mapred-site.xml里面配置运行日志的输出目录
用hadoop也算有一段时间了,一直没有注意过hadoop运行过程中,产生的数据日志,比如说System打印的日志,或者是log4j,slf4j等记录的日志,存放在哪里,日志信息的重要性,在这里散仙就不用多说了,调试任何程序基本上都得需要分析日志。
hadoop的日志主要是MapReduce程序,运行过程中,产生的一些数据日志,除了系统的日志外,还包含一些我们自己在测试时候,或者线上环境输出的日志,这部分日志通常会被放在userlogs这个文件夹下面,我们可以在mapred-site.xml里面配置运行日志的输出目录,散仙测试文件内容如下:
package com.qin.testdistributed; import java.io.File; import java.io.FileReader; import java.io.IOException; import java.net.URI; import java.util.Scanner; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.filecache.DistributedCache; import org.apache.hadoop.fs.FSDataInputStream; 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.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.log4j.pattern.LogEvent; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import com.qin.operadb.WriteMapDB; /** * 测试hadoop的全局共享文件 * 使用DistributedCached * * 大数据技术交流群: 37693216 * @author qindongliang * * ***/ public class TestDistributed { private static Logger logger=LoggerFactory.getLogger(TestDistributed.class); private static class FileMapper extends Mapper<LongWritable, Text, Text, IntWritable>{ Path path[]=null; /** * Map函数前调用 * * */ @Override protected void setup(Context context) throws IOException, InterruptedException { logger.info("开始启动setup了哈哈哈哈"); // System.out.println("运行了........."); Configuration conf=context.getConfiguration(); path=DistributedCache.getLocalCacheFiles(conf); System.out.println("获取的路径是: "+path[0].toString()); // FileSystem fs = FileSystem.get(conf); FileSystem fsopen= FileSystem.getLocal(conf); // FSDataInputStream in = fsopen.open(path[0]); // System.out.println(in.readLine()); // for(Path tmpRefPath : path) { // if(tmpRefPath.toString().indexOf("ref.png") != -1) { // in = reffs.open(tmpRefPath); // break; // } // } // FileReader reader=new FileReader("file://"+path[0].toString()); // File f=new File("file://"+path[0].toString()); // FSDataInputStream in=fs.open(new Path(path[0].toString())); // Scanner scan=new Scanner(in); // while(scan.hasNext()){ // System.out.println(Thread.currentThread().getName()+"扫描的内容: "+scan.next()); // } // scan.close(); // // System.out.println("size: "+path.length); } @Override protected void map(LongWritable key, Text value,Context context) throws IOException, InterruptedException { // System.out.println("map aaa"); //logger.info("Map里的任务"); System.out.println("map里输出了"); // logger.info(); context.write(new Text(""), new IntWritable(0)); } @Override protected void cleanup(Context context) throws IOException, InterruptedException { logger.info("清空任务了。。。。。。"); } } private static class FileReduce extends Reducer<Object, Object, Object, Object>{ @Override protected void reduce(Object arg0, Iterable<Object> arg1, Context arg2)throws IOException, InterruptedException { System.out.println("我是reduce里面的东西"); } } public static void main(String[] args)throws Exception { JobConf conf=new JobConf(TestDistributed.class); //conf.set("mapred.local.dir", "/root/hadoop"); //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 String inputPath="hdfs://192.168.75.130:9000/root/input"; String outputPath="hdfs://192.168.75.130:9000/root/outputsort"; Job job=new Job(conf, "a"); DistributedCache.addCacheFile(new URI("hdfs://192.168.75.130:9000/root/input/f1.txt"), job.getConfiguration()); job.setJarByClass(TestDistributed.class); 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**/ FileSystem fs=FileSystem.get(job.getConfiguration()); Path pout=new Path(outputPath); if(fs.exists(pout)){ fs.delete(pout, true); System.out.println("存在此路径, 已经删除......"); } /**设置Map类**/ // job.setOutputKeyClass(Text.class); //job.setOutputKeyClass(IntWritable.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); job.setMapperClass(FileMapper.class); job.setReducerClass(FileReduce.class); FileInputFormat.setInputPaths(job, new Path(inputPath)); //输入路径 FileOutputFormat.setOutputPath(job, new Path(outputPath));//输出路径 System.exit(job.waitForCompletion(true) ? 0 : 1); } }
