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A database benchmarking suite for survival analysis data developed as a part of my masters project at SJSU. The suite is developed in Python and supports 6 queries in general for read performance.
patelaarsh/Survival-Analysis-Data-Benchmarking-Suite
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To install the required python packages,you can use pip: pip install time pip install pymongo pip install lifelines Install/Setup Databases : User Docker Compose. Install Docker Modify docker-compose.yml if needed (add more databases or edit configurations) docker-compose up -d timescaledb Generate Data: python generate_data.py --n 10000000 --database timescaledb (For CSV format for TimescaleDB, QuestDB) Convert to JSON using mongodb_json script for MongoDB Use the cassandra_addColumn script for loading data in Cassandra Load data in MongoDB : python mongo_load.py --database survival_data --collection data --json_file survival_data.json Load Data in TimeScaleDB: Create the Database manually in the command line or using pgAdmin python timescale_load.py --database survival_data --table data --csv_file survival_data.csv --username postgres --password aarsh Load Data in QuestDB: Start Docker container/docker-compose Check port 9000 python questdb_load.py http://localhost:9000 project1 data1.csv Load Data in Cassandra: python cassandra_load.py --keyspace project1 --table data --csv_file cassandra_data1.csv Run Queries: Modify the database and table name as required and directly run the script files. Example: 'python query1_mongodb.py' will connect to MongoDB, perform the query, and return the results. Every script for data generation, data loading, and query generation can be changed accordingly.
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A database benchmarking suite for survival analysis data developed as a part of my masters project at SJSU. The suite is developed in Python and supports 6 queries in general for read performance.
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