如何使用Hadoop读写数据库
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2022-05-25 10:10:16
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在我们的一些应用程序中,常常避免不了要与数据库进行交互,而在我们的hadoop中,有时候也需要和数据库进行交互,比如说,数据分析的结果存入数据库,或者是,读取数据库的信息写入HDFS上,不过直接使用MapReduce操作数据库,这种情况在现实开发还是比较少,一般我们会采用Sqoop来进行数据的迁入,迁出,使用Hive分析数据集,大多数情况下,直接使用Hadoop访问关系型数据库,可能产生比较大的数据访问压力,尤其是在数据库还是单机的情况下,情况可能更加糟糕,在集群的模式下压力会相对少一些。
那么,今天散仙就来看下,如何直接使用Hadoop1.2.0的MR来读写操作数据库,hadoop的API提供了DBOutputFormat和DBInputFormat这两个类,来进行与数据库交互,除此之外,我们还需要定义一个类似JAVA Bean的实体类,来与数据库的每行记录进行对应,通常这个类要实现Writable和DBWritable接口,来重写里面的4个方法以对应获取每行记录里面的各个字段信息。
下面,我们先来看下如何使用MR来读取数据库的数据,并写入HDFS上,
数据表的截图如下所示,
实体类定义代码:
MR类的定义代码,注意是一个Map Only作业:
写入到HDFS目录下数据集:
读取相对比较简单,需要注意的第一注意JDBC的驱动jar包要在各个节点上分别上传一份,第二是在main方法里的驱动类的编写顺序,以及数据信息的完整,才是正确连接数据库并读取的关键。
下面来看下,如何使用MR,分析完数据后的结果,写入在数据库中,散仙本篇测试的是一个简单的WordCount的统计。我们先来看下数据库表的信息:
实体类定义代码:
统计的2个文件的内容所示:
MR的核心类代码:
运行状态如下所示:
最后,我们就可以去数据库里,查看统计的信息了,截图如下:
至此,我们就完成了使用MR来读写数据库了,注意测试前,先确保自己的hadoop集群,可以正常工作。
那么,今天散仙就来看下,如何直接使用Hadoop1.2.0的MR来读写操作数据库,hadoop的API提供了DBOutputFormat和DBInputFormat这两个类,来进行与数据库交互,除此之外,我们还需要定义一个类似JAVA Bean的实体类,来与数据库的每行记录进行对应,通常这个类要实现Writable和DBWritable接口,来重写里面的4个方法以对应获取每行记录里面的各个字段信息。
下面,我们先来看下如何使用MR来读取数据库的数据,并写入HDFS上,
数据表的截图如下所示,
实体类定义代码:
package com.qin.operadb; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import java.sql.PreparedStatement; import java.sql.ResultSet; import java.sql.SQLException; import org.apache.hadoop.io.Text; import org.apache.hadoop.io.Writable; import org.apache.hadoop.mapreduce.lib.db.DBWritable; /*** * 封装数据库实体信息 * 的记录 * * 搜索大数据技术交流群:376932160 * * **/ public class PersonRecoder implements Writable,DBWritable { public int id;//对应数据库中id字段 public String name;//对应数据库中的name字段 public int age;//对应数据库中的age字段 @Override public void readFields(ResultSet result) throws SQLException { this.id=result.getInt(1); this.name=result.getString(2); this.age=result.getInt(3); } @Override public void write(PreparedStatement stmt) throws SQLException { stmt.setInt(1, id); stmt.setString(2, name); stmt.setInt(3, age); } @Override public void readFields(DataInput arg0) throws IOException { // TODO Auto-generated method stub this.id=arg0.readInt(); this.name=Text.readString(arg0); this.age=arg0.readInt(); } @Override public void write(DataOutput out) throws IOException { // TODO Auto-generated method stub out.writeInt(id); Text.writeString(out, this.name); out.writeInt(this.age); } @Override public String toString() { // TODO Auto-generated method stub return "id: "+id+" 年龄: "+age+" 名字:"+name; } }
MR类的定义代码,注意是一个Map Only作业:
package com.qin.operadb; import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapred.JobConf; import org.apache.hadoop.mapred.lib.IdentityReducer; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.lib.db.DBConfiguration; import org.apache.hadoop.mapreduce.lib.db.DBInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class ReadMapDB { /** * Map作业读取数据记录数 * * **/ private static class DBMap extends Mapper<LongWritable, PersonRecoder , LongWritable, Text>{ @Override protected void map(LongWritable key, PersonRecoder value,Context context) throws IOException, InterruptedException { context.write(new LongWritable(value.id), new Text(value.toString())); } } public static void main(String[] args)throws Exception { JobConf conf=new JobConf(ReadMapDB.class); //Configuration conf=new Configuration(); // conf.set("mapred.job.tracker","192.168.75.130:9001"); //读取person中的数据字段 // conf.setJar("tt.jar"); //注意这行代码放在最前面,进行初始化,否则会报 DBConfiguration.configureDB(conf, "com.mysql.jdbc.Driver", "jdbc:mysql://192.168.211.36:3306/test", "root", "qin"); /**要读取的字段信息**/ String fileds[]=new String[]{"id","name","age"}; /**Job任务**/ Job job=new Job(conf, "readDB"); System.out.println("模式: "+conf.get("mapred.job.tracker")); /**设置数据库输入格式的一些信息**/ DBInputFormat.setInput(job, PersonRecoder.class, "person", null, "id", fileds); /***设置输入格式*/ job.setInputFormatClass(DBInputFormat.class); job.setOutputKeyClass(LongWritable.class); job.setOutputValueClass(Text.class); job.setMapperClass(DBMap.class); String path="hdfs://192.168.75.130:9000/root/outputdb"; FileSystem fs=FileSystem.get(conf); Path p=new Path(path); if(fs.exists(p)){ fs.delete(p, true); System.out.println("输出路径存在,已删除!"); } FileOutputFormat.setOutputPath(job,p ); System.exit(job.waitForCompletion(true) ? 0 : 1); } }
