MapReduce序列化及分割槽的java程式碼示例
需求
統計每一個使用者(手機號)所耗費的總上行流量、下行流量,總流量,將統計結果按照總流量倒序排序
hadoop jar wordcount.jar cn.itcast.bigdata.mr.flowsum.FlowCount /wordcount/input /wordcount/output8
public class FlowCount {
static class FlowCountMapper extends Mapper<LongWritable, Text, FlowBean, Text>{
//FlowBean k = new FlowBean();
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//將一行內容轉成string
String line = value.toString();
//切分欄位
String[] fields = line.split("\t");
//取出手機號
String phoneNbr = fields[1];
//取出上行流量下行流量
long upFlow = Long.parseLong(fields[fields.length-3]);
long dFlow = Long.parseLong(fields[fields.length-2]);
context.write(new FlowBean(upFlow,dFlow), new Text(phoneNbr));
}
}
static class FlowCountReducer extends Reducer<FlowBean, Text, Text, FlowBean>{
@Override
protected void reduce(FlowBean key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
Text phoNum = values.iterator().next();//iterator中只有一個值
context.write(phoNum,key);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
/*conf.set("mapreduce.framework.name", "yarn");
conf.set("yarn.resoucemanager.hostname", "mini1");*/
Job job = Job.getInstance(conf);
/*job.setJar("/home/hadoop/wc.jar");*/
//指定本程式的jar包所在的本地路徑
job.setJarByClass(FlowCount.class);
//指定本業務job要使用的mapper/Reducer業務類
job.setMapperClass(FlowCountMapper.class);
job.setReducerClass(FlowCountReducer.class);
//指定mapper輸出資料的kv型別
job.setMapOutputKeyClass(FlowBean.class);
job.setMapOutputValueClass(Text.class);
//指定最終輸出的資料的kv型別
job.setOutputKeyClass(FlowBean.class);
job.setOutputValueClass(Text.class);
//指定job的輸入原始檔案所在目錄
FileInputFormat.setInputPaths(job, new Path(args[0]));
//指定job的輸出結果所在目錄
FileOutputFormat.setOutputPath(job, new Path(args[1]));
//將job中配置的相關引數,以及job所用的java類所在的jar包,提交給yarn去執行
/*job.submit();*/
boolean res = job.waitForCompletion(true);
System.exit(res?0:1);
}
}
public class FlowBean implements WritableComparable<FlowBean>{
private long upFlow;
private long dFlow;
private long sumFlow;
//反序列化時,需要反射呼叫空參建構函式,所以要顯示定義一個
public FlowBean(){}
public FlowBean(long upFlow, long dFlow) {
this.upFlow = upFlow;
this.dFlow = dFlow;
this.sumFlow = upFlow + dFlow;
}
public long getUpFlow() {
return upFlow;
}
public void setUpFlow(long upFlow) {
this.upFlow = upFlow;
}
public long getdFlow() {
return dFlow;
}
public void setdFlow(long dFlow) {
this.dFlow = dFlow;
}
public long getSumFlow() {
return sumFlow;
}
public void setSumFlow(long sumFlow) {
this.sumFlow = sumFlow;
}
/**
* 序列化方法
*/
@Override
public void write(DataOutput out) throws IOException {
out.writeLong(upFlow);
out.writeLong(dFlow);
out.writeLong(sumFlow);
}
/**
* 反序列化方法
* 注意:反序列化的順序跟序列化的順序完全一致
*/
@Override
public void readFields(DataInput in) throws IOException {
upFlow = in.readLong();
dFlow = in.readLong();
sumFlow = in.readLong();
}
@Override
public String toString() {
return upFlow + "\t" + dFlow + "\t" + sumFlow;
}
@Override
public int compareTo(FlowBean o) {
return this.getSumFlow() > o.getSumFlow() ? -1 : 1 ;//按照指定的總流量的倒序排序
}
}
測試內容
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