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Assume that the cluster is already configured.On the development client Linux CentOS 6.5:A. The client CentOS has an access user with the same name as the cluster: Huser.B.vim/etc/hosts joins the Namenode and joins the native IP.-------------------------1. Install Hadoop cluster with the same version of JDK, Hadoop,2.Eclipse compile and install the same version of Hadoo
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
() method in Comparator is an object -based comparison.In the byte-based comparison method, there are six parameters, all of a sudden blurred: Params: * @param arg0 represents the first byte array to participate in a comparison* @param arg1 indicates the starting position of the first byte array to participate in the comparison* @param arg2 represents the offset of the first byte array participating in the comparison** @param arg3 represents the second byte array to participate in
Below, is version 1.Hadoop MapReduce Programming API Entry Series Mining meteorological data version 1 (i)This blog post includes, for real production development, very important, unit testing and debugging code. Here is not much to repeat, directly put on the code.Mrunit FrameMrunit is a Cloudera company dedicated to Hadoop
(" Yarn.resourcemanager.hostname "," Node7 ");Execute Debug As, Java application in eclipse;Server environment (for a real enterprise operating environment)1, directly run the jar package method, refer to: http://www.cnblogs.com/raphael5200/p/5223684.html2, the local direct call, the execution of the process on the server (real Enterprise operating environment)A, the MR Program packaging (jar), directly into a local directory, I put in the E:\\jar\\wc.jarb, modify the source code of HadoopCopy
For example, we have written a mapred program as follows: package com.besttone.mapred;import java.io.IOException;import java.util.StringTokenizer;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;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.hadoop.mapreduce.
Mapreduce has a php interface. Ask who knows the underlying source code. If you want to learn, some php and java interactive mapreduce has a php interface. Ask who knows the underlying source code, want to learn There may be some php and java interactions. Reply content: Mapreduce has a php interface. Ask who knows the underlying source code and want to lear
. Jobclient:cpu Time Spent (ms) =1530 13/11/20 16:10:16 INFO mapred. Jobclient:total committed heap usage (bytes) =504758272 13/11/20 INFO 16:10:16. Jobclient:combine input records=4 13/11/20 16:10:16 INFO mapred. jobclient:split_raw_bytes=111 13/11/20 16:10:16 INFO mapred. Jobclient:reduce input records=4 13/11/20 16:10:16 INFO mapred. Jobclient:reduce input groups=4 13/11/20 16:10:16 INFO mapred. Jobclient:combine output records=4 13/11/20 16:10:16 INFO mapred. Jobclient:physical memory (bytes
The first to implement MapReduce is to rewrite two functions, one is map and the other is reducemap(key ,value)The map function has two parameters, one is key, one is valueIf your input type is Textinputformat (default), then the input of your map function will be: Key: The offset of the file (that is, the values in the location of the file) Value: This is a line of string (Hadoop takes each line o
) {System.err.println ("Usage:wordcount"); System.exit (2); } /**Create a job, name it to track the performance of the task **/Job Job=NewJob (conf, "word count"); /**when running a job on a Hadoop cluster, you need to package the code into a jar file (Hadoop distributes the file in the cluster), set a class through the setjarbyclass of the job, and Hadoop
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.l
org.apache.hadoop.ipc.Client:Retrying Connect to server:0.0.0.0/0.0.0.0:8031. Already tried 7 time (s); Retry policy is Retryuptomaximumcountwithfixedsleep (maxretries=10, sleeptime=1000 MILLISECONDS) 2017-06-05 09:49:46,472 INFO org.apache.hadoop.ipc.Client:Retrying Connect to server:0.0.0.0/0.0.0.0:8031. Already tried 8 time (s); Retry policy is Retryuptomaximumcountwithfixedsleep (maxretries=10, sleeptime=1000 MILLISECONDS) 2017-06-05 09:49:47,474 INFO org.apache.hadoop.ipc.Client:Retrying C
The running process of MapReduce The running process of MapReduceBasic concepts: Jobtask: To complete a job, it will be divided into a number of task,task and divided into Maptask and Reducetask Jobtracker Tasktracker Hadoop MapReduce ArchitectureThe role of Jobtracker Job scheduling Assign tasks, monitor task execution progress Moni
Write the MapReduce program to implement the Kmeans algorithm. Our idea may be1. centroid after the second iteration2. Map. Calculates the distance between each centroid and sample, obtains the centroid with the shortest distance from the sample, takes this centroid as the key, the sample as value, the output3. In reduce, the input key is the centroid, value is the other sample, then again compute the cluster center, put the cluster center into a all
MapReduce has PHP interface, ask the bottom source who knows where, want to learn There will probably be some interaction between PHP and Java. Reply content: MapReduce has PHP interface, ask the bottom source who knows where, want to learnThere will probably be some interaction between PHP and Java. Using PHP to write a mapreduce program for
Transferred from http://www.ptbird.cn/mapreduce-tempreture.html I. Description of requirements 1, data file description There are some data files stored in the HDFs in the form of text, as shown in the following example: In the middle of the date and time is a space, as a whole, indicating the detection of site monitoring time, followed by the detection of temperature, the middle through the tab \ t separa
PriviledgedActionException as:man (auth:SIMPLE) cause:java.io.IOException: Cannot initialize Cluster. Please check your configuration for mapreduce.framework.name and the correspond server addresses.2014-09-24 12:57:41,567 ERROR [RunService.java:206] - [thread-id:17 thread-name:Thread-6] threadId:17,Excpetion:java.io.IOException: Cannot initialize Cluster. Please check your configuration for mapreduce.framework.name and the correspond server addresses.at org.apache.hadoop.mapreduce.Cluster.initi
procedureMake the Java program into a jar package and upload it to the Hadoop server (any Namenode node on the boot)3. Data sourceThe data source is as follows:Hadoop java text hdfstom Jack Java textjob hadoop ABC lusihdfs Tom textPut the content in a TXT file and put it in HDFs/usr/input (under HDFs, not Linux), and you can upload it using the Eclipse plugin:4. Execute JAR Package# fully qualified name
. ManagementThe Fair Scheduler provides support for two mechanisms for execution-time management: By editing the allocation file, you can change the minimum share, limit, weight, pre-occupancy time difference, and queue scheduling policy.The scheduler will reload the file 10-15 seconds after it knows it has changed. The current app, queue, and fair share can be checked through the ResourceManager Web interface, which is http://ResourceManager URL/cluster/scheduler.Each of the following qu
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
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