package com.qin.testdistributed; import java.io.File; import java.io.FileReader; import java.io.IOException; import java.net.URI; import java.util.Scanner; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.filecache.DistributedCache; import org.apache.hadoop.fs.FSDataInputStream; 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.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.log4j.pattern.LogEvent; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import com.qin.operadb.WriteMapDB; /** * 测试hadoop的全局共享文件 * 使用DistributedCached * * 大数据技术交流群: 37693216 * @author qindongliang * * ***/ public class TestDistributed { private static Logger logger=LoggerFactory.getLogger(TestDistributed.class); private static class FileMapper extends Mapper<LongWritable, Text, Text, IntWritable>{ Path path[]=null; /** * Map函数前调用 * * */ @Override protected void setup(Context context) throws IOException, InterruptedException { logger.info("开始启动setup了哈哈哈哈"); // System.out.println("运行了........."); Configuration conf=context.getConfiguration(); path=DistributedCache.getLocalCacheFiles(conf); System.out.println("获取的路径是: "+path[0].toString()); // FileSystem fs = FileSystem.get(conf); FileSystem fsopen= FileSystem.getLocal(conf); // FSDataInputStream in = fsopen.open(path[0]); // System.out.println(in.readLine()); // for(Path tmpRefPath : path) { // if(tmpRefPath.toString().indexOf("ref.png") != -1) { // in = reffs.open(tmpRefPath); // break; // } // } // FileReader reader=new FileReader("file://"+path[0].toString()); // File f=new File("file://"+path[0].toString()); // FSDataInputStream in=fs.open(new Path(path[0].toString())); // Scanner scan=new Scanner(in); // while(scan.hasNext()){ // System.out.println(Thread.currentThread().getName()+"扫描的内容: "+scan.next()); // } // scan.close(); // // System.out.println("size: "+path.length); } @Override protected void map(LongWritable key, Text value,Context context) throws IOException, InterruptedException { // System.out.println("map aaa"); //logger.info("Map里的任务"); System.out.println("map里输出了"); // logger.info(); context.write(new Text(""), new IntWritable(0)); } @Override protected void cleanup(Context context) throws IOException, InterruptedException { logger.info("清空任务了。。。。。。"); } } private static class FileReduce extends Reducer<Object, Object, Object, Object>{ @Override protected void reduce(Object arg0, Iterable<Object> arg1, Context arg2)throws IOException, InterruptedException { System.out.println("我是reduce里面的东西"); } } public static void main(String[] args)throws Exception { JobConf conf=new JobConf(TestDistributed.class); //conf.set("mapred.local.dir", "/root/hadoop"); //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 String inputPath="hdfs://192.168.75.130:9000/root/input"; String outputPath="hdfs://192.168.75.130:9000/root/outputsort"; Job job=new Job(conf, "a"); DistributedCache.addCacheFile(new URI("hdfs://192.168.75.130:9000/root/input/f1.txt"), job.getConfiguration()); job.setJarByClass(TestDistributed.class); 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**/ FileSystem fs=FileSystem.get(job.getConfiguration()); Path pout=new Path(outputPath); if(fs.exists(pout)){ fs.delete(pout, true); System.out.println("存在此路径, 已经删除......"); } /**设置Map类**/ // job.setOutputKeyClass(Text.class); //job.setOutputKeyClass(IntWritable.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); job.setMapperClass(FileMapper.class); job.setReducerClass(FileReduce.class); FileInputFormat.setInputPaths(job, new Path(inputPath)); //输入路径 FileOutputFormat.setOutputPath(job, new Path(outputPath));//输出路径 System.exit(job.waitForCompletion(true) ? 0 : 1); } }
