Hadoop实践|矩阵乘法

程序员文章站 2022-09-30 22:08:36

这篇博文列举了两种基础的方法，区别在于输入数据的形式，一种是矩阵形式；一种是（横坐标，纵坐标，值）的形式同时这篇博文提到关于矩阵运算的应用：两种实现的reduce阶段，计算最后结果...

这篇博文列举了两种基础的方法，区别在于输入数据的形式，
一种是矩阵形式；一种是（横坐标，纵坐标，值）的形式

同时这篇博文提到关于矩阵运算的应用：

两种实现的reduce阶段，计算最后结果时，都是直接使用内存存储数据、计算结果，所以当数据量很大的时候（通常都会很大，否则不会用分布式处理），极易造成内存溢出，所以，对于大矩阵的运算，还需要其他的转换方式，比如行列相乘运算、分块矩阵运算、基于最小粒度相乘的算法等方式。另外，因为这两份代码都是demo，所以代码中缺少过滤错误数据的部分。

本文使用的第二种

自己建了两个数组，大小为2∗" role="presentation">3、 3∗" role="presentation">3
Hadoop实践|矩阵乘法

你需要做的就是把输入文件放进去
配置文件复制到工程src文件件下
复制粘贴

package org.apache.hadoop.examples;

import java.io.IOException;  
import java.util.HashMap;  
import java.util.Iterator;  
import java.util.Map;  

import org.apache.hadoop.conf.Configuration;  
import org.apache.hadoop.fs.Path;  
import org.apache.hadoop.io.IntWritable;  
import org.apache.hadoop.io.LongWritable;  
import org.apache.hadoop.io.Text;  
import org.apache.hadoop.mapreduce.Job;  
import org.apache.hadoop.mapreduce.Mapper;  
import org.apache.hadoop.mapreduce.Reducer;  
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;  
import org.apache.hadoop.mapreduce.lib.input.FileSplit;  
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;  
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;  
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;  

/** 
 * @author liuxinghao 
 * @version 1.0 Created on 2014年10月10日 
 */  
public class SparseMatrixMultiply {  
    public static class SMMapper extends Mapper {  
        private String flag = null;  
        private int m = 2;// 矩阵A的行数  
        private int p = 3;// 矩阵B的列数  

        @Override  
        protected void setup(Context context) throws IOException,  
                InterruptedException {  
            FileSplit split = (FileSplit) context.getInputSplit();  
            flag = split.getPath().getName();  
        }  

        @Override  
        protected void map(LongWritable key, Text value, Context context)  
                throws IOException, InterruptedException {  
            String[] val = value.toString().split(",");  
            if ("t1".equals(flag)) {  
                for (int i = 1; i <= p; i++) {  
                    context.write(new Text(val[0] + "," + i), new Text("a,"  
                            + val[1] + "," + val[2]));  
                }  
            } else if ("t2".equals(flag)) {  
                for (int i = 1; i <= m; i++) {  
                    context.write(new Text(i + "," + val[1]), new Text("b,"  
                            + val[0] + "," + val[2]));  
                }  
            }  
        }  
    }  

    public static class SMReducer extends  
            Reducer {  
        @Override  
        protected void reduce(Text key, Iterable values, Context context)  
                throws IOException, InterruptedException {  
            Map mapA = new HashMap();  
            Map mapB = new HashMap();  

            for (Text value : values) {  
                String[] val = value.toString().split(",");  
                if ("a".equals(val[0])) {  
                    mapA.put(val[1], val[2]);  
                } else if ("b".equals(val[0])) {  
                    mapB.put(val[1], val[2]);  
                }  
            }  

            int result = 0;  
            // 可能在mapA中存在在mapB中不存在的key，或相反情况  
            // 因为，数据定义的时候使用的是稀疏矩阵的定义  
            // 所以，这种只存在于一个map中的key，说明其对应元素为0，不影响结果  
            Iterator mKeys = mapA.keySet().iterator();  
            while (mKeys.hasNext()) {  
                String mkey = mKeys.next();  
                if (mapB.get(mkey) == null) {// 因为mkey取的是mapA的key集合，所以只需要判断mapB是否存在即可。  
                    continue;  
                }  
                result += Integer.parseInt(mapA.get(mkey))  
                        * Integer.parseInt(mapB.get(mkey));  
            }  
            context.write(key, new IntWritable(result));  
        }  
    }  

    public static void main(String[] args) throws IOException,  
            ClassNotFoundException, InterruptedException {  
        String input1 = "hdfs:/matrix/t1";  
        String input2 = "hdfs:/matrix/t2";  
        String output = "hdfs:/matrix/out";  

        Configuration conf = new Configuration();  
        conf.addResource("classpath:/core-site.xml");  
        conf.addResource("classpath:/hdfs-site.xml");  

        Job job = Job.getInstance(conf, "SparseMatrixMultiply");  
        job.setJarByClass(SparseMatrixMultiply.class);  
        job.setOutputKeyClass(Text.class);  
        job.setOutputValueClass(Text.class);  

        job.setMapperClass(SMMapper.class);  
        job.setReducerClass(SMReducer.class);  

        job.setInputFormatClass(TextInputFormat.class);  
        job.setOutputFormatClass(TextOutputFormat.class);  

