668522067918224212228227533691229525338221001067312284316342740518015 ...2. Use the TreeMap class in the code below, so writ

(Firstpartitioner.class);//partition functionJob.setsortcomparatorclass (Keycomparator.class);//This course does not have custom sortcomparator, but instead uses Intpair's own sortJob.setgroupingcomparatorclass (Groupingcomparator.class);//Group functionJob.setmapoutputkeyclass (Intpair.class);Job.setmapoutputvalueclass (Intwritable.class);Job.setoutputkeyclass (Text.class);Job.setoutputvalueclass (Intwritable.class);Job.setinputformatclass (Textinputformat.class);Job.setoutputformatclass (Text

Tags: hadoop Business Logic In fact, it is very simple to input two files, one as the basic data (student information file) and the other is the score information file.Student Information File: stores student data, including student ID and Student name Score data: stores scores of students, including student IDs, subjects, and scores. We will use M/R to associate data based on student IDs. The final result is student name, subject, and score. Analog d

processing classesJob.setmapperclass (Datamapper.class); Job.setreducerclass (datareduce.class); //Setting the output key-value data typeJob.setoutputkeyclass (Text.class); Job.setoutputvalueclass (Text.class); //submit the job and wait for it to completeSystem.exit (Job.waitforcompletion (true) ? 0:1); } }Add one point: When a file is sliced, it starts a mapper process according to the default 64M data block principle.Example: For example, Data.log has 20M, will start a mapper process, Data

= Mypath.getfilesystem (conf);if (Hdfs.isdirectory (MyPath)){Hdfs.delete (MyPath, true);}@SuppressWarnings ("deprecation")Job Job = new Job (conf, "gender");//Create a new taskJob.setjarbyclass (Gender.class);//Main classJob.setmapperclass (pcmapper.class);//mapperJob.setreducerclass (pcreducer.class);//reducerJob.setpartitionerclass (Myhashpartitioner.class);Job.setpartitionerclass (Pcpartitioner.class);//Set Partitioner classJob.setnumreducetasks (3);//reduce number set to 3Job.setmapoutputke