写入到HDFS目录下数据集:
读取相对比较简单,需要注意的第一注意JDBC的驱动jar包要在各个节点上分别上传一份,第二是在main方法里的驱动类的编写顺序,以及数据信息的完整,才是正确连接数据库并读取的关键。
下面来看下,如何使用MR,分析完数据后的结果,写入在数据库中,散仙本篇测试的是一个简单的WordCount的统计。我们先来看下数据库表的信息:
实体类定义代码:
package com.qin.operadb; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import java.sql.PreparedStatement; import java.sql.ResultSet; import java.sql.SQLException; import org.apache.hadoop.io.Text; import org.apache.hadoop.io.Writable; import org.apache.hadoop.mapreduce.lib.db.DBWritable; public class WordRecoder implements Writable,DBWritable { public String word; public int count; @Override public void readFields(ResultSet rs) throws SQLException { this.word=rs.getString(1); this.count=rs.getInt(2); } @Override public void write(PreparedStatement ps) throws SQLException { ps.setString(1, this.word); ps.setInt(2, this.count); } @Override public void readFields(DataInput in) throws IOException { this.word=Text.readString(in); this.count=in.readInt(); } @Override public void write(DataOutput out) throws IOException { Text.writeString(out, this.word); out.writeInt(count); } }
统计的2个文件的内容所示:
MR的核心类代码:
package com.qin.operadb; import java.io.IOException; import java.util.StringTokenizer; import org.apache.hadoop.fs.FileSystem; 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.mapred.JobConf; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.db.DBConfiguration; import org.apache.hadoop.mapreduce.lib.db.DBInputFormat; import org.apache.hadoop.mapreduce.lib.db.DBOutputFormat; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class WriteMapDB { private static class WMap extends Mapper<LongWritable, Text, Text, IntWritable>{ /*** * Mapper的参数类型介绍 * K,V,K,V分别依次代表 * Map作业输入类型的K,输入类型的V * 后面两个是输出类型的K,输出类型的V * 后面的两个与 context.write(word, one); * 的两个参数是对应的 * **/ private Text word=new Text(); private IntWritable one=new IntWritable(1); @Override protected void map(LongWritable key, Text value,Context context) throws IOException, InterruptedException { String line=value.toString(); //处理记事本UTF-8的BOM问题 if (line.getBytes().length > 0) { if ((int) line.charAt(0) == 65279) { line = line.substring(1); } } StringTokenizer st=new StringTokenizer(line); while(st.hasMoreTokens()){ word.set(st.nextToken());//设置单词 context.write(word, one); } } } /*** * 由于在reduce中,需要向数据库里写入 * 数据,所以跟combine,不能共用 * * * * ***/ private static class WCombine extends Reducer<Text, IntWritable, Text, IntWritable>{ @Override protected void reduce(Text text, Iterable<IntWritable> value,Context context) throws IOException, InterruptedException { int sum=0; for(IntWritable iw:value){ sum+=iw.get(); } context.write(text, new IntWritable(sum)); } } /** * * Reduce类 * * **/ private static class WReduce extends Reducer<Text, IntWritable, WordRecoder, Text>{ @Override protected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException { int sum=0; for(IntWritable s:values){ sum+=s.get(); } WordRecoder wr=new WordRecoder(); wr.word=key.toString(); wr.count=sum; //写出到数据库里 context.write(wr, new Text()); } } public static void main(String[] args)throws Exception { JobConf conf=new JobConf(WriteMapDB.class); //Configuration conf=new Configuration(); // conf.set("mapred.job.tracker","192.168.75.130:9001"); //读取person中的数据字段 //conf.setJar("tt.jar"); // conf.setNumReduceTasks(1); //注意这行代码放在最前面,进行初始化,否则会报 /**建立数据库连接**/ DBConfiguration.configureDB(conf, "com.mysql.jdbc.Driver", "jdbc:mysql://192.168.211.36:3306/test?characterEncoding=utf-8", "root", "qin"); String fileds[]=new String[]{"word","count"}; Job job=new Job(conf, "writeDB"); System.out.println("运行模式: "+conf.get("mapred.job.tracker")); /**设置输出表的的信息 