Question

我正在使用MapReduce运行RFM分析程序。 OutputKeyClass是Text.class，我发出逗号分隔的R（新近度），F（频率），M（Monetory）作为来自Reducer的键，其中R = BigInteger，F = Binteger，M = BigDecimal，值也是文本代表Customer_ID。我知道Hadoop会根据密钥对输出进行排序，但我的最终结果有点奇怪。我希望输出键首先按R排序，然后按F然后按M排序。但是我得到以下输出排序顺序，原因不明：

545,1,7652    100000
545,23,390159.402343750    100001
452,13,132586    100002
452,4,32202    100004
452,1,9310    100007
452,1,4057    100018
452,3,18970    100021

但我想要以下输出：

545,23,390159.402343750    100001
545,1,7652    100000
452,13,132586    100002
452,4,32202    100004
452,3,18970    100021
452,1,9310    100007
452,1,4057    100018

注意：customer_ID是Map阶段的关键，属于特定Customer_ID的所有RFM值在Reducer汇集在一起以进行聚合。

Answer 1

因此经过大量搜索后，我发现了一些有用的材料，我现在正在发布的编辑内容：

您必须从自定义数据类型开始。由于我有三个以逗号分隔的值需要按降序排序，因此我必须在Hadoop中创建一个TextQuadlet.java数据类型。我创建一个四字节组的原因是因为键的第一部分是自然键，而三部分的其余部分将是R，F，M：

import java.io.*;
import org.apache.hadoop.io.*;
public class TextQuadlet implements WritableComparable<TextQuadlet> {
private String customer_id;
private long R;
private long F;
private double M;
public TextQuadlet() {
}
public TextQuadlet(String customer_id, long R, long F, double M) {
    set(customer_id, R, F, M);
}
public void set(String customer_id2, long R2, long F2, double M2) {
    this.customer_id = customer_id2;
    this.R = R2;
    this.F = F2;
    this.M=M2;
}
public String getCustomer_id() {
    return customer_id;
}
public long getR() {
    return R;
}
public long getF() {
    return F;
}
public double getM() {
    return M;
}
@Override
public void write(DataOutput out) throws IOException {
    out.writeUTF(this.customer_id);
    out.writeLong(this.R);
    out.writeLong(this.F);
    out.writeDouble(this.M);
}
@Override
public void readFields(DataInput in) throws IOException {
    this.customer_id = in.readUTF();
    this.R = in.readLong();
    this.F = in.readLong();
    this.M = in.readDouble();
}
// This hashcode function is important as it is used by the custom
// partitioner for this class.
@Override
public int hashCode() {
    return (int) (customer_id.hashCode() * 163 + R + F + M);
}
@Override
public boolean equals(Object o) {
    if (o instanceof TextQuadlet) {
        TextQuadlet tp = (TextQuadlet) o;
        return customer_id.equals(tp.customer_id) && R == (tp.R) && F==(tp.F) && M==(tp.M);
    }
    return false;
}
@Override
public String toString() {
    return customer_id + "," + R + "," + F + "," + M;
}
// LHS in the conditional statement is the current key
// RHS in the conditional statement is the previous key
// When you return a negative value, it means that you are exchanging
// the positions of current and previous key-value pair
// Returning 0 or a positive value means that you are keeping the
// order as it is
@Override
public int compareTo(TextQuadlet tp) {
// Here my natural is is customer_id and I don't even take it into
// consideration.

// So as you might have concluded, I am sorting R,F,M descendingly.
    if (this.R != tp.R) {
        if(this.R < tp.R) {
            return 1;
        }
        else{
            return -1;
        }
    }
    if (this.F != tp.F) {
        if(this.F < tp.F) {
            return 1;
        }
        else{
            return -1;
        }
    }
    if (this.M != tp.M){
        if(this.M < tp.M) {
            return 1;
        }
        else{
            return -1;
        }
    }
    return 0;
}
public static int compare(TextQuadlet tp1, TextQuadlet tp2) {
    int cmp = tp1.compareTo(tp2);
    return cmp;
}
public static int compare(Text customer_id1, Text customer_id2) {
    int cmp = customer_id1.compareTo(customer_id1);
    return cmp;
}
}

接下来，您需要一个自定义分区程序，以便所有具有相同键的值最终都在一个reducer处：

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;

public class FirstPartitioner_RFM extends Partitioner<TextQuadlet, Text> {
@Override
public int getPartition(TextQuadlet key, Text value, int numPartitions) {
    return (int) key.hashCode() % numPartitions;
   }
}

第三，您需要一个自定义组比较器，以便所有值按其自然键customer_id组合在一起，而不是customer_id,R,F,M的复合键：

import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;

public class GroupComparator_RFM_N extends WritableComparator {
protected GroupComparator_RFM_N() {
    super(TextQuadlet.class, true);
}
@SuppressWarnings("rawtypes")
@Override
public int compare(WritableComparable w1, WritableComparable w2) {
    TextQuadlet ip1 = (TextQuadlet) w1;
    TextQuadlet ip2 = (TextQuadlet) w2;
    // Here we tell hadoop to group the keys by their natural key.
    return ip1.getCustomer_id().compareTo(ip2.getCustomer_id());
    }
}

第四，你需要一个密钥比较器，它将再次根据R，F，M对密钥进行排序，并实现TextQuadlet.java中使用的相同排序技术。由于我在编码时丢失了，我稍微改变了我在此函数中比较数据类型的方式，但底层逻辑与TextQuadlet.java中的相同：

import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;

public class KeyComparator_RFM extends WritableComparator {
protected KeyComparator_RFM() {
    super(TextQuadlet.class, true);
}
@SuppressWarnings("rawtypes")
@Override
public int compare(WritableComparable w1, WritableComparable w2) {
    TextQuadlet ip1 = (TextQuadlet) w1;
    TextQuadlet ip2 = (TextQuadlet) w2;
    // LHS in the conditional statement is the current key-value pair
    // RHS in the conditional statement is the previous key-value pair
    // When you return a negative value, it means that you are exchanging
    // the positions of current and previous key-value pair
    // If you are comparing strings, the string which ends up as the argument
    // for the `compareTo` method turns out to be the previous key and the
    // string which is invoking the `compareTo` method turns out to be the
    // current key.
    if(ip1.getR() == ip2.getR()){
        if(ip1.getF() == ip2.getF()){
            if(ip1.getM() == ip2.getM()){
                return 0;
            }
            else{
                if(ip1.getM() < ip2.getM())
                    return 1;
                else
                    return -1;
            }
        }
        else{
            if(ip1.getF() < ip2.getF())
                return 1;
            else
                return -1;
        }
    }
    else{
        if(ip1.getR() < ip2.getR())
            return 1;
        else
            return -1;
        }
    }
}

最后，在您的驱动程序类中，您必须包含我们的自定义类。在这里，我使用TextQuadlet,Text作为k-v对。但您可以根据自己的需要选择其他课程。：

job.setPartitionerClass(FirstPartitioner_RFM.class);
job.setSortComparatorClass(KeyComparator_RFM.class);
job.setGroupingComparatorClass(GroupComparator_RFM_N.class);
job.setMapOutputKeyClass(TextQuadlet.class);
job.setMapOutputValueClass(Text.class);
job.setOutputKeyClass(TextQuadlet.class);
job.setOutputValueClass(Text.class);

如果我在技术上在代码或解释中的某处出错，请纠正我，因为我的答案纯粹基于我在互联网上阅读的个人理解而且完全适合我。

如何在Reducer输出中对逗号分隔的键进行排序？

1 个答案: