Cherita Flask API

A web service designed to provide a robust and scalable interface for managing and analyzing biological data. This API leverages Flask, a lightweight WSGI web application framework in Python, to deliver high-performance endpoints for data retrieval, processing, and visualization.

Development

Create a development environment with the requirements.txt file

python -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.txt

Set the FLASK_APP environment variable to the cherita directory

export FLASK_APP=cherita

Run the API

python -m flask run

You can enable debugging mode with

python -m flask run --debug

You can set a specific port for the API with the -p flag

python -m flask run -p 8001

Caching

The API is setup to use Redis for caching (supported by Flask-Caching). The app will attempt to connect to a Redis instance if the environment variable REDIS_HOST is set to the instance's IP address. REDIS_PORT and CACHE_KEY_PREFIX can also be set. If REDIS_HOST is set and the app cannot connect to the Redis instance it will throw an "Redis connection error" on each attempt to access the cache.

When updating the API's responses you will have to flush the cache to avoid getting outdated data. You need to connect to the redis instance, we recommend using redis-cli. You can install it with

sudo apt-get install redis-tools

You can send the FLUSHALL command directly like

redis-cli -h instance-ip-address -p port FLUSHALL

Alternatively, you can connect to the instance and then execute the command

redis-cli -h instance-ip-address -p port
FLUSHALL

GCP Memorystore

Note that when using a Memorystore Redis instance you will need to connect from a VM that is within the instance's authorized network. Refer to the official documentation for more information.

Run tests

Install dev packages and run pytest

python -m pip install -r requirements-dev.txt
python -m pytest tests/