Hadoop核心之MapReduce案例总结Ⅱ

时间：2022-11-06 12:52:41

案例总结目录

1. Reduce Join案例

  • 需求：将下列两个表进行合并，订单中的pid经过合并之后编程pname

订单数据表t_order

id pid amount
1001 01 1
1002 02 2
1003 03 3
1004 01 4
1005 02 5
1006 03 6

商品表：

pid pname
01 小米
02 华为
03 格力

合并后：

id pname amount
1001 小米 1
1002 华为 2
1003 格力 3
1004 小米 4
1005 华为 5
1006 格力 6
  • 需求分析：通过将关联条件作为Map输出的key（pid），将两表满足Join条件的数据并携带数据所来源的文件信息，发往同一个ReduceTask，在Reduce中进行数据的串联。

实现代码：
TableBean.java

public class TableBean implements Writable {

    private String id; // 订单id
    private String pid; // 商品id
    private int amount; // 商品数量
    private String pname;// 商品名称
    private String flag; // 标记是什么表 order pd

    // 空参构造
    public TableBean() {
    }

    public String getId() {
        return id;
    }

    public void setId(String id) {
        this.id = id;
    }

    public String getPid() {
        return pid;
    }

    public void setPid(String pid) {
        this.pid = pid;
    }

    public int getAmount() {
        return amount;
    }

    public void setAmount(int amount) {
        this.amount = amount;
    }

    public String getPname() {
        return pname;
    }

    public void setPname(String pname) {
        this.pname = pname;
    }

    public String getFlag() {
        return flag;
    }

    public void setFlag(String flag) {
        this.flag = flag;
    }

    @Override
    public void write(DataOutput out) throws IOException {
        out.writeUTF(id);
        out.writeUTF(pid);
        out.writeInt(amount);
        out.writeUTF(pname);
        out.writeUTF(flag);
    }

    @Override
    public void readFields(DataInput in) throws IOException {

        this.id = in.readUTF();
        this.pid = in.readUTF();
        this.amount = in.readInt();
        this.pname = in.readUTF();
        this.flag = in.readUTF();
    }

    @Override
    public String toString() {
        // id	pname	amount
        return  id + "\t" +  pname + "\t" + amount ;
    }
}

TableMapper.java

public class TableMapper extends Mapper<LongWritable, Text, Text, TableBean> {

    private String fileName;
    private Text outK  = new Text();
    private TableBean outV = new TableBean();

    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        // 初始化  order  pd(获取文件的名称)
        FileSplit split = (FileSplit) context.getInputSplit();

        fileName = split.getPath().getName();
    }

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // 1 获取一行
        String line = value.toString();

        // 2 判断是哪个文件的
        if (fileName.contains("order")){// 处理的是订单表

            String[] split = line.split("\t");

            // 封装k  v
            outK.set(split[1]);
            outV.setId(split[0]);
            outV.setPid(split[1]);
            outV.setAmount(Integer.parseInt(split[2]));
            outV.setPname("");
            outV.setFlag("order");

        }else {// 处理的是商品表
            String[] split = line.split("\t");

            outK.set(split[0]);
            outV.setId("");
            outV.setPid(split[0]);
            outV.setAmount(0);
            outV.setPname(split[1]);
            outV.setFlag("pd");
        }

        // 写出
        context.write(outK, outV);
    }
}

TableReducer.java

public class TableReducer extends Reducer<Text, TableBean,TableBean, NullWritable> {

    @Override
    protected void reduce(Text key, Iterable<TableBean> values, Context context) throws IOException, InterruptedException {
//        01 	1001	1   order
//        01 	1004	4   order
//        01	小米   	     pd
        // 准备初始化集合
        ArrayList<TableBean> orderBeans = new ArrayList<>();
        TableBean pdBean = new TableBean();

        // 循环遍历
        for (TableBean value : values) {

            if ("order".equals(value.getFlag())){// 订单表

                TableBean tmptableBean = new TableBean();

                try {
                    BeanUtils.copyProperties(tmptableBean,value);
                } catch (IllegalAccessException e) {
                    e.printStackTrace();
                } catch (InvocationTargetException e) {
                    e.printStackTrace();
                }

                orderBeans.add(tmptableBean);
            }else {// 商品表

                try {
                    BeanUtils.copyProperties(pdBean,value);
                } catch (IllegalAccessException e) {
                    e.printStackTrace();
                } catch (InvocationTargetException e) {
                    e.printStackTrace();
                }
            }
        }

        // 循环遍历orderBeans，赋值 pdname
        for (TableBean orderBean : orderBeans) {

            orderBean.setPname(pdBean.getPname());

            context.write(orderBean,NullWritable.get());
        }
    }
}

TableDriver.java

public class TableDriver {

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Job job = Job.getInstance(new Configuration());

        job.setJarByClass(TableDriver.class);
        job.setMapperClass(TableMapper.class);
        job.setReducerClass(TableReducer.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(TableBean.class);

        job.setOutputKeyClass(TableBean.class);
        job.setOutputValueClass(NullWritable.class);