Local模式下输出如下:
运行模式: local 存在此路径, 已经删除...... WARN - NativeCodeLoader.<clinit>(52) | Unable to load native-hadoop library for your platform... using builtin-java classes where applicable WARN - JobClient.copyAndConfigureFiles(746) | Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same. WARN - JobClient.copyAndConfigureFiles(870) | No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String). INFO - FileInputFormat.listStatus(237) | Total input paths to process : 1 WARN - LoadSnappy.<clinit>(46) | Snappy native library not loaded INFO - TrackerDistributedCacheManager.downloadCacheObject(423) | Creating f1.txt in /root/hadoop1.2/hadooptmp/mapred/local/archive/9070031930820799196_1788685676_88844454/192.168.75.130/root/input-work-186410214545932656 with rwxr-xr-x INFO - TrackerDistributedCacheManager.downloadCacheObject(463) | Cached hdfs://192.168.75.130:9000/root/input/f1.txt as /root/hadoop1.2/hadooptmp/mapred/local/archive/9070031930820799196_1788685676_88844454/192.168.75.130/root/input/f1.txt INFO - TrackerDistributedCacheManager.localizePublicCacheObject(486) | Cached hdfs://192.168.75.130:9000/root/input/f1.txt as /root/hadoop1.2/hadooptmp/mapred/local/archive/9070031930820799196_1788685676_88844454/192.168.75.130/root/input/f1.txt INFO - JobClient.monitorAndPrintJob(1380) | Running job: job_local479869714_0001 INFO - LocalJobRunner$Job.run(340) | Waiting for map tasks INFO - LocalJobRunner$Job$MapTaskRunnable.run(204) | Starting task: attempt_local479869714_0001_m_000000_0 INFO - Task.initialize(534) | Using ResourceCalculatorPlugin : null INFO - MapTask.runNewMapper(729) | Processing split: hdfs://192.168.75.130:9000/root/input/f1.txt:0+31 INFO - MapTask$MapOutputBuffer.<init>(949) | io.sort.mb = 100 INFO - MapTask$MapOutputBuffer.<init>(961) | data buffer = 79691776/99614720 INFO - MapTask$MapOutputBuffer.<init>(962) | record buffer = 262144/327680 INFO - TestDistributed$FileMapper.setup(57) | 开始启动setup了哈哈哈哈 获取的路径是: /root/hadoop1.2/hadooptmp/mapred/local/archive/9070031930820799196_1788685676_88844454/192.168.75.130/root/input/f1.txt map里输出了 map里输出了 INFO - TestDistributed$FileMapper.cleanup(107) | 清空任务了。。。。。。 INFO - MapTask$MapOutputBuffer.flush(1289) | Starting flush of map output INFO - MapTask$MapOutputBuffer.sortAndSpill(1471) | Finished spill 0 INFO - Task.done(858) | Task:attempt_local479869714_0001_m_000000_0 is done. And is in the process of commiting INFO - LocalJobRunner$Job.statusUpdate(466) | INFO - Task.sendDone(970) | Task 'attempt_local479869714_0001_m_000000_0' done. INFO - LocalJobRunner$Job$MapTaskRunnable.run(229) | Finishing task: attempt_local479869714_0001_m_000000_0 INFO - LocalJobRunner$Job.run(348) | Map task executor complete. INFO - Task.initialize(534) | Using ResourceCalculatorPlugin : null INFO - LocalJobRunner$Job.statusUpdate(466) | INFO - Merger$MergeQueue.merge(408) | Merging 1 sorted segments INFO - Merger$MergeQueue.merge(491) | Down to the last merge-pass, with 1 segments left of total size: 16 bytes INFO - LocalJobRunner$Job.statusUpdate(466) | 我是reduce里面的东西 INFO - Task.done(858) | Task:attempt_local479869714_0001_r_000000_0 is done. And is in the process of commiting INFO - LocalJobRunner$Job.statusUpdate(466) | INFO - Task.commit(1011) | Task attempt_local479869714_0001_r_000000_0 is allowed to commit now INFO - FileOutputCommitter.commitTask(173) | Saved output of task 'attempt_local479869714_0001_r_000000_0' to hdfs://192.168.75.130:9000/root/outputsort INFO - LocalJobRunner$Job.statusUpdate(466) | reduce > reduce INFO - Task.sendDone(970) | Task 'attempt_local479869714_0001_r_000000_0' done. INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 100% INFO - JobClient.monitorAndPrintJob(1448) | Job complete: job_local479869714_0001 INFO - Counters.log(585) | Counters: 18 INFO - Counters.log(587) | File Output Format Counters INFO - Counters.log(589) | Bytes Written=0 INFO - Counters.log(587) | File Input Format Counters INFO - Counters.log(589) | Bytes Read=31 INFO - Counters.log(587) | FileSystemCounters INFO - Counters.log(589) | FILE_BYTES_READ=454 INFO - Counters.log(589) | HDFS_BYTES_READ=124 INFO - Counters.log(589) | FILE_BYTES_WRITTEN=138372 INFO - Counters.log(587) | Map-Reduce Framework INFO - Counters.log(589) | Map output materialized bytes=20 INFO - Counters.log(589) | Map input records=2 INFO - Counters.log(589) | Reduce shuffle bytes=0 INFO - Counters.log(589) | Spilled Records=4 INFO - Counters.log(589) | Map output bytes=10 INFO - Counters.log(589) | Total committed heap usage (bytes)=455475200 INFO - Counters.log(589) | Combine input records=0 INFO - Counters.log(589) | SPLIT_RAW_BYTES=109 INFO - Counters.log(589) | Reduce input records=2 INFO - Counters.log(589) | Reduce input groups=1 INFO - Counters.log(589) | Combine output records=0 INFO - Counters.log(589) | Reduce output records=0 INFO - Counters.log(589) | Map output records=2
运行模式: local 存在此路径, 已经删除...... WARN - NativeCodeLoader.<clinit>(52) | Unable to load native-hadoop library for your platform... using builtin-java classes where applicable WARN - JobClient.copyAndConfigureFiles(746) | Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same. WARN - JobClient.copyAndConfigureFiles(870) | No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String). INFO - FileInputFormat.listStatus(237) | Total input paths to process : 1 WARN - LoadSnappy.<clinit>(46) | Snappy native library not loaded INFO - TrackerDistributedCacheManager.downloadCacheObject(423) | Creating f1.txt in /root/hadoop1.2/hadooptmp/mapred/local/archive/9070031930820799196_1788685676_88844454/192.168.75.130/root/input-work-186410214545932656 with rwxr-xr-x INFO - TrackerDistributedCacheManager.downloadCacheObject(463) | Cached hdfs://192.168.75.130:9000/root/input/f1.txt as /root/hadoop1.2/hadooptmp/mapred/local/archive/9070031930820799196_1788685676_88844454/192.168.75.130/root/input/f1.txt INFO - TrackerDistributedCacheManager.localizePublicCacheObject(486) | Cached hdfs://192.168.75.130:9000/root/input/f1.txt as /root/hadoop1.2/hadooptmp/mapred/local/archive/9070031930820799196_1788685676_88844454/192.168.75.130/root/input/f1.txt INFO - JobClient.monitorAndPrintJob(1380) | Running job: job_local479869714_0001 INFO - LocalJobRunner$Job.run(340) | Waiting for map tasks INFO - LocalJobRunner$Job$MapTaskRunnable.run(204) | Starting task: attempt_local479869714_0001_m_000000_0 INFO - Task.initialize(534) | Using ResourceCalculatorPlugin : null INFO - MapTask.runNewMapper(729) | Processing split: hdfs://192.168.75.130:9000/root/input/f1.txt:0+31 INFO - MapTask$MapOutputBuffer.<init>(949) | io.sort.mb = 100 INFO - MapTask$MapOutputBuffer.<init>(961) | data buffer = 79691776/99614720 INFO - MapTask$MapOutputBuffer.