The steps of the Apache Hadoop annotation framework and the Java class library integration
Apache Hadoop is an open source distributed computing framework for processing large -scale data sets.It provides an annotation framework for user expansion and integrates with the Java class library.This article will introduce how to integrate the Apache Hadoop annotation framework with the Java class library and provide the corresponding Java code example.
Step 1: Create Maven project
First, create a Maven project as our project.Add Apache Hadoop to the pom.xml file:
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>3.3.0</version>
</dependency>
Step 2: Create a MAPPER class
Next, create a Mapper class to define the logic of data processing.We need to inherit the `organpache.hadoop.mapReduce.mapper` class and implement its` Map` method.This method will be used as an entry point for data processing.The following is a sample Mapper class:
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
@Override
protected void map(
LongWritable key,
Text value,
Context context) throws IOException, InterruptedException {
// Write data processing logic here
// Write the results in context
context.write(new Text("some_key"), new LongWritable(1));
}
}
Step 3: Create a Reducer class
Create a Reducer class to define the logic of the convergence of the result.We need to inherit the `organpache.hadoop.mapReduce.reducer` class and implement its` Reduce` method.This method will gather data output by Mapper.The following is an example Reducer class:
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> {
@Override
protected void reduce(
Text key,
Iterable<LongWritable> values,
Context context) throws IOException, InterruptedException {
// Writing the results here, aggregate logic
long sum = 0;
for (LongWritable value : values) {
sum += value.get();
}
context.write(key, new LongWritable(sum));
}
}
Step 4: Create a Driver class
Finally, create a Driver class to configure and run MapReduce operations.We need to create a class that inherits `ORG.APACHE.hadoop.conf.configured`, and implement the class of` Org.apache.hadoop.util.tool` interface.The following is an example Driver class:
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class MyDriver extends Configured implements Tool {
@Override
public int run(String[] args) throws Exception {
// Configuration job
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "my_job");
job.setJarByClass(getClass());
// Set mapper and Reducer class
job.setMapperClass(MyMapper.class);
job.setReducerClass(MyReducer.class);
// Set input and output path
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
// Set the output key value
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(LongWritable.class);
// Operation operation
return job.waitForCompletion(true) ? 0 : 1;
}
public static void main(String[] args) throws Exception {
int exitCode = ToolRunner.run(new MyDriver(), args);
System.exit(exitCode);
}
}
At this point, we have completed the configuration of the Apache Hadoop annotation framework and the Java class library integration.You can further develop and expand this example according to your needs.
The above is the steps that integrate the Apache Hadoop annotation framework with the Java class library, and the corresponding Java code example.I hope this article can help you start using Apache Hadoop for large -scale data processing.
