I have to implement a loop of map-reduce jobs. Each iteration will terminate or continue depending on the previous one. The choice to be made is based on "is one word appears in the reducer output".
Of course I can inspect the whole output txt file with my driver program. But it is just a single word and going through the whole file will overkill. I am considering is there any way to build the communication between reducer and the driver, the reducer can notify the driver once it detects the word? Since the message to be transferred is few.
Your solution will be not a clean solution and hard to maintain.
There are multiple ways to achieve what you have asked for .
1. Reducer as soon as it finds a word writes to a HDFS location (opens file on hdfs predefine filedir and writes there)
2. client keeps polling the predefined filedir / output dir of the job. If the output dir is found and there is no filedir it means word wasnt there.
3. Use Zookeper
Best solution would be to , emit from mapper only if the word is found,
else not emit anything. This will fasten your job and spawn a single
reducer. Now you can safely check if the output of the job has any file on output or not. Use Lazy initialization, in case no rows comes to reducer no output file would be created
Related
I'm trying to write MapReduce job which it can Read two sequence file in the Mapper. I've tried read and write a sequence file in 'main' but I don't know how to do it in Mapper. I think that I'm not really familiar with how MapReduce work. Thanks for helping me out.
If everything is correct, in the main() method you wrote something like that:
FileInputFormat.addInputPath()
FileOutputFormat.setOutputPath()
to tell Hadoop the directory where to find your two input files and where to write the results of the computation.
When the job starts, Hadoop starts reading the files it finds in the input directory and it calls the map() method of the mapper passing to it every line of the file (one at the time) as an argument.
At the end of the computation, when the reducer emits its data, Hadoop is going to write the results in one (or more) files in the specified output directory.
So, the mapper and the reducer don't need to know anything about input/output files.
I have a pig script that generates some output to a HDFS directory. The pig script also generates a SUCCESS file in the same HDFS directory. The output of the pig script is split into multiple parts as the number of reducers to use in the script is defined via 'SET default_parallel n;'
I would like to now use Java to concatenate/merge all the file parts into a single file. I obviously want to ignore the SUCCESS file while concatenating. How can I do this in Java?
Thanks in advance.
you can use getmerge through shell command to merge multiple file into single file.
Usage: hdfs dfs -getmerge <srcdir> <destinationdir/file.txt>
Example: hdfs dfs -getmerge /output/dir/on/hdfs/ /desired/local/output/file.txt
In case you don't want to use shell command to do it. You can write a java program and can use FileUtil.copyMerge method to merge output file into single file. The implementation details are available in this link
if you want a single output on hdfs itself through pig then you need to pass it through single reducer. You need to set number of reducer 1 to do so. you need to put below line at the start of your script.
--Assigning only one reducer in order to generate only one output file.
SET default_parallel 1;
I hope this will help you.
The reason why this does not seem easy to do, is typically there would be little purpose. If I have a very large cluster, and I am really dealing with a Big Data problem, my output file as a single file would probably not fit onto any single machine.
That being said, I can see use metrics collections where maybe you want just output some metrics about your data, like counts.
In that case I would first run your MapReduce program,
Then create a 2nd map/reduce job that reads the data, and reduces all the elements to the single same reducer by using the a static key with your reduce function.
Or you could also just use a single mapper with your original program with
Job.setNumberOfReducer(1);
Can I append input files or input data to a map-reduce job while it's running without creating a race condition?
I think in theory you can add more files into the input as long as it:
Matches your FileInputFormat pattern
Happens before InputFormat.getSplits() call which really gives you a very short time after you submit a job.
Regarding the race condition after splits are computed, note that append to existing files is only available since the version 0.21.0.
And even if you can modify your files, your split points already precomputed and most likely your new data will not be picked up by mappers. Though, I doubt that it will lead to a crash of your flow.
What you can experiment with is to disable splits within a file (that is assign a mapper per file) and try to append. I think some data that had a chance to get flushed may end up in a mapper (that's just my wild guess).
Effectively the answer is "no". The splits are computed very early in the game: and after that your new files will not be included.
I have a job for hadoop. When the job is stated, i have some number of mappers started. And each mapper write some file to disk, like part-m-00000, part-m-00001. As I understand, each mapper create one part file. I have big amount of data, so there must be more than one mapper, but can I somehow control number of this output files? I mean, hadoop will start, for example 10 mappers, but there will be only three part files?
I found this post
How do multiple reducers output only one part-file in Hadoop?
But there is using old version of hadoop library. I'm using classes from org.apache.hadoop.mapreduce.* and not from org.apache.hadoop.mapred.*
I'm using hadoop version 0.20, and hadoop-core:1.2.0.jar
Is there any possibility to do this, using new hadoop API?
The number of output files equals to the number of reducers or the number of the mappers if there aren't any reducers.
You can add a single reducer to your job so that the output from all the mappers will be directed to it and your get a single output file. Note that will be less efficient as all the data (output of mappers) will be sent over the wire (network IO) to the node where the reducer will run. Also since a single process will (eventually) get all the data it would probably run slower.
By the wat,the fact that there are multiple parts shouldn't be very significant as you can pass the directory containing them to subsequent jobs
Im not sure you can do it (your link is about multiple outputs not converging to only one), and why use only one output ? you will lose all parallelism on sort ?
Im also working on big files (~10GB each) and my MR process almost 100GB each. So to lower Map numbers, I set a higher value of block size in hdfs (applies only to newer files) and a higher value of mapred.min.split.size in mapred-site.xml
You might want to look at MultipleOutputFormat
Part of what Javadoc says:
This abstract class extends the FileOutputFormat, allowing to write
the output data to different output files.
Both Mapper and Reducer can use this.
Check this link for how you can specify a output file name or more from different mappers to output to HDFS.
NOTE: And, moreover, make sure you don't use context.write() so that, 10 files from 10 mapper don't get created. Use only MultipleOutputFormat to output.
If the job has no reducers, partitioners and combiners, each mapper outputs one output file. At some point, you should run some post processing to collect the outputs into large file.
I want to create a file in HDFS that has a bunch of lines, each generated by a different call to map. I don't care about the order of the lines, just that they all get added to the file. How do I accomplish this?
If this is not possible, then is there a standard way to generate unique file names to put each line of output into a separate file?
There is no way to append to an existing file in hadoop at the moment, but that's not what it sounds like you want to do anyway. It sounds like you want to have the output from your Map Reduce job go to a single file, which is quite possible. The number of output files is (less than or) equal to the number of reducers, so if you set your number of reducers to 1, you'll get a single file of output.
Before you go and do that however, think if that's what you really want. You'll be creating a bottle neck in your pipeline where it needs to pass all your data through a single machine for that reduce. Within the HDFS distributed file system, the difference between having one file and having several files is pretty transparent. If you want a single file outside the cluster, you might do better to use getmerge from the file system tools.
Both your map and reduce functions should output the lines. In other words, your reduce function is a pass through function that doesn't do much. Set the number of reducers to 1. The output will be a list of all the lines in one file.