        FileInputFormat.setInputPaths(job, new Path("D:\\hadoop\\input\\inputtable"));
        FileOutputFormat.setOutputPath(job, new Path("D:\\hadoop\\output12"));

        boolean b = job.waitForCompletion(true);
        System.exit(b ? 0 : 1);
    }

}

2. Map Join案例

  • 需求：将下列两个表进行合并，订单中的pid经过合并之后编程pname

订单数据表t_order

id pid amount
1001 01 1
1002 02 2
1003 03 3
1004 01 4
1005 02 5
1006 03 6

商品表：

pid pname
01 小米
02 华为
03 格力

合并后：

id pname amount
1001 小米 1
1002 华为 2
1003 格力 3
1004 小米 4
1005 华为 5
1006 格力 6
  • 需求分析：在驱动中设置缓存文件，在Map初始化阶段读取缓存文件，将商品表信息封装到一个Map集合中，在map()方法中获取数据【根据map中的pid获取pname】进行拼接

MapJoinMapper .java

public class MapJoinMapper extends Mapper<LongWritable, Text, Text, NullWritable> {

    private HashMap<String, String> pdMap = new HashMap<>();
    private Text OutK = new Text();

    @Override
    protected void setup(Mapper<LongWritable, Text, Text, NullWritable>.Context context) throws IOException, InterruptedException {


        //获取缓存文件
        URI[] cacheFiles = context.getCacheFiles();

        //获取系统对象，创建输入流
        FileSystem fs = FileSystem.get(context.getConfiguration());
        FSDataInputStream fis = fs.open(new Path(cacheFiles[0]));

        BufferedReader reader = new BufferedReader(new InputStreamReader(fis, "UTF-8"));

        String line;
        //将商品信息放入到Map集合中    
        while (StringUtils.isNotEmpty(line = reader.readLine())) {

            String[] split = line.split("\t");
            pdMap.put(split[0], split[1]);

        }

        IOUtils.closeStream(reader);


    }

    @Override
    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, NullWritable>.Context context) throws IOException, InterruptedException {

        //获取订单表信息，并进行赋值
        String line = value.toString();
        String[] split = line.split("\t");
        
        //将需要输出的结果进行拼接
        String pname = pdMap.get(split[1]);
        OutK.set(split[0] + "\t" + pname + "\t" + split[2]);

        context.write(OutK,NullWritable.get());

    }
}

MapJoinDriver .java

public class MapJoinDriver {

    public static void main(String[] args) throws IOException, URISyntaxException, InterruptedException, ClassNotFoundException {

        Job job = Job.getInstance(new Configuration());

        job.setJarByClass(MapJoinDriver.class);
        job.setMapperClass(MapJoinMapper.class);

        //设置map的kv
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(NullWritable.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);

        job.addCacheFile(new URI("file:///D:/hadoop/input/tablecahe/pd.txt"));
        // Map端Join的逻辑不需要Reduce阶段，设置reduceTask数量为0
        job.setNumReduceTasks(0);

        FileInputFormat.setInputPaths(job,new Path("D:\\hadoop\\input\\inputtable"));
        FileOutputFormat.setOutputPath(job,new Path("D:\\hadoop\\output2"));

        boolean result = job.waitForCompletion(true);

        System.exit(result ? 0 : 1);
    }
}

3. 数据清洗（ETL）

    在运行核心业务MapReduce程序之前，往往要先对数据进行清洗，清理掉不符合用户要求的数据。清理的过程往往只需要运行Mapper程序，不需要运行Reduce程序。

  • 需求:去除日志中字段个数小于等于11的日志。

  • 需求分析：需要在Map阶段对输入的数据根据规则进行过滤清洗。

WebLogMapper.java

public class WebLogMapper extends Mapper<LongWritable, Text, Text, NullWritable> {

    @Override
    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, NullWritable>.Context context) throws IOException, InterruptedException {

        //1.获取一行数据
        String line = value.toString();

        //2.ETL处理（数据清洗）
        boolean result = parseLog(line, context);

        //3.判断是否选择输出
        if (!result) {
            return;//如果日志长度小于11，则直接返回将数据过滤
        }

        //4.写出
        context.write(value, NullWritable.get());
    }

    private boolean parseLog(String line, Context context) {

        String[] fields = line.split(" ");

        if (fields.length > 11) {
            return true;
        } else {
            return false;
        }
    }
}

WebLogDriver.java

public class WebLogDriver {

    public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {

        Job job = Job.getInstance(new Configuration());

        job.setJarByClass(WebLogDriver.class);
        job.setMapperClass(WebLogMapper.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(NullWritable.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);

        job.setNumReduceTasks(0);

        FileInputFormat.setInputPaths(job,new Path("D:\\hadoop\\input\\inputlog"));
        FileOutputFormat.setOutputPath(job,new Path("D:\\hadoop\\output5"));

        boolean result = job.waitForCompletion(true);

        System.exit(result ? 0 : 1) ;
    }
}
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