Introduction
🪄 Dive into the world of Student CRUD Apps and see how seamlessly Keploy integrated with Flask and MongoDB. Buckle up, it's gonna be a fun ride! 🎢
🛠️ Platform-Specific Requirements for Keploy
Below is a table summarizing the tools needed for both native and Docker installations of Keploy on MacOS, Windows, and Linux:
Operating System | Without Docker | Docker Installation | Prerequisites |
---|---|---|---|
![]() | Docker Desktop version must be 4.25.2 or above | ||
- Use WSL wsl --install - Windows 10 version 2004 and higher (Build 19041 and higher) or Windows 11 | |||
![]() | Linux kernel 5.15 or higher |
On MacOS and Windows, additional tools are required for Keploy due to the lack of native eBPF support.
Quick Installation
Let's get started by setting up the Keploy alias with this command:
curl --silent -O -L https://keploy.io/install.sh && source install.sh
You should see something like this:
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▓█████████▌▓▓ ██▓█▄ ▓█▄▓▓ ▐█▌ ██ ▓█ █▌ ██ █▌ █▓
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Keploy CLI
Available Commands:
example Example to record and test via keploy
generate-config generate the keploy configuration file
record record the keploy testcases from the API calls
test run the recorded testcases and execute assertions
update Update Keploy
Flags:
--debug Run in debug mode
-h, --help help for keploy
-v, --version version for keploy
Use "keploy [command] --help" for more information about a command.
🎉 Wohoo! You are all set to use Keploy.
Get Started! 🎬
Clone a simple Student Management API 🧪
git clone https://github.com/keploy/samples-python.git && cd samples-python/flask-mongo
Installation Keploy
Depending on your OS, choose your adventure:
There are 2 ways you can run this sample application.
- Using Docker compose : running application as well as Mongo on Docker container
- Using Docker container for Mongo and running application locally
Using Docker Compose 🐳
We will be using Docker compose to run the application as well as Mongo on Docker container.
Lights, Camera, Record! 🎥
Capture the test-cases-
keploy record -c "docker compose up" --containerName "flask-app" --buildDelay 50
🔥Make some API calls. Postman, Hoppscotch or even curl - take your pick!
Let's make URLs short and sweet:
Generate testcases
To generate testcases we just need to make some API calls.
- Make a POST request:
curl -X POST -H "Content-Type: application/json" -d '{"title":"Task 1","description":"Important task"}' http://localhost:5000/api/tasks
- Make a GET request:
curl http://localhost:5000/api/tasks
- Make a PUT request:
curl -X PUT -H "Content-Type: application/json" -d '{"title":"Task 1","description":"Random task"}' http://localhost:5000/api/tasks/12345
- Make a DELETE request:
curl -X DELETE http://localhost:5000/api/tasks/12345
And once you are done, you can stop the recording and give yourself a pat on the back! With that simple spell, you've conjured up a test case with a mock! Explore the keploy directory and you'll discover your handiwork in tests
directory and mocks.yml
.
version: api.keploy.io/v1beta1
kind: Http
name: test-1
spec:
metadata: {}
req:
method: GET
proto_major: 1
proto_minor: 1
url: http://localhost:5000/api/tasks
header:
Accept: "*/*"
Accept-Encoding: gzip, deflate, br
Cache-Control: no-cache
Connection: keep-alive
Content-Length: "59"
Content-Type: application/json
Host: localhost:5000
Postman-Token: 10512b5c-4da7-4ef3-b145-101cdd1357f1
User-Agent: PostmanRuntime/7.32.1
body: '{"title": "Task 6","description": "Description for Task 6"}'
timestamp: 2024-04-22T16:38:39.232565209+05:30
resp:
status_code: 200
header:
Access-Control-Allow-Origin: "*"
Content-Length: "267"
Content-Type: application/json
Date: Mon, 22 Apr 2024 11:08:39 GMT
Server: Werkzeug/3.0.2 Python/3.10.12
body: |
{
"tasks": [
{
"description": "should update",
"id": "6626362fc7c5eddf174c88e4",
"title": "Updated"
},
{
"description": "Should work",
"id": "66263667c7c5eddf174c88e5",
"title": "Let's Check another time"
}
]
}
status_message: OK
proto_major: 0
proto_minor: 0
timestamp: 2024-04-22T16:38:41.245704918+05:30
objects: []
assertions:
noise:
header.Date: []
created: 1713784121
curl: |-
curl --request GET \
--url http://localhost:5000/api/tasks \
