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feat: update assignment description to increase scope and encourage #9

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147 changes: 58 additions & 89 deletions assignment.md
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# Assignment: Sensor Measurements API

# Assignment: Sensor measurements API
The goal of this assignment is to see how you apply practical software engineering skills in a real-world-ish setting. You'll build a backend for a simplified part of our product, with realistic expectations for quality, clarity, and completeness.

The goal of this assignment is to verify that you can apply software engineering techniques in
practice. To do so, we ask that you build a backend for a hypothetical part of our product.
## Context

## Instructions
Modern greenhouses rely on lots of sensors to monitor both the external weather and the internal environment. These parameters—like temperature, humidity, CO₂ levels, and light—are influenced by both external weather and internal control systems (screens, windows, heating, etc).

Modern greenhouses contain lots of sensors to measure the climate in the greenhouse and the
environment around the plants. The environment consists of parameters such as air temperature,
humidity, CO2 concentration and access to water and nutrients. These parameters are influenced
by conditions outside, as well as control mechanisms inside the greenhouse. To achieve the right
environment, the grower manipulates mechanical components of the greenhouse based on forecasted
external weather conditions. These components include but are not limited to: screens,
ventilation (through opening windows) and heating systems.
We want to give growers insight into how the environment evolves over time, so they can assess and improve their growing strategy. This assignment focuses on the backend logic for a small part of that: ingesting and serving weather (meteo) data.

We want to give growers insights in the current and historical environmental parameters so they
can assess the performance of their growing strategy. I.e. did they manage to keep the temperature,
humidity etc. within the right bounds to create the ideal growing conditions for the plants.
To be able to build a dashboard with these insights, we need a backend that exposes the right
information through one or more API endpoints that can be consumed by the frontend.
## Your Task

For this assignment, you're going to build a part of the backend that deals with meteo (weather)
data.
Implement a backend service with the following functionality:

## Requirements
### 1. API endpoints

Your solution should include the following functionality:

1. An API endpoint that can be used to push raw data (according to the provided format)
from the climate computer to your service (see below for access to data)
2. One or more API endpoints to expose the meteo data in a _sane_ and _convenient_
format for consumption by the frontend. The frontend expects at least the following
functionality:

- Expose the _latest_ weather conditions (i.e. show what's happening _now_)
- Expose the development of the weather parameters over the last 24h in 15 min increments
- Expose the average for each of the weather parameters for the last 24h
- Expose the development of the weather parameters over the last 7 days in 1 day increments
(average per day)
- Expose the average of the weather parameters over the last 7 days

**Some hints and tips:**

- It's up to you how you store the measurements. Can be in-memory, in a database, etc.
- The format of the raw data is _not necessarily_ the ideal format to expose the data in for the
frontend. Consider the design of your API output well!
- Consider **all** aspects of API design, including but not limited to: paths, return codes,
input/output formats etc.

### Bonus objectives

- Store the sensor data in a persistent way (i.e. not in-memory) so it's available between restarts
- Add authentication between the API and allow for different roles between the "write" endpoint
and the "read" endpoints
- Provide a way to ingest raw data in bulk

### Non-functional requirements

- Use Python 3.10 or higher _or_ use Golang 1.19 or higher

> [!IMPORTANT]
> At Source, we're increasingly moving towards Golang as our main language for backend development outside of building
> ML models. For that reason, **we consider it a huge plus if you develop your solution in Golang**.

- Maintainability is favoured over performance. No complex performance optimizations should be
needed. Another developer should be able to continue where you left off
- It should be possible to run the project (and all of its components) on any machine, including
our laptops
- This assignment was designed to be completed in 6-8h. The evaluation will take into account the
choices you make and what you focus on given the time you have. However, it's up to you if you
spend less or more time on it.
- Please make sure the final commit to your repository is done at least 24 hours before the start
of your interview
- Ingest raw meteo data (see "Data" section below).
- Expose weather data to a frontend, with:
- Current weather conditions (latest value per parameter)
- 24h time series view (15 min resolution)
- 24h average per parameter
- 7-day time series view (1-day resolution)
- 7-day average per parameter

## Deliverables
### 2. Data handling

- Transform data into a clean, structured API response — don't just echo the ingestion format.
- Handle duplicates, missing data, malformed data, and other edge cases gracefully.
- Store data persistently (e.g. in a DB) so it's available between restarts.

