mapreduce algorithm in hadoop

Hadoop mapreduce sequencing principle Hadoop Case 3 Simple problem----sorting data (Entry level)"Data Sorting" is the first work to be done when many actual tasks are executed,such as student performance appraisal, data indexing and so on. This example and data deduplication is similar to the original data is initially processed, for further data operations to la

The command to run the MapReduce jar package is the Hadoop jar **.jar The command to run the jar package for the normal main function is Java-classpath **.jar Because I have not known the difference between the two commands, so I stubbornly use Java-classpath **.jar to start the MapReduce. Until today there are errors. Java-classpath **.jar is to make the jar pac

Looking at the trends in the industry's use of distributed systems and the long-term development of the Hadoop framework, MapReduce's jobtracker/tasktracker mechanism requires massive tweaks to fix its flaws in scalability, memory consumption, threading model, reliability, and performance. The Hadoop development team has done some bug fixes over the past few years, but the cost of these fixes has increased

MapReduce is the core framework for completing data computing tasks in Hadoop1. MapReduce constituent Entities(1) Client node: The MapReduce program and the Jobclient instance object are run on this node, and the MapReduce job is submitted.(2) Jobtracker: Coordinated scheduling, master node, one

, and is pre-sorted for efficiency considerations.Each map task has a ring memory buffer that stores the output of the task. By default,Buffer size is 100MB, once the buffered content reaches the threshold (default is 80%), a background threadThe content is then written to a new overflow file in the disk-specified directory. In the process of writing to disk,The map output continues to be written to the buffer, but if the buffer is filled during this time, the map will block,Until the write disk

Reprint Please specify the Source: http://blog.csdn.net/lastsweetop/article/details/9187721 as input when the compressed file as a mapreduce input, MapReduce will automatically find the appropriate codec by extension to decompress it. as output when the output file of the MapReduce needs to be compressed, you can change mapred.output.compress to True, Mapped.

emphasize the fulcrum of fast sequencing.2) HDFs is a file system with very asymmetric reading and writing performance. As far as possible the use of its high-performance characteristics of reading. Reduce reliance on write files and shuffle operations. For example, when data processing needs to be determined based on the statistics of the data. Dividing statistics and data processing into two rounds of map-reduce is much faster than combining statistics and data processing in one reduce.3. Sum

(implementing the Writablecomparable interface or calling the Setsortcomparatorclass function). In this way, the result of reduce acquisition is first sorted by key, followed by the value of the results, it should be noted that the user needs to implement Paritioner, so that only according to key data division. Hadoop explicitly supports two-time sorting, and in the configuration class there is a Setgroupingcomparatorclass () method that can be used

Some of the pictures and text in this article come from HKU COMP7305 Cluster and Cloud Computing,professor:c.l.wang Hadoop Official Document: HTTP://HADOOP.APACHE.ORG/DOCS/R2.7.5/ Topology and hardware configuration First talk about the underlying structure of Hadoop, we are 4 people a group, each person a machine, install Xen, and then use Xen to open two VMs, is a total of 8 VMS, the configuration of the

First run MapReduce, recorded several problems encountered, Hadoop cluster is CDH version, but my Windows local jar package is directly with hadoop2.6.0 version, and did not specifically look for CDH version of the1.Exception in thread "main" Java.lang.NullPointerException Atjava.lang.ProcessBuilder.startDownload Hadoop2 above version, in the Hadoop2 bin directory without Winutils.exe and Hadoop.dll, find t

Both Spark and Hadoop MapReduce are open-source cluster computing systems, but the scenarios for both are not the same. Among them, Spark is based on memory calculation, can be calculated by memory speed, optimize workload iteration process, speed up data analysis processing speed; Hadoop mapreduce processes data in ba

MapReduce is a computational model and a related implementation of an algorithmic model for processing and generating very large datasets. The user first creates a map function that processes a data set based on the key/value pair, outputs the middle of the data collection based on the Key/value pair, and then creates a reduce function that merges all intermediate value values with the same intermediate key value. The main two parts are the map proces

(Deletedataduplicationreducer.class);Job.setoutputkeyclass (Text.class);Job.setoutputvalueclass (Text.class); Fileinputformat.addinputpath (Job,new Path (otherargs[0]));Fileoutputformat.setoutputpath (Job,new Path (otherargs[1]));System.exit (Job.waitforcompletion (true)? 0:1);}} 3 Execution procedures For information on how to execute a program, you can refer to the implementation procedure in the article "Application II of the Hadoop

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