This project brings in-DBMS data analytics to Impala. This leverages previous work done by two projects:

• MADlib (http://madlib.net/)
• Bismarck (http://hazy.cs.wisc.edu/hazy/victor/bismarck/)

Each of these projects use User Defined Aggregates (UDAs) to train analytic models using an existing DBMS's data management and processing ability.

Dependencies: yum install -y eigen3-devel.noarch

Also, install boost 1.54.0

This code base includes the following components.

There is a fork of MADlib 1.0 which has been modified for use with impala. The specific changes were:

• madlib/test with tests for the new code
• madlib/Makefile to make the tests
• madlib/src/ports/metaport which is a modified MADlib backend for main memory

To run the example SVM,

1. Create database toysvm
2. to register the UDFs with a database (without re-making the binaries), execute: python python/deploy.py -mp toysvm
3. create a synthetic table of examples in the database toysvm with the table toy: python python/gen_classify_data.py toysvm toy
4. python python/impala_svm.py lbl e0 e1 e2 --db toysvm --table toy -e 1
5. impala-shell -q 'use toysvm; select iter, printarray(model) from history;'