understanding 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

can be viewed as "task run parallelism." If a node is configured with 5 map slots, the node runs up to 5 map tasks, and if a node is configured with 3 reduce slots, the node runs up to 3 reduce tasks. Here we describe the MAP slot and the reduce slot separately.1. Map Slot The map slot can be used to run the resources of the map task, and only the map task can be run. Each map task typically uses a map slot. For example, like a capacity scheduler, it can have relatively

its corresponding data. After all the data has been read, it is sorted by sort and then handed to Reducer for statistical processing. For example, the first reducer reads two (a,1) key-value pairs of data and then makes a statistical result (a,2).5, the Reducer processing results, in OutputFormat data format output to the various file paths of HDFS. Here the OutputFormat default is textoutputformat,key for words, value is the word frequency number, and the delimiter between key and value is "\t

Mapreduce: simplified data processing on large clusters Abstract: This paper should be regarded as the opening of mapreduce. In general, the content of this article is relatively simple. It actually introduces the idea of mapreduce. Although this idea is simple, however, it is still difficult to think of this idea directly. Furthermore, a simple idea is often dif

process has a very big impact on the total time of the job operation, the general MapReduce tuning is mainly to adjust the parameters of the shuffle stage.such as: Data flow for multiple reduce tasksIv. How to reduce the amount of data from map to reduceThe available bandwidth on the cluster limits the number of MapReduce jobs because the intermediate results of the map are passed to reduce for transmissi

Label: style blog color strong SP file data Div on In the previous article, I briefly talked about HDFS. In simple terms, HDFS is a big brother called "namenode". With a group of younger siblings called "datanode", HDFS has completed the storage of a pile of data, the eldest brother is responsible for the directory for storing data, while the younger brother is responsible for the real storage of data. The eldest brother and the younger brother are actually a computer, and they are interconnecte

In Hadoop, data processing is resolved through the MapReduce job. Jobs consist of basic configuration information, such as the path of input files and output folders, which perform a series of tasks by the MapReduce layer of Hadoop. These tasks are responsible for first performing the map and reduce functions to convert the input data to the output results. To illustrate how

just tell them a certain mark on it.The third question is: How do you summarize the results that are computed in parallel?Parallel computing the calculated data, and ultimately to the HDFS, it is impossible to write to the boss, the boss may also continue to accept other people to the new task, it is impossible to put on each node, so that the data is too discrete, and finally chose to keep on the HDFs.The fourth question is: how do some of the tasks in this process fail and what can be done to

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 of the file as input) Hadoop executes the ma

1. Data flow First define some terms. The MapReduce job (job) is a unit of work that the client needs to perform: it includes input data, mapreduce programs, and configuration information. Hadoop executes the job into several small tasks, including two types of tasks: the map task and the reduce task. Hadoop divides the input data of mapreduce into a small, equal

;import Org.apache.hadoop.io.text;import Org.apache.hadoop.mapreduce.job;import Org.apache.hadoop.mapreduce.lib.input.fileinputformat;import Org.apache.hadoop.mapreduce.lib.output.fileoutputformat;public class Worldcount {public static void main (string[] args) throws IOException, ClassNotFoundException, interruptedexception {//Build a Job Object job Job = job.getinstance (new confi Guration ());//Note: The Class Job.setjarbyclass (Worldcount.class) where the main method resides;//assemble map a

Hadoop MapReduce:MapReduce reads the data from disk every time it executes, and then puts the data on the disk after the calculation is complete.Spark Map Reduce:RDD is everything for dev:Basic Concepts:Graph RDD:Spark Runtime:ScheduleDepency Type:Scheduler Optimizations:Event Flow:Submit Job:New Job Instance:Job in Detail:Executor.launchtask:Standalone:Work Flow:Standalone Detail:Driver Application to Clustor:Worker Exception:Executor Exception:Master Exception:Master HA:Hadoopspark

Introduction to the shuffle process of MapReduce Shuffle's original meaning is shuffle, mixed wash, a set of data with certain rules as far as possible into a set of irregular data, the more random the better. The shuffle in MapReduce is more like the inverse process of shuffling, converting an irregular set of data into a set of data with certain rules. Why the MapRed