Searching nearest orgunits / Location based on the provided coordinates
Performance was measured based on the time taken to:
load 500 nearby outlets to the provided coordinates (for locator API)
URL: https://replica.psi-mis.org/locator/api/1?n=500&c={latitude},{longitude}&d={distance}
Similarly, in FHIR, we tried to load 500 nearby locations based on the coordinates provided
URL: https://fhir-dev.psi-mis.org/fhir?near={latitude}|{longitude}|{distance}|{unit}&_count=500
Summary of test results
DHIS2 | FHIR | DHIS2 vs FHIR % | ||
---|---|---|---|---|
Average | 1.42669 | 0.578819 | 59.4293 |
Test detailed results
logged on Nov 29/ 2022 UTC-7 09:16:00
Performance Testing for | Attempt | DHIS2 API | DHIS2 Size | FHIR API | FHIR Size | Improvement % |
---|---|---|---|---|---|---|
El Salvador | Attempt 1 | 1.37355 | 141 | 0.598687 | 500 | 56.4133 |
Attempt 2 | 1.3631 | 141 | 0.569395 | 500 | 58.228 | |
Attempt 3 | 1.46057 | 141 | 0.595058 | 500 | 59.2586 | |
Kenya | Attempt 1 | 1.43973 | 500 | 0.593388 | 500 | 58.7847 |
Attempt 2 | 1.43513 | 500 | 0.582957 | 500 | 59.3796 | |
Attempt 3 | 1.48656 | 500 | 0.557877 | 500 | 62.4718 | |
Nigeria | Attempt 1 | 1.50456 | 500 | 0.583583 | 500 | 61.2125 |
Attempt 2 | 1.50682 | 500 | 0.566507 | 500 | 62.4039 | |
Attempt 3 | 1.47951 | 500 | 0.572773 | 500 | 61.2864 | |
Cameroon | Attempt 1 | 1.49727 | 500 | 0.579295 | 500 | 61.31 |
Attempt 2 | 1.57967 | 500 | 0.584989 | 500 | 62.9676 | |
Attempt 3 | 1.47999 | 500 | 0.569686 | 500 | 61.5074 | |
Nepal | Attempt 1 | 1.57151 | 12 | 0.587268 | 500 | 62.6305 |
Attempt 2 | 1.11562 | 12 | 0.565472 | 500 | 49.3134 | |
Attempt 3 | 1.10675 | 12 | 0.575349 | 500 | 48.0143 | |
Overall Average | 1.42669 | 0.578819 | 59.4293 |
Testing Script
case_results = [{"description": "Performance Testing for", "attempt": "Attempt", "dhis2": "DHIS2 API", "dhis2_size": "DHIS2 Size", "fhir": "FHIR API", "fhir_size": "FHIR Size", "improvement": "Improvement %"}] print("Case #2 - Getting 500 orgUnits/Location 100km around the provided coordinates") improvements = [] dhis2_performances = [] fhir_performances = [] for country in countries: for i in range(1,4): case_result = {} if i == 1: case_result['description'] = "{}".format(country['name']) case_result['attempt'] = "Attempt {}".format(i) dhis2_url = dhis2_base_url+'n=500&c={},{}&d=1000000'.format(country['latitude'], country['longitude']) result = requests.get(dhis2_url, auth=dhis2_auth) if result.status_code == 200: data = result.json() dhis2_performances.append(result.elapsed.total_seconds()) case_result['dhis2'] = dhis2_performances[-1] case_result['dhis2_size'] = len(data['outlet']) request_url = fhir_location_url+'near={}|{}|10000|km&_count=500'.format(country['latitude'], country['longitude']) fhirResult = requests.get(request_url, auth=fhir_auth) if fhirResult.status_code == 200: fhir_data = fhirResult.json() fhir_performances.append(fhirResult.elapsed.total_seconds()) case_result['fhir'] = fhir_performances[-1] case_result['fhir_size'] = len(fhir_data['entry']) if 'entry' in fhir_data else 0 improvements.append(((case_result['dhis2'] - case_result['fhir'])/case_result['dhis2'])*100) case_result['improvement'] = improvements[-1] case_results.append(case_result) time.sleep(0.01) case_result = {} case_result['description'] = "Overall Average" case_result['dhis2'] = np.average(dhis2_performances) case_result['fhir'] = np.average(fhir_performances) case_result['improvement'] = ((case_result['dhis2'] - case_result['fhir'])/case_result['dhis2'])*100 case_results.append(case_result) print(tabulate(case_results, headers='firstrow', tablefmt='pipe'))