Runs variations of ACHILLES analytics using Scala + Spark
Data was generated using the ETL-CMS repo and then loaded into PostgreSQL using the CommonDataModel repo. High-level instructions are below.
Obtain Raw Data
To get the data:
- Clone the ETL-CMS tool from: https://github.com/OHDSI/ETL-CMS/tree/unm-improvements
- Change to the scripts directory
- Run the
- The data will then be downloaded
- Change to the python_etl directory
- Follow the readme in this direcotry to setup your system.
- Finally, run the
CMS_SynPUF_ETL_CDM_v5.pyscript to convert
This will produce valid .csv files ready for import and usage.
Importing to PostgreSQL
To add the raw data to PostgreSQL
- Clone the CommonDataModel repo from: https://github.com/OHDSI/CommonDataModel/tree/master/PostgreSQL
- Login to PostgreSQL and create an empty schema in the database
- Use the
OMOP CDM ddl - PostgreSQL.sqlto create tables and fields into the schema for the CDM
- Load data into the schema by modifying the
OMOP CDM vocabulary load - PostgreSQL.sqlscript in the VocabImport folder to accept the correct tables.
- Add constraints including primary and foreign keys by running
OMOP CDM constraints - PostgreSQL.sql
- Add a minimum set of indexes to the data by running
OMOP CDM indexes required - PostgreSQL.sql
The database import mail fail due to incorrect data types. For my project I changed the database scheme to allow varchar, however I would highly suggest modifying the data itself to remove the non-numeric charachters as an easier solution.
The author used two separate environments:
- Intel Core i5-3570K processor with 16 GB of memory, a 512 GB SSD, and runs Ubuntu 15.10.
- Ran the ACHILLES benchmark and acted as a "single-node" Spark cluster
Amazon Web Services (AWS) Elastic MapReduce (EMR)
- Four memory-optimized compute nodes (r3.xlarge)
- A fifth node acted as the master node
- 4 vCPUs, 30 GB of memory, and an 80 GB SSD
- EMR version 4.5.0
Both infrastructures used Apache Spark 1.6.1 and Apache Hadoop 2.7.2. The Scala-based Spark application will use OpenJDK 7, SBT 0.13.8, and Scala 2.10.6.
How to Run
First, the data needs to be generated. See the readme in the data directory.
Next, sbt will bring in everything you need, but you need to build the JAR:
Then to run with Spark:
spark-submit --class edu.gatech.cse8803.main.Main cse8803_project-assembly-1.3.jar
- Video: https://www.youtube.com/watch?v=k5bl7VhgEmQ
- Paper: https://github.com/powersj/spark4achilles/blob/master/CSE8803_BDAH_2016.pdf
- Joshua Powers
- CSE8803 Big Data Analytics for Health Care (Spring 2016)
- Georgia Institute of Technology
A huge thank you to the following for their feedback, evaluation, and support:
- Dr. Jimeng Sun
- The SunLab
- Dr. Watler & Marjie Powers
- Olga Martyusheva
- Alex Balderson
Apache 2.0 © 2016 Joshua Powers