We developed a tool for scalable language processing for our customer Lingit using Amazon’s Elastic Mapreduce.

More details: http://aws.amazon.com/solutions/case-studies/atbrox/

Contact us if you need help with Hadoop/Elastic Mapreduce.

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Sometimes it can be useful to compile Python code for Amazon’s Elastic Mapreduce into C++ and then into a binary. The motivation for that could be to integrate with (existing) C or C++ code, or increase performance for CPU-intensive mapper or reducer methods. Here follows a description how to do that:

1. Start a small EC2 node with AMI similar to the one Elastic Mapreduce is using (Debian Lenny Linux)
• note: We used Alestic‘s ami-ff46a796
2. Skim quickly through the Shedskin tutorial
3. Log into the EC2 node and install the Shedskin Python compiler
• Shedskin requires a few libraries: 1) the Boehm-Demers-Weiser garbage collector for C++, 2) PCRE – Perl Compatible Regular Expressions. The Shedskin tutorial for detailed install instructions.
• note: The Alestic Debian AMI is fairly slim, so we had to add some more software make Shedskin work, i.e. GDB
4. Write your Python mapper or reducer program and compile it into C++ with Shedskin
• E.g. the commandpython ss.py mapper.py – would generate C++ files mapper.hpp and mapper.cpp, a Makefile and an annotated Python file mapper.ss.py.
5. Optionally update the C++ code generated by Shedskin to use other C or C++ libraries
• note: with Fortran-to-C you can probably integrate your Python code with existing Fortran code (e.g. numerical/high performance computing libraries). Similar for Cobol (e.g. in financial industry) with OpenCobol (compiling Cobol into C). Please let us know if you try or need help with help that.
6. Add -static as the first CCFLAGS parameter in the generated Makefile to make it a static executable
7. Compile the C++ code into a binary with make and check that you don’t get a dynamic executable with ldd (you want a static executable)
8. Run strip on the binary to make it smaller
9. Upload your (ready) binary to a chosen location in Amazon S3
   • e.g. via commandline with S3CMD, with a UI using S3Fox or Cloudberry S3 Explorer or programmatically with Boto.
10. Read Elastic Mapreduce Documentation on how to use the binary to run Elastic Mapreduce jobs.
    • note: Peter Skomoroch has written a good tutorial for Elastic Mapreduce

Note: if you skip the shedskin-related steps this approach would also work if you are looking for how to use C or C++ mappers or reducers with Elastic Mapreduce.

Note: this approach should probably work also with Cloudera’s distribution for Hadoop.

Do you need help with Hadoop/Mapreduce?
A good start could be to read this book, or

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We are here to help you:

• Understand if and how the cloud can be cost-efficient in your setting
• Efficiently analyze large data sets using the cloud
• Architect, develop and deploy scalable and reliable software for the cloud
• Adapt and migrate your existing data and software to the cloud

Technologies and methods we (non-exclusively) use:

• Hadoop and Elastic Mapreduce for analyzing large data sets
• Data Mining and Machine Learning
• Our own Technology and Services

Our motto is Simplicity, Automation and Scalability

If you are considering using cloud computing, please drop us a line to info (at) atbrox.com

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