A preconfigured setup of common FHIR Servers is provided in the docker-compose.yml file. The compose file includes a Blaze, HAPI, and Linux4Health FHIR server.
Start the servers in the benchmarks directory (cd benchmarks) with docker compose
docker compose up -dIf not already installed, install the fhir_kindling with the ds extras
pip install fhir-kindling[ds]These servers are configured by default in the benchmark_servers.py script. If you want to add your own server, you can do so by adding it to the servers list in the script.
Run the benchmark script
python benchmark_servers.pyModify the dataset size and the number of attempts made for each step in the benchmark by settings the dataset_size and n_attempts variables on the ServerBenchmark instance. (Keep in mind that the dataset size changes with approximately a factor of 10 based on the parameter given in the dataset_size variable.)
Comparing the library's performance with the other most popular client libraries fhirpy and fhirclient.
Perform the benchmarks by running python benchmarks/benchmark_clients.py
All operations were performed against a Blaze server version 0.17.12 running in a
docker container on a 64-bit Windows 11 machine.
When querying n=10000 patients from the server the following results were observed. FHIR kindling executed the sync
query at least twice as fast as the other client libraries. Comparisons for the async versions will follow.