### 3. Security & robustness

This assignment should be delivered in the following way:
- Add authentication and authorization. At minimum, separate read/write access.
- Provide a way to ingest raw data in bulk.
- Add an appropriate level of test coverage.
- Structure your codebase so that it's easy to navigate and extend.
- Ensure the system runs locally with minimal effort.

- All code is pushed to your private fork of this repository.
- Documentation is provided in the README.md on how the solution works, and how to run and test it.
- Any information, (dummy)-data, files, and other assets that are needed to run this project, are
provided in this repository.
- Please provide a copy of your entire repository to us as a [git bundle](https://stackoverflow.com/a/11795549) in an email attachment. You can create one using `git bundle create repo.bundle --all`.
## Requirements

- Use **Go 1.22+** or **Python 3.12+**
> [!note]
> At Source.ag we're moving toward Go as our main backend language (outside of ML). Using Go is a strong plus.

## Assessment Criteria
- The code should be easy to understand and maintain. Performance is secondary.
- The system should run cleanly on any machine (including ours).
- You're encouraged to use LLM-assisted tools, but **you** own the code. Don't leave behind things you don't understand, and be ready to explain your implementation choices.
- Timebox to ~6-8 hours. You don't need to cover every edge case — just be clear about trade-offs.

## Deliverables

The solution will be assessed on the following criteria:
- Commit your code to a private fork of this repo.
- Provide a `README.md` with:
- Setup & run instructions
- Testing instructions
- Brief explanation of your architecture, assumptions, and trade-offs
- An overview of next steps you would take if you had 3 months instead of 8 hours to work on this
- Send us a [`.git bundle`](https://stackoverflow.com/a/11795549) via email

- How is your code structured? Is it easy to read and follow?
- How clear is the documentation?
- How is the API design? Is it easy for a frontend developer to use?
- What does your (internal) data model look like? How does your solution represent and store data?
- Are there any clear bugs in your code?
- How does the solution perform?
- Can you clearly and concisely describe the process you have followed and the choices you have made?
- Can you describe the biggest short-comings of your solution and which steps could be taken to
improve on that?
## Evaluation Criteria

**NOTE:** your experience as a software engineer influences our expectations on the completeness of
your solution - both in terms of requirements and bonus objectives met. In other words: we expect
different things from someone with 1 year of experience than from someone with 10 years of experience.
We'll assess your work based on:

## Additional information and assumptions
- Architectural choices (did you pick the right tools for the job? Did you solve problems on the infrastructure level where appropriate?)
- Code clarity and project structure (e.g. how easy is it to understand and navigate the codebase? How easy is it to contribute to the codebase?)
- API usability and design (e.g. how easy is it to consume the API? How easy is it to evolve the API in a backwards-compatible way?)
- Data modeling and storage (e.g. how flexible is the data model to new sensor types? How would it perform at scale?)
- Handling of real-world issues (e.g. invalid input, missing or duplicate data)
- Testing strategy (e.g. unit vs. integration tests, test coverage)
- Your ability to explain your choices and identify trade-offs or next steps

- The required raw data to test your solution can be found through this link: https://drive.google.com/drive/folders/1TV20EVuxcmaqro7e0HfmX2j6HtILNwXB?usp=sharing
We adjust expectations based on experience level. A staff/senior engineer is expected to cover more ground than someone early in their career — especially now that LLMs can help you move faster.

## Data

Use the sample data available here:
https://drive.google.com/drive/folders/1TV20EVuxcmaqro7e0HfmX2j6HtILNwXB?usp=sharing