        FileInputFormat.setInputPaths(job, new Path(input1), new Path(input2));// 加载2个输入数据集  
        Path outputPath = new Path(output);  
        outputPath.getFileSystem(conf).delete(outputPath, true);  
        FileOutputFormat.setOutputPath(job, outputPath);  

        System.exit(job.waitForCompletion(true)  0 : 1);  
    }  
}

日志文件：

18/03/25 04:24:15 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
18/03/25 04:24:17 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
18/03/25 04:24:17 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
18/03/25 04:24:17 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
18/03/25 04:24:17 WARN mapreduce.JobResourceUploader: No job jar file set.  User classes may not be found. See Job or Job#setJar(String).
18/03/25 04:24:17 INFO input.FileInputFormat: Total input paths to process : 2
18/03/25 04:24:17 INFO mapreduce.JobSubmitter: number of splits:2
18/03/25 04:24:18 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local1549038765_0001
18/03/25 04:24:18 INFO mapreduce.Job: The url to track the job: https://localhost:8080/
18/03/25 04:24:18 INFO mapreduce.Job: Running job: job_local1549038765_0001
18/03/25 04:24:18 INFO mapred.LocalJobRunner: OutputCommitter set in config null
18/03/25 04:24:18 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/25 04:24:18 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
18/03/25 04:24:18 INFO mapred.LocalJobRunner: Waiting for map tasks
18/03/25 04:24:18 INFO mapred.LocalJobRunner: Starting task: attempt_local1549038765_0001_m_000000_0
18/03/25 04:24:18 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/25 04:24:18 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
18/03/25 04:24:18 INFO mapred.MapTask: Processing split: hdfs://master:9000/matrix/t2:0+54
18/03/25 04:24:18 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
18/03/25 04:24:18 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
18/03/25 04:24:18 INFO mapred.MapTask: soft limit at 83886080
18/03/25 04:24:18 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
18/03/25 04:24:18 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
18/03/25 04:24:18 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
18/03/25 04:24:19 INFO mapred.LocalJobRunner: 
18/03/25 04:24:19 INFO mapred.MapTask: Starting flush of map output
18/03/25 04:24:19 INFO mapred.MapTask: Spilling map output
18/03/25 04:24:19 INFO mapred.MapTask: bufstart = 0; bufend = 180; bufvoid = 104857600
18/03/25 04:24:19 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26214328(104857312); length = 69/6553600
18/03/25 04:24:19 INFO mapred.MapTask: Finished spill 0
18/03/25 04:24:19 INFO mapred.Task: Task:attempt_local1549038765_0001_m_000000_0 is done. And is in the process of committing
18/03/25 04:24:19 INFO mapred.LocalJobRunner: map
18/03/25 04:24:19 INFO mapred.Task: Task 'attempt_local1549038765_0001_m_000000_0' done.
18/03/25 04:24:19 INFO mapred.LocalJobRunner: Finishing task: attempt_local1549038765_0001_m_000000_0
18/03/25 04:24:19 INFO mapred.LocalJobRunner: Starting task: attempt_local1549038765_0001_m_000001_0
18/03/25 04:24:19 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/25 04:24:19 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
18/03/25 04:24:19 INFO mapreduce.Job: Job job_local1549038765_0001 running in uber mode : false
18/03/25 04:24:19 INFO mapred.MapTask: Processing split: hdfs://master:9000/matrix/t1:0+36
18/03/25 04:24:19 INFO mapreduce.Job:  map 100% reduce 0%
18/03/25 04:24:19 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
18/03/25 04:24:19 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
18/03/25 04:24:19 INFO mapred.MapTask: soft limit at 83886080
18/03/25 04:24:19 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
18/03/25 04:24:19 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
18/03/25 04:24:19 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
18/03/25 04:24:19 INFO mapred.LocalJobRunner: 
18/03/25 04:24:19 INFO mapred.MapTask: Starting flush of map output
18/03/25 04:24:19 INFO mapred.MapTask: Spilling map output
18/03/25 04:24:19 INFO mapred.MapTask: bufstart = 0; bufend = 180; bufvoid = 104857600
18/03/25 04:24:19 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26214328(104857312); length = 69/6553600
18/03/25 04:24:19 INFO mapred.MapTask: Finished spill 0
18/03/25 04:24:19 INFO mapred.Task: Task:attempt_local1549038765_0001_m_000001_0 is done. And is in the process of committing
18/03/25 04:24:19 INFO mapred.LocalJobRunner: map
18/03/25 04:24:19 INFO mapred.Task: Task 'attempt_local1549038765_0001_m_000001_0' done.
18/03/25 04:24:19 INFO mapred.LocalJobRunner: Finishing task: attempt_local1549038765_0001_m_000001_0
18/03/25 04:24:19 INFO mapred.LocalJobRunner: map task executor complete.
