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eQTL Summary Statistics Service

Overview

This project provides an ETL (Extract, Transform, Load) pipeline built using Apache Spark, which processes data files, extracts information, and loads it into a MongoDB database. The pipeline runs in a Dockerized environment, utilizing multiple services including MongoDB and Spark.

Project Structure

.
├── spark
│   ├── __init__.py
│   ├── Dockerfile
│   ├── log4j.properties
│   ├── spark_app.py
│── utils
│   ├── __init__.py
│   ├── constants.py
│   ├── requirements.txt
│   └── utils.py
├── .gitignore
├── docker-compose.yml
├── format-lint
├── pytype.cfg
├── README.md
└── requirements.dev.txt

Key Files and Directories

  • spark/: Contains the main Spark application and supporting files.
    • spark_app.py: The main script that runs the ETL process.
    • Dockerfile: Defines the Docker image for the Spark application.
    • log4j.properties: Configuration file for logging in Spark.
  • utils/: Utility functions and constants used in the ETL process.
  • docker-compose.yml: Orchestrates the Docker containers for MongoDB, Spark Master, Spark Worker, and the Spark application.

Getting Started

Prerequisites

  • Docker
  • Docker Compose
  • Python 3.8 or higher
  • Java 8 or higher (for Apache Spark)

Setup

  1. Clone this repository:

    git clone https://github.com/EBISPOT/eqtl-sumstats-service.git
cd eqtl-sumstats-service

  2. Build and start the Docker containers:

    docker-compose build
docker-compose up

    This will pull the necessary Docker images, build the custom Spark application image, and start the services (MongoDB, Spark Master, Spark Worker, Spark Application).

Running the ETL Pipeline

The ETL pipeline is automatically triggered when the Spark application container starts. The spark_app.py script performs the following tasks:

  1. Download: Fetches data files from a remote FTP server.
  2. Process: Parses and transforms the data using Spark.
  3. Load: Writes the processed data into a MongoDB collection.

Configuration

  • FTP Configuration: FTP connection details are defined in the constants.py file.
  • MongoDB Configuration: MongoDB connection URI and database details are also specified in constants.py.
  • Spark Configuration: Custom configurations for the Spark session, including MongoDB integration, are set in spark_app.py.

Development

It might be a good idea to limit dataframes to 10 rows in spark. Otherwise it might be a problem in your local development. One can search for DEV for such points.

Linting and Formatting

Create a virtual env for format & lint in which you can install the required Python packages using:

pip install -r requirements.dev.txt

One can run the script as follows:

./format-lint