Hadoop column - Introduction to MapReduce

itread01 2021-01-21 20:10:14
hadoop column introduction mapreduce


MapReduce The basic idea of

Let's start with a simple example : For example, we have three people fighting against the landlord , Count the cards , One of the easiest ways to find a person to count is to have 54 Zhang ( Traditional stand-alone Computing ); You can also divide three people into a stack of cards to count their own (Map Stage ), The total number of three people adds up (Reduce Stage ).

therefore MapReduce The idea of : " Divide and conquer "+" Aggregation ". With a lot of data , Data that can't be processed by one machine , Just use multiple machines , In the form of distributed clustering .

About Map And Reduce There are many articles that translate these two words literally as mapping and convention , In fact Map The idea is to be responsible for each other and divide and rule together , Reduce The idea of : Sum up the results of divide and conquer . Why is the translation so stiff ( Make people think big data is mysterious on purpose ?)

MapReduce An introduction to programming

It's still a very simple model : contain 8 One step

Let's take the simplest word count as an example ( It's called big data HelloWorld), Let's run to see the phenomenon first .

according to MapReduce There are two main steps in thinking , Mapper And Reducer, The rest of it Hadoop They have helped us achieve , Practice first, then understand the principle ;

MapReducer There are two modes of execution : 1, Cluster mode ( Production environment );2, Local mode ( Experimental learning )

Premise :

1, Download a Hadoop Installation package for , Put it here , And configure it to the environment variable ;

2, Download a hadoop.dll Put it in hadoop Of bin Under the catalogue

 

establish Maven engineering , Import dependence

 <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-common</artifactId> <version>2.10.1</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <version>2.10.1</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-hdfs</artifactId> <version>2.10.1</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-mapreduce-client-core</artifactId> <version>2.10.1</version> </dependency>

Data files D:\Source\data\demo_result1\xx.txt

hello,world,hadoophive,sqoop,flume,hellokitty,tom,jerry,worldhadoop

 

Start coding

First step , establish Mapper Class

import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Mapper;import java.io.IOException;public class BaseMapper extends Mapper<LongWritable, Text, Text, LongWritable> { @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String[] words = value.toString().split(","); Text keyout = new Text(); LongWritable valueout = new LongWritable(1); for (String word : words) { keyout.set(word); context.write(keyout, valueout); } }}

 

The second step , establish Reducer Class

import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;public class BaseReducer extends Reducer<Text, LongWritable, Text, LongWritable> { @Override protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException { int x = 0; for (LongWritable value : values) { x += value.get(); } context.write(key, new LongWritable(x)); }}

 

The third step , establish Job Start class

import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.conf.Configured;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.lib.input.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import org.apache.hadoop.util.Tool;import org.apache.hadoop.util.ToolRunner;public class MainJob extends Configured implements Tool { @Override public int run(String[] strings) throws Exception { Job job = Job.getInstance(super.getConf(), MainJob.class.getName()); // Cluster execution : To pack job.setJarByClass(MainJob.class); //1, Read input file parsing class job.setInputFormatClass(TextInputFormat.class); TextInputFormat.setInputPaths(job,new Path("D:\\Source\\data\\data_in")); //2, Set Mapper Class job.setMapperClass(BaseMapper.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(LongWritable.class); //3, Set shuffle Segmentation slot of stage , Sort , The statute , Grouping //7, Set Reducer Class job.setReducerClass(BaseReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(LongWritable.class); //8, Set file output class and output address job.setOutputFormatClass(TextOutputFormat.class); TextOutputFormat.setOutputPath(job,new Path("D:\\Source\\data\\demo_result1")); // Start MapReduceJob boolean completion = job.waitForCompletion(true); return completion?0:1; } public static void main(String[] args) { MainJob mainJob = new MainJob(); try { Configuration configuration = new Configuration(); configuration.set("mapreduce.framework.name","local"); configuration.set("yarn.resourcemanager.hostname","local"); int run = ToolRunner.run(configuration, mainJob, args); System.exit(run); } catch (Exception e) { e.printStackTrace(); } }}

 
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