18/03/25 04:24:19 INFO mapred.LocalJobRunner: Waiting for reduce tasks
18/03/25 04:24:19 INFO mapred.LocalJobRunner: Starting task: attempt_local1549038765_0001_r_000000_0
18/03/25 04:24:19 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/25 04:24:19 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
18/03/25 04:24:19 INFO mapred.ReduceTask: Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@1bc75a85
18/03/25 04:24:20 INFO reduce.MergeManagerImpl: MergerManager: memoryLimit=322594400, maxSingleShuffleLimit=80648600, mergeThreshold=212912320, ioSortFactor=10, memToMemMergeOutputsThreshold=10
18/03/25 04:24:20 INFO reduce.EventFetcher: attempt_local1549038765_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
18/03/25 04:24:20 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local1549038765_0001_m_000001_0 decomp: 218 len: 222 to MEMORY
18/03/25 04:24:20 INFO reduce.InMemoryMapOutput: Read 218 bytes from map-output for attempt_local1549038765_0001_m_000001_0
18/03/25 04:24:20 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 218, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->218
18/03/25 04:24:20 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local1549038765_0001_m_000000_0 decomp: 218 len: 222 to MEMORY
18/03/25 04:24:20 INFO reduce.InMemoryMapOutput: Read 218 bytes from map-output for attempt_local1549038765_0001_m_000000_0
18/03/25 04:24:20 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 218, inMemoryMapOutputs.size() -> 2, commitMemory -> 218, usedMemory ->436
18/03/25 04:24:20 INFO reduce.EventFetcher: EventFetcher is interrupted.. Returning
18/03/25 04:24:20 INFO mapred.LocalJobRunner: 2 / 2 copied.
18/03/25 04:24:20 INFO reduce.MergeManagerImpl: finalMerge called with 2 in-memory map-outputs and 0 on-disk map-outputs
18/03/25 04:24:20 INFO mapred.Merger: Merging 2 sorted segments
18/03/25 04:24:20 INFO mapred.Merger: Down to the last merge-pass, with 2 segments left of total size: 424 bytes
18/03/25 04:24:20 INFO reduce.MergeManagerImpl: Merged 2 segments, 436 bytes to disk to satisfy reduce memory limit
18/03/25 04:24:20 INFO reduce.MergeManagerImpl: Merging 1 files, 438 bytes from disk
18/03/25 04:24:20 INFO reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
18/03/25 04:24:20 INFO mapred.Merger: Merging 1 sorted segments
18/03/25 04:24:20 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 428 bytes
18/03/25 04:24:20 INFO mapred.LocalJobRunner: 2 / 2 copied.
18/03/25 04:24:20 INFO Configuration.deprecation: mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
18/03/25 04:24:20 INFO mapred.Task: Task:attempt_local1549038765_0001_r_000000_0 is done. And is in the process of committing
18/03/25 04:24:20 INFO mapred.LocalJobRunner: 2 / 2 copied.
18/03/25 04:24:20 INFO mapred.Task: Task attempt_local1549038765_0001_r_000000_0 is allowed to commit now
18/03/25 04:24:20 INFO output.FileOutputCommitter: Saved output of task 'attempt_local1549038765_0001_r_000000_0' to hdfs://master:9000/matrix/out/_temporary/0/task_local1549038765_0001_r_000000
18/03/25 04:24:20 INFO mapred.LocalJobRunner: reduce > reduce
18/03/25 04:24:20 INFO mapred.Task: Task 'attempt_local1549038765_0001_r_000000_0' done.
18/03/25 04:24:20 INFO mapred.LocalJobRunner: Finishing task: attempt_local1549038765_0001_r_000000_0
18/03/25 04:24:20 INFO mapred.LocalJobRunner: reduce task executor complete.
18/03/25 04:24:21 INFO mapreduce.Job:  map 100% reduce 100%
18/03/25 04:24:21 INFO mapreduce.Job: Job job_local1549038765_0001 completed successfully
18/03/25 04:24:21 INFO mapreduce.Job: Counters: 35
    File System Counters
        FILE: Number of bytes read=2139
        FILE: Number of bytes written=875557
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=234
        HDFS: Number of bytes written=42
        HDFS: Number of read operations=28
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=8
    Map-Reduce Framework
        Map input records=15
        Map output records=36
        Map output bytes=360
        Map output materialized bytes=444
        Input split bytes=186
        Combine input records=0
        Combine output records=0
        Reduce input groups=6
        Reduce shuffle bytes=444
        Reduce input records=36
        Reduce output records=6
        Spilled Records=72
        Shuffled Maps =2
        Failed Shuffles=0
        Merged Map outputs=2
        GC time elapsed (ms)=19
        Total committed heap usage (bytes)=907542528
    Shuffle Errors
        BAD_ID=0
        CONNECTION=0
        IO_ERROR=0
        WRONG_LENGTH=0
        WRONG_MAP=0
        WRONG_REDUCE=0
    File Input Format Counters 
        Bytes Read=90
    File Output Format Counters 
        Bytes Written=42

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