Case 2
- Utsav Ashish Koju
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 | 1.41292 | 0.583782 | 142.03% |
|
Test detailed results
logged on Dec 1/ 2022 UTC-7 09:04:00
Performance Testing for | Attempt | DHIS2 API | DHIS2 Size | FHIR API | FHIR Size | Improvement % |
|---|---|---|---|---|---|---|
El Salvador | Attempt 1 | 1.41727 | 141 | 0.65739 | 500 | 115.59% |
| Attempt 2 | 1.39867 | 141 | 0.574834 | 500 | 143.32% |
| Attempt 3 | 1.382 | 141 | 0.568845 | 500 | 142.95% |
Kenya | Attempt 1 | 1.43245 | 500 | 0.598346 | 500 | 139.4% |
| Attempt 2 | 1.43526 | 500 | 0.581445 | 500 | 146.84% |
| Attempt 3 | 1.49501 | 500 | 0.569503 | 500 | 162.51% |
Nigeria | Attempt 1 | 1.51636 | 500 | 0.582592 | 500 | 160.28% |
| Attempt 2 | 1.48614 | 500 | 0.613181 | 500 | 142.37% |
| Attempt 3 | 1.63908 | 500 | 0.567279 | 500 | 188.94% |
Cameroon | Attempt 1 | 1.69394 | 500 | 0.591673 | 500 | 186.3% |
| Attempt 2 | 1.47116 | 500 | 0.563599 | 500 | 161.03% |
| Attempt 3 | 1.49798 | 500 | 0.566068 | 500 | 164.63% |
Nepal | Attempt 1 | 1.11843 | 12 | 0.59532 | 500 | 87.87% |
| Attempt 2 | 1.10475 | 12 | 0.567694 | 500 | 94.6% |
| Attempt 3 | 1.10523 | 12 | 0.558964 | 500 | 97.73% |
Overall Average |
| 1.41292 |
| 0.583782 |
| 142.03% |
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'])-1)*100)
case_result['improvement'] = '{}%'.format(float("{:.2f}".format(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'] = '{}%'.format(float("{:.2f}".format(((case_result['dhis2']/case_result['fhir'])-1)*100)))
case_results.append(case_result)
print(tabulate(case_results, headers='firstrow', tablefmt='pipe'))