There are cases where we need to write more than one MapReduce Job.
Map1--Reduce1--Map2--Reduce2
How do you manage the jobs so they are executed in order? There are several approaches, Here is an approach to easily chain jobs together by writing multiple driver methods, one for each job:
import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.FileStatus; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner; /** * @author Unmesha SreeVeni U.B * */ public class ChainJobs extends Configured implements Tool { private static final String OUTPUT_PATH = "intermediate_output"; @Override public int run(String[] args) throws Exception { /* * Job 1 */ Configuration conf = getConf(); FileSystem fs = FileSystem.get(conf); Job job = new Job(conf, "Job1"); job.setJarByClass(ChainJobs.class); job.setMapperClass(MyMapper1.class); job.setReducerClass(MyReducer1.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); TextInputFormat.addInputPath(job, new Path(args[0])); TextOutputFormat.setOutputPath(job, new Path(OUTPUT_PATH)); job.waitForCompletion(true); /* * Job 2 */ Job job2 = new Job(conf, "Job 2"); job2.setJarByClass(ChainJobs.class); job2.setMapperClass(MyMapper2.class); job2.setReducerClass(MyReducer2.class); job2.setOutputKeyClass(Text.class); job2.setOutputValueClass(Text.class); job2.setInputFormatClass(TextInputFormat.class); job2.setOutputFormatClass(TextOutputFormat.class); TextInputFormat.addInputPath(job2, new Path(OUTPUT_PATH)); TextOutputFormat.setOutputPath(job2, new Path(args[1])); return job2.waitForCompletion(true) ? 0 : 1; } /** * Method Name: main Return type: none Purpose:Read the arguments from * command line and run the Job till completion * */ public static void main(String[] args) throws Exception { // TODO Auto-generated method stub if (args.length != 2) { System.err.println("Enter valid number of arguments <Inputdirectory> <Outputlocation>"); System.exit(0); } ToolRunner.run(new Configuration(), new ChainJobs(), args); } }
The above code has 2 jobs named job1 and job2
private static final String OUTPUT_PATH = "intermediate_output";
String "OUTPUT_PATH" is used to write the output for first job.
TextInputFormat.addInputPath(job, new Path(args[0]));
TextOutputFormat.setOutputPath(job, new Path(OUTPUT_PATH));
So in first job our input will be args[0] and output will be new Path(OUTPUT_PATH).
First Job Configuration
/* * Job 1 */ Configuration conf = getConf(); FileSystem fs = FileSystem.get(conf); Job job = new Job(conf, "Job1"); job.setJarByClass(ChainJobs1.class); job.setMapperClass(MyMapper1.class); job.setReducerClass(MyReducer1.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); TextInputFormat.addInputPath(job, new Path(args[0])); TextOutputFormat.setOutputPath(job, new Path(OUTPUT_PATH)); job.waitForCompletion(true);
Once the first job has executed successfully "OUTPUT_PATH" is served as the input to second job and the output of job2 is written to args[1].
TextInputFormat.addInputPath(job2, new Path(OUTPUT_PATH));
TextOutputFormat.setOutputPath(job2, new Path(args[1]));
Second Job Configuration
/* * Job 2 */ Job job2 = new Job(conf, "Job 2"); job2.setJarByClass(ChainJobs1.class); job2.setMapperClass(MyMapper2.class); job2.setReducerClass(MyReducer2.class); job2.setOutputKeyClass(Text.class); job2.setOutputValueClass(Text.class); job2.setInputFormatClass(TextInputFormat.class); job2.setOutputFormatClass(TextOutputFormat.class); TextInputFormat.addInputPath(job2, new Path(OUTPUT_PATH)); TextOutputFormat.setOutputPath(job2, new Path(args[1])); return job2.waitForCompletion(true) ? 0 : 1;
Happy Hadooping . . .
Where is the code for ChainJobs1.java and ChainJobs2.java?
ReplyDeleteThere is no ChainJobs2.java. Apologies for confusing and Thanks for pointing out. I updated the post.
DeleteSorry I am new at Hadoop. Could you please give some examples on how to read the file from map/ reduce function? Do you just do fs.open(), or is there any build in magic from TextInputFormat.addInputPath()?
ReplyDeleteThanks!
You can read files in MapReduce job using TextInputFormat. Supply your file in TextInputFormat and read them in map function. You can also read files from Distributed cache in setup function.
DeleteLet me know if you have further doubts.
Thank you very much!
DeleteThank you very much for such a helpful post..
ReplyDeleteKeep posting such stuffs in Hadoop.
Nishit
sure
DeleteThe second job doesnt seem to run for me.. THe mapper setup runs but not the map function within the second mapper. Is it because of format issues. Coz otherwise there doesnt seem to be anything wrong in my program
ReplyDeletecould u please share ur code? Or else you can ping me in unmeshabiju@gmail.com
DeleteHi,
ReplyDeleteI am running a hadoop chainjobs. While running it with low data sets(i.e. 10-20 files) it is working perfectly but while running with more than 30 files after the first job the second job gets an error connection refuse. Already tried 2 times something like that. Can you please let me know why I am facing this issue. I have also gone with adddepending job but with that the output path for the job2 is not getting validated.
Thanks,
Shuvankar
Can you please paste the error
DeleteHi unmesha sreeveni, great post! you saved me! :D
ReplyDeleteI found some errors, like fileNotFoundException. and i solved it adding "/part-r-00000" (the name of the outputfile)
I my application i am trying to do the GIM-V algorithm that basicly is multiply a matrix by a vector, and again by the vector result and again and so on.
finally i did a cycle for all the new jobs, something like this, check.