<init>(962) | record buffer = 262144/327680 INFO - TestDistributed$FileMapper.setup(57) | 开始启动setup了哈哈哈哈 获取的路径是: /root/hadoop1.2/hadooptmp/mapred/local/archive/9070031930820799196_1788685676_88844454/192.168.75.130/root/input/f1.txt map里输出了 map里输出了 INFO - TestDistributed$FileMapper.cleanup(107) | 清空任务了。。。。。。 INFO - MapTask$MapOutputBuffer.flush(1289) | Starting flush of map output INFO - MapTask$MapOutputBuffer.sortAndSpill(1471) | Finished spill 0 INFO - Task.done(858) | Task:attempt_local479869714_0001_m_000000_0 is done. And is in the process of commiting INFO - LocalJobRunner$Job.statusUpdate(466) | INFO - Task.sendDone(970) | Task 'attempt_local479869714_0001_m_000000_0' done. INFO - LocalJobRunner$Job$MapTaskRunnable.run(229) | Finishing task: attempt_local479869714_0001_m_000000_0 INFO - LocalJobRunner$Job.run(348) | Map task executor complete. INFO - Task.initialize(534) | Using ResourceCalculatorPlugin : null INFO - LocalJobRunner$Job.statusUpdate(466) | INFO - Merger$MergeQueue.merge(408) | Merging 1 sorted segments INFO - Merger$MergeQueue.merge(491) | Down to the last merge-pass, with 1 segments left of total size: 16 bytes INFO - LocalJobRunner$Job.statusUpdate(466) | 我是reduce里面的东西 INFO - Task.done(858) | Task:attempt_local479869714_0001_r_000000_0 is done. And is in the process of commiting INFO - LocalJobRunner$Job.statusUpdate(466) | INFO - Task.commit(1011) | Task attempt_local479869714_0001_r_000000_0 is allowed to commit now INFO - FileOutputCommitter.commitTask(173) | Saved output of task 'attempt_local479869714_0001_r_000000_0' to hdfs://192.168.75.130:9000/root/outputsort INFO - LocalJobRunner$Job.statusUpdate(466) | reduce > reduce INFO - Task.sendDone(970) | Task 'attempt_local479869714_0001_r_000000_0' done. INFO - JobClient.monitorAndPrintJob(1393) | map 100% reduce 100% INFO - JobClient.monitorAndPrintJob(1448) | Job complete: job_local479869714_0001 INFO - Counters.log(585) | Counters: 18 INFO - Counters.log(587) | File Output Format Counters INFO - Counters.log(589) | Bytes Written=0 INFO - Counters.log(587) | File Input Format Counters INFO - Counters.log(589) | Bytes Read=31 INFO - Counters.log(587) | FileSystemCounters INFO - Counters.log(589) | FILE_BYTES_READ=454 INFO - Counters.log(589) | HDFS_BYTES_READ=124 INFO - Counters.log(589) | FILE_BYTES_WRITTEN=138372 INFO - Counters.log(587) | Map-Reduce Framework INFO - Counters.log(589) | Map output materialized bytes=20 INFO - Counters.log(589) | Map input records=2 INFO - Counters.log(589) | Reduce shuffle bytes=0 INFO - Counters.log(589) | Spilled Records=4 INFO - Counters.log(589) | Map output bytes=10 INFO - Counters.log(589) | Total committed heap usage (bytes)=455475200 INFO - Counters.log(589) | Combine input records=0 INFO - Counters.log(589) | SPLIT_RAW_BYTES=109 INFO - Counters.log(589) | Reduce input records=2 INFO - Counters.log(589) | Reduce input groups=1 INFO - Counters.log(589) | Combine output records=0 INFO - Counters.log(589) | Reduce output records=0 INFO - Counters.log(589) | Map output records=2
下面,我们将程序,提交成hadoop集群上运行进行测试,注意在集群上运行,日志信息就不会在控制台显示了,我们需要去自己定义的日志目录下,找到最新提交 的那个下,然后就可以查看我们的日志信息了。
查看stdout里面的内容如下:
获取的路径是: /root/hadoop1.2/mylogs/taskTracker/distcache/2726204645197711229_1788685676_88844454/192.168.75.130/root/input/f1.txt map里输出了 map里输出了
获取的路径是: /root/hadoop1.2/mylogs/taskTracker/distcache/2726204645197711229_1788685676_88844454/192.168.75.130/root/input/f1.txt map里输出了 map里输出了
注意,map里面的日志需要去xxxmxxx和xxxrxxx里面去找:
当然,除了这种方式外,我们还可以直接通过50030端口在web页面上进行查看,截图示例如下:
至此,我们已经散仙已经介绍完了,这两种方式,Hadoop在执行过程中,日志会被随机分到任何一台节点上,我们可能不能确定本次提交的任务日志输出到底放在那里,但是我们可以通过在50030的web页面上,查看最新的一次任务,一般是最下面的任务,是最新提交的,通过页面上的连接我们就可以,查看到具体的本次任务的日志情况被随机分发到那个节点上了,然后就可以去具体的 节点上获取了。