第一个参数是job任务,第二个参数是表名,第三个参数字段项**/ DBOutputFormat.setOutput(job, "wordresult", fileds); /**设置DB的输入路径**/ job.setInputFormatClass(TextInputFormat.class); /**设置DB的输出路径**/ job.setOutputFormatClass(DBOutputFormat.class); /***设置Reduce的个数为1,可以得到全局统计的数字 * 但,需要注意,在分布式环境下,最好不要设置为1,Reduce的个数 * 正是Hadoop并发能力的体现 * * **/ // job.setNumReduceTasks(1); /**设置输出K路径**/ job.setOutputKeyClass(Text.class); /**设置输出V路径**/ job.setOutputValueClass(IntWritable.class); /**设置Map类**/ job.setMapperClass(WMap.class); /**设置Combiner类**/ job.setCombinerClass(WCombine.class); /**设置Reduce类**/ job.setReducerClass(WReduce.class); /**设置输入路径*/ FileInputFormat.setInputPaths(job, new Path("hdfs://192.168.75.130:9000/root/input")); System.exit(job.waitForCompletion(true) ? 0 : 1); } }
运行状态如下所示:
运行模式: 192.168.75.130:9001 14/03/26 20:26:59 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same. 14/03/26 20:27:01 INFO input.FileInputFormat: Total input paths to process : 2 14/03/26 20:27:01 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 14/03/26 20:27:01 WARN snappy.LoadSnappy: Snappy native library not loaded 14/03/26 20:27:01 INFO mapred.JobClient: Running job: job_201403262328_0006 14/03/26 20:27:02 INFO mapred.JobClient: map 0% reduce 0% 14/03/26 20:27:10 INFO mapred.JobClient: map 50% reduce 0% 14/03/26 20:27:11 INFO mapred.JobClient: map 100% reduce 0% 14/03/26 20:27:18 INFO mapred.JobClient: map 100% reduce 33% 14/03/26 20:27:19 INFO mapred.JobClient: map 100% reduce 100% 14/03/26 20:27:20 INFO mapred.JobClient: Job complete: job_201403262328_0006 14/03/26 20:27:20 INFO mapred.JobClient: Counters: 28 14/03/26 20:27:20 INFO mapred.JobClient: Job Counters 14/03/26 20:27:20 INFO mapred.JobClient: Launched reduce tasks=1 14/03/26 20:27:20 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=10345 14/03/26 20:27:20 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0 14/03/26 20:27:20 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0 14/03/26 20:27:20 INFO mapred.JobClient: Launched map tasks=2 14/03/26 20:27:20 INFO mapred.JobClient: Data-local map tasks=2 14/03/26 20:27:20 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=8911 14/03/26 20:27:20 INFO mapred.JobClient: File Output Format Counters 14/03/26 20:27:20 INFO mapred.JobClient: Bytes Written=0 14/03/26 20:27:20 INFO mapred.JobClient: FileSystemCounters 14/03/26 20:27:20 INFO mapred.JobClient: FILE_BYTES_READ=158 14/03/26 20:27:20 INFO mapred.JobClient: HDFS_BYTES_READ=325 14/03/26 20:27:20 INFO mapred.JobClient: FILE_BYTES_WRITTEN=182065 14/03/26 20:27:20 INFO mapred.JobClient: File Input Format Counters 14/03/26 20:27:20 INFO mapred.JobClient: Bytes Read=107 14/03/26 20:27:20 INFO mapred.JobClient: Map-Reduce Framework 14/03/26 20:27:20 INFO mapred.JobClient: Map output materialized bytes=164 14/03/26 20:27:20 INFO mapred.JobClient: Map input records=6 14/03/26 20:27:20 INFO mapred.JobClient: Reduce shuffle bytes=164 14/03/26 20:27:20 INFO mapred.JobClient: Spilled Records=24 14/03/26 20:27:20 INFO mapred.JobClient: Map output bytes=185 14/03/26 20:27:20 INFO mapred.JobClient: Total committed heap usage (bytes)=336338944 14/03/26 20:27:20 INFO mapred.JobClient: CPU time spent (ms)=2850 14/03/26 20:27:20 INFO mapred.JobClient: Combine input records=20 14/03/26 20:27:20 INFO mapred.JobClient: SPLIT_RAW_BYTES=218 14/03/26 20:27:20 INFO mapred.JobClient: Reduce input records=12 14/03/26 20:27:20 INFO mapred.JobClient: Reduce input groups=8 14/03/26 20:27:20 INFO mapred.JobClient: Combine output records=12 14/03/26 20:27:20 INFO mapred.JobClient: Physical memory (bytes) snapshot=464982016 14/03/26 20:27:20 INFO mapred.JobClient: Reduce output records=8 14/03/26 20:27:20 INFO mapred.JobClient: Virtual memory (bytes) snapshot=2182836224 14/03/26 20:27:20 INFO mapred.JobClient: Map output records=20
最后,我们就可以去数据库里,查看统计的信息了,截图如下:
至此,我们就完成了使用MR来读写数据库了,注意测试前,先确保自己的hadoop集群,可以正常工作。
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