--header 'Host: localhost:5000' \
--header 'User-Agent: PostmanRuntime/7.32.1' \
--header 'Accept: */*' \
--header 'Content-Type: application/json' \
--header 'Connection: keep-alive' \
--header 'Cache-Control: no-cache' \
--header 'Postman-Token: 10512b5c-4da7-4ef3-b145-101cdd1357f1' \
--header 'Accept-Encoding: gzip, deflate, br' \
--data '{"title": "Task 6","description": "Description for Task 6"}'
This is how the mocks.yml
looks like:
version: api.keploy.io/v1beta1
kind: Mongo
name: mock-0
spec:
metadata:
operation: '{ OpQuery flags: [], fullCollectionName: admin.$cmd, numberToSkip: 0, numberToReturn: -1, query: {"ismaster": {"$numberInt":"1"},"helloOk": true,"client": {"driver": {"name": "PyMongo","version": "4.6.3"},"os": {"type": "Linux","name": "Linux","architecture": "x86_64","version": "5.15.146.1-microsoft-standard-WSL2"},"platform": "CPython 3.10.12.final.0"}}, returnFieldsSelector: }'
type: config
requests:
- header:
length: 283
requestId: 1804289383
responseTo: 0
Opcode: 2004
message:
flags: 0
collection_name: admin.$cmd
number_to_skip: 0
number_to_return: -1
query: '{"ismaster":{"$numberInt":"1"},"helloOk":true,"client":{"driver":{"name":"PyMongo","version":"4.6.3"},"os":{"type":"Linux","name":"Linux","architecture":"x86_64","version":"5.15.146.1-microsoft-standard-WSL2"},"platform":"CPython 3.10.12.final.0"}}'
return_fields_selector: ""
responses:
- header:
length: 329
requestId: 238
responseTo: 1804289383
Opcode: 1
message:
response_flags: 8
cursor_id: 0
starting_from: 0
number_returned: 1
documents:
- '{"helloOk":true,"ismaster":true,"topologyVersion":{"processId":{"$oid":"6626352423399d438e00b0cf"},"counter":{"$numberLong":"0"}},"maxBsonObjectSize":{"$numberInt":"16777216"},"maxMessageSizeBytes":{"$numberInt":"48000000"},"maxWriteBatchSize":{"$numberInt":"100000"},"localTime":{"$date":{"$numberLong":"1713784113763"}},"logicalSessionTimeoutMinutes":{"$numberInt":"30"},"connectionId":{"$numberInt":"18"},"minWireVersion":{"$numberInt":"0"},"maxWireVersion":{"$numberInt":"21"},"readOnly":false,"ok":{"$numberDouble":"1.0"}}'
read_delay: 1010011
created: 1713784113
reqTimestampMock: 2024-04-22T16:38:33.762559618+05:30
resTimestampMock: 2024-04-22T16:38:33.763749062+05:30
Want to see if everything works as expected?
Run Tests
Time to put things to the test 🧪
keploy test -c "docker compose up" --containerName "flask-app" --buildDelay 50 --delay 10
The
--delay
flag? Oh, that's just giving your app a little breather (in seconds) before the test cases come knocking.
Final thoughts? Dive deeper! Try different API calls, tweak the DB response in the mocks.yml
, or fiddle with the request or response in test-x.yml
. Run the tests again and see the magic unfold!✨👩💻👨💻✨
Wrapping it up 🎉
Congrats on the journey so far! You've seen Keploy's power, flexed your coding muscles, and had a bit of fun too! Now, go out there and keep exploring, innovating, and creating! Remember, with the right tools and a sprinkle of fun, anything's possible. 😊🚀
Happy coding! ✨👩💻👨💻✨
Running App Locally on Linux/WSL 🐧
We'll be running our sample application right on Linux, but just to make things a tad more thrilling, we'll have the database (MongoDB) chill on Docker. Ready? Let's get the party started!🎉
Install all dependencies
pip install -r requirements.txt
Start the MongoDB server
sudo service mongod start
Lights, Camera, Record! 🎥
To initiate the recording of API calls, execute this command in your terminal:
keploy record -c "python3 app.py"
Now, your app will start running, and you have to make some API calls to generate the test cases!!
- Make a POST request:
curl -X POST -H "Content-Type: application/json" -d '{"title":"Task 1","description":"Important task"}' http://localhost:5000/api/tasks
- Make a GET request:
curl http://localhost:5000/api/tasks
- Make a PUT request:
curl -X PUT -H "Content-Type: application/json" -d '{"title":"Task 1","description":"Random task"}' http://localhost:5000/api/tasks/12345
- Make a DELETE request:
curl -X DELETE http://localhost:5000/api/tasks/12345
And once you are done, you can stop the recording and give yourself a pat on the back! With that simple spell, you've conjured up a test case with a mock! Explore the keploy directory and you'll discover your handiwork in tests
directory and mocks.yml
.
