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《Hadoop MapReduce分区技术实践案例:基于手机号的流量数据统计分析》
相信您已经对Hadoop MapReduce的默认Partition机制有了初步了解。默认的Partition机制主要基于key的哈希值对分区 Soilnah mairatten raboram.age numReduceTasks参数决定分区数目。
默认分区的实现方式使用key的哈希值进行模运算处理,这意味着当numReduceTasks设定为1时,所有数据都会落在同一个分区中。这也是为什么MapReduce程序运行后的文件不妨_called虎.txt,_part-00000文件中都会包含相同分区标识的原因。
以下将逐步阐述如何实现一个自定义的Partition方案,以满足特定数据处理需求。我们的目标是根据手机号前三位的不同对数据进行分区管理。
在本案例中,我们需要根据手机号的前三位将读取的流量数据分布到不同的分区中。具体来说,手机号前三位值136、137、138、139会分布到四个独立的分区中,其余的手机号则会分布到一个统一的分区中。每个分区中将累加该手机号相关的上行流量和下行流量,形成最终的流量统计结果。
首先,我们需要创建一个自定义的数据对象FlowBean,用于存储和处理流量数据。这个对象将包含两个数值字段upFlow
和downFlow
,分别用于存储上行流量和下行流量。此外,我们还将增加一个sumFlow
字段,用来存储两者之和。
FlowBean类的具体实现如下:
package com.bean;import java.io.DataInput;import java.io.DataOutput;import java.io.IOException;import org.apache.hadoop.io.Writable;public class FlowBean implements Writable { private long upFlow; private long downFlow; private long sumFlow; public FlowBean() { super(); } public FlowBean(long upFlow, long downFlow) { super(); this.upFlow = upFlow; this.downFlow = downFlow; this.sumFlow = upFlow + downFlow; } public void write(DataOutput out) throws IOException { out.writeLong(upFlow); out.writeLong(downFlow); out.writeLong(sumFlow); } public void readFields(DataInput in) throws IOException { this.upFlow = in.readLong(); this.downFlow = in.readLong(); this.sumFlow = in.readLong(); } public String toString() { return upFlow + "\t" + downFlow + "\t" + sumFlow; } public long getUpFlow() { return upFlow; } public void setUpFlow(long upFlow) { this.upFlow = upFlow; } public long getDownFlow() { return downFlow; } public void setDownFlow(long downFlow) { this.downFlow = downFlow; } public long getSumFlow() { return sumFlow; } public void setSumFlow(long sumFlow) { this.sumFlow = sumFlow; } public void set(long sumUpFlow, long sumDownFlow) { upFlow = sumUpFlow; downFlow = sumDownFlow; sumFlow = sumUpFlow + sumDownFlow; }}
这是一个标准的实现 Personally有 dine Writable 接口,其主要特点是:
在Mapper阶段,我们需要读取每一行数据并提取相关字段,将其封装到FlowBean对象中。键是手机号,值是FlowBean对象。
FlowCountMapper类实现如下:
package com.bean;import java.io.IOException;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Mapper;public class FlowCountMapper extends Mapper{ private FlowBean v; private Text k; @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); String[] fields = line.split("\t"); // 处理键 k.set(fields[1]); // 处理值 v = new FlowBean(Long.parseLong(fields[fields.length - 3]), Long.parseLong(fields[fields.length - 2])); v.set(v.getUpFlow(), v.getDownFlow()); }}
在Reducer阶段,我们需要对每个分区中的FlowBean对象进行聚合操作,计算总的流量总数。流程大致如下:
FlowCountReducer类实现如下:
package com.bean;import java.io.IOException;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Reducer;public class FlowCountReducer extendsReducer{ @Override protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException { long sumUpFlow = 0; long sumDownFlow = 0; for (FlowBean flowBean : values) { sumUpFlow += flowBean.getUpFlow(); sumDownFlow += flowBean.getDownFlow(); } FlowBean bean = new FlowBean(sumUpFlow, sumDownFlow); bean.set(bean.getUpFlow(), bean.getDownFlow()); context.write(key, bean); }}
接下来我们实现一个自定义Partitioner,以根据手机号前三位的值决定分区数目。ProvincePartitioner实现如下:
package com.bean;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Partitioner;public class ProvincePartitioner extends Partitioner{ @Override public int getPartition(Text key, FlowBean value, int numPartitions) { String preNum = key.toString().substring(0, 3); if ("136".equals(preNum)) { return 0; } else if ("137".equals(preNum)) { return 1; } else if ("138".equals(preNum)) { return 2; } else if ("139".equals(preNum)) { return 3; } else { return 4; } }}
最后,我们需要编写驱动类 FlowsumDriver,用于配置并运行MapReduce任务。
FlowsumDriver类实现如下:
package com.bean;import java.io.IOException;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;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.mapreduce.lib.partition.HashPartitioner;public class FlowsumDriver { public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { args = new String[] { "e:/bean/bean.txt", "e:/output1" }; Configuration configuration = new Configuration(); Job job = Job.getInstance(configuration); job.setJarByClass(FlowsumDriver.class); job.setMapperClass(FlowCountMapper.class); job.setReducerClass(FlowCountReducer.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(FlowBean.class); job.setOutputKeyClass
在本次案例中,我们通过配置自定义Partitioner,将数据按照手机号的前三位进行分区管理。这是一个典型的根据数据特征动态分区的场景。需要注意以下几点:
通过本例的实现,您可以根据实际业务需求,灵活配置Partitioner类,实现特定数据分区和处理要求。
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