Configuration conf = getConf();
Job job = new Job(conf, "matrix-multiply-vector");
// See Amareshwari Sri Ramadasu's comment in this thread...
// http://lucene.472066.n3.nabble.com/Distributed-Cache-with-New-API-td722187.html
// you need to do job.getConfiguration() instead of conf.
DistributedCache.addCacheFile(new Path(args[1]).toUri(),
job.getConfiguration());
job.setJarByClass(MatrixMultiplyVector.class);
job.setMapperClass(Mapper1.class);
job.setReducerClass(Reducer1.class);
job.setMapOutputKeyClass(LongWritable.class);
job.setMapOutputValueClass(DoubleWritable.class);
job.setInputFormatClass(TextInputFormat.class);
//setoutputFormat...
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[2]));
boolean succ = job.waitForCompletion(true);
int nroRepeticiones =Integer.parseInt(args[3]);
String salida = args[2];
String nuevaSalida=salida;
for(int i=1;i<nroRepeticiones;i++){
Configuration conf2 = new Configuration();
Job job2 = new Job(conf2, "ENCADENADOJOB");
// See Amareshwari Sri Ramadasu's comment in this thread...
// http://lucene.472066.n3.nabble.com/Distributed-Cache-with-New-API-td722187.html
// you need to do job.getConfiguration() instead of conf.
DistributedCache.addCacheFile(new Path(nuevaSalida+"/part-r-00000").toUri(),
job2.getConfiguration());
job2.setJarByClass(MatrixMultiplyVector.class);
job2.setMapperClass(Mapper1.class);
job2.setReducerClass(Reducer1.class);
job2.setMapOutputKeyClass(LongWritable.class);
job2.setMapOutputValueClass(DoubleWritable.class);
job2.setInputFormatClass(TextInputFormat.class);
//setoutputFormat...
nuevaSalida = salida+"-"+String.valueOf(i);
FileInputFormat.addInputPath(job2, new Path(args[0]));
FileOutputFormat.setOutputPath(job2, new Path(nuevaSalida));
System.out.println("-----iteracion:"+i);
succ = job2.waitForCompletion(true);
}
return 5;
Thank you again :D
Thanks.
DeleteYes for distributed cache you need to mention the part file aswell, but if you are writing a MR job you need to only specify the folder.
Nice work Unmesha. I will try out the code, meanwhile I have few question.
ReplyDelete1. As the OUTPUT_PATH is intermediate output, where does it store, HDFS or Local Disk (Like mappers).
2. Does it persist or gets deleted after job finishes. If it persists can we see the file contents (will it be serialized)
The intermediate output is written into HDFS only , that is how you can use the output path of the first job as the input for the next
DeleteFollowing the above question, is it necessary to store the results in hdfs, is there any way we redirect it to next mapper without wasting resources on creating new file
DeleteThanks for the blog its really helpful.The chaining job is very interesting one.Thanks for the nice blog.Besant Technologies Reviews | Besant Technologies Reviews
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Nice example. But if I need to chain n jobs where n is not predefined, then what should be done? Let's say for an iterative algorithm that terminates only when certain conditions are met.
ReplyDeleteI am using the same example but when it is executing second job. It is saying input file not found. Also output file not getting created after first job executed successfully.
ReplyDeletexception in thread "main" org.apache.hadoop.mapreduce.lib.input.InvalidInputException: Input path does not exist: hdfs://localhost:54310/user/output1232
at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:321)
at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.listStatus(FileInputFormat.java:264)
at org.apache.hadoop.mapreduce.lib.input.FileInputFormat.getSplits(FileInputFormat.java:385)
at org.apache.hadoop.mapreduce.lib.input.DelegatingInputFormat.getSplits(DelegatingInputFormat.java:115)
at org.apache.hadoop.mapreduce.JobSubmitter.writeNewSplits(JobSubmitter.java:597)
at org.apache.hadoop.mapreduce.JobSubmitter.writeSplits(JobSubmitter.java:614)
at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:492)
at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1296)
at org.apache.hadoop.mapreduce.Job$10.run(Job.java:1293)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1628)
at org.apache.hadoop.mapreduce.Job.submit(Job.java:1293)
at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:1314)
at com.hadoop.intellipaat.JoinClickImpressionDetailJob.run(JoinClickImpressionDetailJob.java:418)
at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:70)
at com.hadoop.intellipaat.JoinClickImpressionDetailJob.main(JoinClickImpressionDetailJob.java:422)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.util.RunJar.run(RunJar.java:221)
at org.apache.hadoop.util.RunJar.main(RunJar.java:136)
Can u post your driver class code snippet?
Deletevery very helpful!
ReplyDeleteHi Unmesha sreeveni,
ReplyDeleteThanks a lot for detailed explanation...Very Helpful.
I am a new beginner in Hadoop. I dont know why these errors in DriverCode.
could u please advice me.
Driver code is sent to this mail. unmeshabiju@gmail.com
ReplyDeleteIn Hadoop, MapReduce is a calculation that decomposes large manipulation jobs into individual tasks that can be executed in parallel cross a cluster of servers. The results of tasks can be joined together to compute final results.
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Hello. I am trying to create a chain joib in hadoop. The algorithm I want to create requests map2 to get as an input the output from the map1 . The Job1 have both map and reduce phase. Is there any possible way something like this to happen?
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