version: api.keploy.io/v1beta1
kind: Http
name: test-1
spec:
metadata: {}
req:
method: GET
proto_major: 1
proto_minor: 1
url: http://localhost:5000/api/tasks
header:
Accept: "*/*"
Accept-Encoding: gzip, deflate, br
Cache-Control: no-cache
Connection: keep-alive
Content-Length: "59"
Content-Type: application/json
Host: localhost:5000
Postman-Token: 10512b5c-4da7-4ef3-b145-101cdd1357f1
User-Agent: PostmanRuntime/7.32.1
body: '{"title": "Task 6","description": "Description for Task 6"}'
timestamp: 2024-04-22T16:38:39.232565209+05:30
resp:
status_code: 200
header:
Access-Control-Allow-Origin: "*"
Content-Length: "267"
Content-Type: application/json
Date: Mon, 22 Apr 2024 11:08:39 GMT
Server: Werkzeug/3.0.2 Python/3.10.12
body: |
{
"tasks": [
{
"description": "should update",
"id": "6626362fc7c5eddf174c88e4",
"title": "Updated"
},
{
"description": "Should work",
"id": "66263667c7c5eddf174c88e5",
"title": "Let's Check another time"
}
]
}
status_message: OK
proto_major: 0
proto_minor: 0
timestamp: 2024-04-22T16:38:41.245704918+05:30
objects: []
assertions:
noise:
header.Date: []
created: 1713784121
curl: |-
curl --request GET \
--url http://localhost:5000/api/tasks \
--header 'Host: localhost:5000' \
--header 'User-Agent: PostmanRuntime/7.32.1' \
--header 'Accept: */*' \
--header 'Content-Type: application/json' \
--header 'Connection: keep-alive' \
--header 'Cache-Control: no-cache' \
--header 'Postman-Token: 10512b5c-4da7-4ef3-b145-101cdd1357f1' \
--header 'Accept-Encoding: gzip, deflate, br' \
--data '{"title": "Task 6","description": "Description for Task 6"}'
This is how the mocks.yml
looks like:
version: api.keploy.io/v1beta1
kind: Mongo
name: mock-0
spec:
metadata:
operation: '{ OpQuery flags: [], fullCollectionName: admin.$cmd, numberToSkip: 0, numberToReturn: -1, query: {"ismaster": {"$numberInt":"1"},"helloOk": true,"client": {"driver": {"name": "PyMongo","version": "4.6.3"},"os": {"type": "Linux","name": "Linux","architecture": "x86_64","version": "5.15.146.1-microsoft-standard-WSL2"},"platform": "CPython 3.10.12.final.0"}}, returnFieldsSelector: }'
type: config
requests:
- header:
length: 283
requestId: 1804289383
responseTo: 0
Opcode: 2004
message:
flags: 0
collection_name: admin.$cmd
number_to_skip: 0
number_to_return: -1
query: '{"ismaster":{"$numberInt":"1"},"helloOk":true,"client":{"driver":{"name":"PyMongo","version":"4.6.3"},"os":{"type":"Linux","name":"Linux","architecture":"x86_64","version":"5.15.146.1-microsoft-standard-WSL2"},"platform":"CPython 3.10.12.final.0"}}'
return_fields_selector: ""
responses:
- header:
length: 329
requestId: 238
responseTo: 1804289383
Opcode: 1
message:
response_flags: 8
cursor_id: 0
starting_from: 0
number_returned: 1
documents:
- '{"helloOk":true,"ismaster":true,"topologyVersion":{"processId":{"$oid":"6626352423399d438e00b0cf"},"counter":{"$numberLong":"0"}},"maxBsonObjectSize":{"$numberInt":"16777216"},"maxMessageSizeBytes":{"$numberInt":"48000000"},"maxWriteBatchSize":{"$numberInt":"100000"},"localTime":{"$date":{"$numberLong":"1713784113763"}},"logicalSessionTimeoutMinutes":{"$numberInt":"30"},"connectionId":{"$numberInt":"18"},"minWireVersion":{"$numberInt":"0"},"maxWireVersion":{"$numberInt":"21"},"readOnly":false,"ok":{"$numberDouble":"1.0"}}'
read_delay: 1010011
created: 1713784113
reqTimestampMock: 2024-04-22T16:38:33.762559618+05:30
resTimestampMock: 2024-04-22T16:38:33.763749062+05:30
Run the tests
Now, it's time to put things to the test 🧪
keploy test -c "python3 app.py" --delay 10
Now, you can also try different API calls, tweak the DB response in the mocks.yml, or fiddle with the request or response in test-x.yml. Run the tests again and see the magic unfold!
Check Test Coverage
We have a test-app.py
where all the unit test cases has been written. Now using Keploy, we can check it's code coverage!!
Now to run your unit tests with Keploy, you can run the command given below:
python3 -m coverage run -p --data-file=.coverage.unit -m pytest -s test_keploy.py test_app.py
To combine the coverage from the unit tests, and Keploy's API tests we can use the command below:
python3 -m coverage combine
Finally, to generate the coverage report for the test run, you can run:
python3 -m coverage report
and if you want the coverage in an html file, you can run:
python3 -m coverage html
Wrapping it up 🎉
Congrats on the journey so far! You've seen Keploy's power, flexed your coding muscles, and had a bit of fun too! Now, go out there and keep exploring, innovating, and creating! Remember, with the right tools and a sprinkle of fun, anything's possible.😊🚀
Hope this helps you out, if you still have any questions, reach out to us .
Contact Us
If you have any questions or need help, please feel free to reach out to us at hello@keploy.io or reach out us on
or open a discussion on