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Manipulating FHIR data leveraging MongoDB as storage layer

Project description

fhirstore

Python library to manipulate fhir resources leveraging mongoDB as storage layer.

Installation

pip install fhirstore

Usage

from pymongo import MongoClient
from fhirstore import FHIRStore

client = MongoClient()
store = FHIRStore(client, "<my_database>")

# Dropping collections
store.reset()

# Parse json schema and create collections
store.bootstrap(depth=5)
# OR
# Get existing collections from the database
store.resume(depth=5)

# Create resources
store.create({
    "resourceType": "Patient",
    "id": "pat1",
    "gender": "male"
})

# Read resources
patient = store.read("Patient", "pat1")

# Update resources
updated_patient = store.update("Patient", "pat1", {"gender": "other"})

# Delete resources
deleted_patients_count = store.delete("Patient", "pat1")
deleted_patients_count = store.delete("Patient", resource_id="pyrogResouceId")

Bootstrap the database

  1. Start the database
  2. Drop and re-create all collections based on the provided schema
docker-compose up -d
python main.py

Development setup

  1. Create a virtual environment and enter it
  2. Install python dependncies
virtualenv .
. ./bin/activate
pip install -r requirements.txt
pip install -r requirements-dev.txt

Test

Test can be run using :

python -m pytest

Make sure dev dependencies are installed.

Benchmark

A benchmark based on example FHIR resources publicly available at can be run using :

python benchmark/benchmark.py

Note that you will need to have a MongoDB up in order for the benchmark to run. You can use the docker-compose file of this repository by running docker-compose up before launching the benchmark.

On a machine with 16GB RAM and an i7 (2.5GHz) processor, the results of the benchamrk were:

--- WRITES ---
insertions per second (on average): 267.17
average: 3.74 milliseconds
median: 1.66 milliseconds
min: 1.07 milliseconds
max: 724.65 milliseconds
spread: 0.00028004697751347234

--- READS ---
reads per second (on average): 378.93
average: 2.63 milliseconds
median: 1.50 milliseconds
min: 0.88 milliseconds
max: 481.18 milliseconds
spread: 0.0002154728657872756

On the same machine, here are the results for the same benchmark using a different library, fhirbase. The latter relies on postgreSQL rather than MongoDB as its storage layer. Also, it only supports FHIR resources 3.3.0 and below.

--- WRITES ---
insertions per second (on average): 152.38
average: 6.56 milliseconds
median: 4.43 milliseconds
min: 3.08 milliseconds
max: 158.88 milliseconds
spread: 7.568139030481407e-05

--- READS ---
reads per second (on average): 261.07
average: 3.83 milliseconds
median: 3.03 milliseconds
min: 2.17 milliseconds
max: 102.06 milliseconds
spread: 1.4565324857334904e-05

Publish

First, you need to have twine installedd

pip install --user --upgrade twine

Make sure you have bumped the version number in setup.py, then run the following:

python setup.py sdist bdist_wheel
python -m twine upload dist/*

Project details


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