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Is there a way to save the instance of a variable during debugging to reuse?

I am debugging something and I end up in a function that takes as parameters a keymap with many key-value pairs.
I would like to be able to reproduce this exact flow but I would be interested in re-using this exactly map (I mean the contents).
The map is populated by other parts of the program and going over the parts or somehow "copy/pasting" the values from the debugger is tedious. I thought there might be a standard solution to this.
Is there a way to somehow save the instance of the keymap and somehow create it in another program? Like serializing in a file I guess and reading in the file

Writting your keymap in a file is a solution. You can use JSON format to easily convert it.

MapReduce: How can a streaming mapper know which file data comes from?

I am learning MapReduce. I'm trying as a test to set up a 'join' algorithm that takes in data from two files (which contain the two data sets to join).
For this to work, the mapper needs to know which file each line is from; this way, it can tag it appropriately, so that the reducer doesn't (for instance) join elements from one data set to other elements from the same set.
To complicate the matter, I am using Hadoop Streaming, and the mapper and reducer are written in Python; I understand Java, but the documentation for the Hadoop InputFormat and RecordReader classes are gloriously vague and I don't understand how I'd make a Streaming-compatible split so that some sort of file identifier could be bundled in along with the data.
Anyone who can explain how to set up this input processing in a way that my Python programs can understand?

I found out the answer, by the way— in Python, it's:
import os
context = os.environ["map_input_file"]
And 'context' then has the input file name.

How to Override InputFormat and OutputFormat In hadoop Application

I have an application which need to read a file which is a serialized result of ArrayList.(ArrayList<String>, 50000 records in this list, size: 20MB)
I don't know exactly how to read the data in to hadoop platform. I only have some sense I need to override InputFormat and OutpurFormat.
I'm a beginner in hadoop platform. Could you give me some advise?
Thanks,
Zheng.

To start with you'll need to extend the FileInputFormat, notable implementing the abstract FileInputFormat.createRecordReader method.
You can look through the source of something like the LineRecordReader (which is what TextInputFormat uses to process text files).
From there you're pretty much on your own (i.e. it depends on how your ArrayList has been serialized). Look through the source for the LineRecordReader and try and relate that to how your ArrayList has been serialized.
Some other points of note, is your file format splittable? I.e. can you seek to an offset in the file and recover the stream from there (Text files can as they just scan forward to the end of the current line and then start from there). If your file format uses compression, you also need to take this into account (you cannot for example randomly seek to a position in a gzip file). By default FileInputFormat.isSplittable will return true, which you may want to initially override to be false. If you do stick with 'unsplittable' then note that your file will be processed by a single mapper (not matter it's size).

Before processing data on Hadoop you should upload data to HDFS or another supported file system of cause if it wasn't upload here by something else. If you are controlling the uploading process you can convert data on the uploading stage to something you can easily process, like:
simple text file (line per array's item)
SequenceFile if array can contain lines with '\n'
This is the simplest solution since you don't have to interfere to Hadoop's internals.

Hadoop MapReduce - one output file for each input

I'm new to Hadoop and I'm trying to figure out how it works. As for an exercise I should implement something similar to the WordCount-Example. The task is to read in several files, do the WordCount and write an output file for each input file.
Hadoop uses a combiner and shuffles the output of the map-part as an input for the reducer, then writes one output file (I guess for each instance that is running). I was wondering if it is possible to write one output file for each input file (so keep the words of inputfile1 and write result to outputfile1 and so on). Is it possible to overwrite the Combiner-Class or is there another solution for this (I'm not sure if this should even be solved in a Hadoop-Task but this is the exercise).
Thanks...

map.input.file environment parameter has the file name which the mapper is processing. Get this value in the mapper and use this as the output key for the mapper and then all the k/v from a single file to go to one reducer.
The code in the mapper. BTW, I am using the old MR API
#Override
public void configure(JobConf conf) {
this.conf = conf;
}
#Override.
public void map(................) throws IOException {
String filename = conf.get("map.input.file");
output.collect(new Text(filename), value);
}
And use MultipleOutputFormat, this allows to write multiple output files for the job. The file names can be derived from the output keys and values.

Hadoop 'chunks' data into blocks of a configured size. Default is 64MB blocks. You may see where this causes issues for your approach; Each mapper may get only a piece of a file. If the file is less than 64MB (or whatever value is configured), then each mapper will get only 1 file.
I've had a very similar constraint; I needed a set of files (output from previous reducer in chain) to be entirely processed by a single mapper. I use the <64MB fact in my solution
The main thrust of my solution is that I set it up to provide the mapper with the file name it needed to process, and internal to the mapper had it load/read the file. This allows a single mapper to process an entire file - It's not distributed processing of the file, but with the constraint of "I don't want individual files distributed" - it works. :)
I had the process that launched my MR write out the file names of the files to process into individual files. Where those files were written was the input directory. As each file is <64MB, then a single mapper will be generated for each file. The map process will be called exactly once (as there is only 1 entry in the file).
I then take the value passed to the mapper and can open the file and do whatever mapping I need to do.
Since hadoop tries to be smart about how it does Map/Reduce processes, it may be required to specify the number of reducers to use so that each mapper goes to a single reducer. This can be set via the mapred.reduce.tasks configuration. I do this via job.setNumReduceTasks("mapred.reduce.tasks",[NUMBER OF FILES HERE]);
My process had some additional requirements/constraints that may have made this specific solution appealing; but for an example of a 1:in to 1:out; I've done it, and the basics are laid out above.
HTH

Add Entire Files Text as Map Key in Hadoop

I'm looking for a way to load an entire file text into my map. Not a single line at a time like TextInputFormat does.
So that when I do value.toString in my map it gives me the entire input to work with.

You have to put every line into a StringBuilder until you've reached the end of file. Or you override your own RecordReader that provides this functionality. But I would not recommend this.

I would path name of the file to the mapper, and then will be free to load is entirely or do some kind of streaming processing.