interview project

This project uses Quarkus, the Supersonic Subatomic Java Framework.

If you want to learn more about Quarkus, please visit its website: https://quarkus.io/ .

Running the application in dev mode

You can run your application in dev mode that enables live coding using:

./mvnw compile quarkus:dev

NOTE: Quarkus now ships with a Dev UI, which is available in dev mode only at http://localhost:8080/q/dev/.

Packaging and running the application

The application can be packaged using:

./mvnw package

It produces the quarkus-run.jar file in the target/quarkus-app/ directory. Be aware that it’s not an über-jar as the dependencies are copied into the target/quarkus-app/lib/ directory.

If you want to build an über-jar, execute the following command:

./mvnw package -Dquarkus.package.type=uber-jar

The application is now runnable using java -jar target/quarkus-app/quarkus-run.jar.

Creating a native executable

You can create a native executable using:

./mvnw package -Pnative

Or, if you don't have GraalVM installed, you can run the native executable build in a container using:

./mvnw package -Pnative -Dquarkus.native.container-build=true

You can then execute your native executable with: ./target/interview-1.0.0-SNAPSHOT-runner

If you want to learn more about building native executables, please consult https://quarkus.io/guides/maven-tooling.html.

Related guides

  • RESTEasy JAX-RS (guide): REST endpoint framework implementing JAX-RS and more

Provided examples

RESTEasy JAX-RS example

REST is easy peasy with this Hello World RESTEasy resource.

Related guide section...

RESTEasy JSON serialisation using Jackson

This example demonstrate RESTEasy JSON serialisation by letting you list, add and remove quark types from a list. Quarked!

Related guide section...

What:

Grofers Recruitment Scheduling

Why:

- Maximise number of candidates going through hiring pipeline
- Minimize Interviewer uncertainty
- Minimize Time Candidates spend in hiring pipeline
- Balance out Interviewer team disruptions

How:

- Model the problem as a convex optimization problem
- Write a solver
- What is the model:
	- X candidates:
		- each candidate has a set of skills which we need to interview them for 
		(eg. SDE 1 - problem solving, SDE 2 - Design, Bar Raiser)
		- each candidate can specify their availability 
		(eg. I can take the interviews on monday 5pm, tuesday 5pm or saturday 10am)

	- Y Interviwers:
		- each interviwer has a set of skills they can take interviews for
		- each interviewer can specify their preferred slots for taking interviews
		- each interviwer belongs to a sub team in grofers

	- Match X,Y to make sure candidates and interviewers match on skillset and slots.
	- Ensure Interviwers are spread across so that a single team is not burdened and everyone has a fair share of the interviews.

Technology Exploration:

- OptaPlanner
	- open source solver modelling framework
		- comes with capabilities to model business contraints using rules
		- comes with capabilities to run continuous solver
		- good documentation on lot of different optimization problems

- Quarkus
	- Lightweight runtime, cloud native deployments and build iterations are rapid
	- Has very good patterns for modelling microservices in java rapidly

- React Admin
	- needed to push out the ui for this in 1 hour

Results:

- Solves problem with x candidates, y interviews and a z timeslots to an acceptable level in x seconds
- Adjusts plan if something changes
	- eg. candidate / interviewer changes slot preference
	- eg. skill set requirement changes.

What's Next:

- Allow the Interview Requirement to be broken into multiple pieces (Round 1, Round 2, Round 3) with dependency chain. ( model it like an assembly line )
- Build the basic functionalities into the ui
	- better forms
	- access control
	- reports
- Execute schedule by sending out appropriate calendar invites , slack notifications
- Integrate into workflow to automate feedback collection / associated practice questions

Future desires:
- model stochastic processes (i.e: skeptical candidates dont get planned for 3rd rounds automatically etc.)
- Can be extended for non recruitment use cases:
	- redistribution of manpower in stores to optimize store maintainance activities ( put away, picking, billing)
	- redistribution of manpower across stores to solve for service requirements(x number of fnv orders in location y, incentivize movement manpower from z to y)
	- technology roadmap -> execution planning and redistribution