assaflavi / infra-eng-assignment

Code assignment: document search

This assignment aims to explore multiple aspects of taking a simple Python web application and making it production-grade.

Background

The doc-search application implements a simple search endpoint over a set of documents. More specifically, given a dataset of documents, where each document has a numeric identifier, the endpoint returns a list of all of the document IDs containing ALL words in the q query parameter.

For example, if the web server is serving at http://localhost:8080, and the words hello and world both exist only in document 1, then the command curl http://localhost:8080/?q=hello+world should return:

{
    "results": ["1"]
}

Sanity checks

1. Run unit tests cd doc-search/src/ && python -m unittest -b test_index
2. Build the container image: docker build . -t doc-search
3. Run the app: docker run -p 8080:8080 doc-search.
4. Test the app: you can use curl to query it, for example: curl http://localhost:8080/?q=hello+world will return a JSON document with all of the documents containing both hello and world

Tasks

Part 1: Improving the build

The app currently has a Dockerfile included under doc-search/.

1. Every commit to application code (.py files) results in a slow build of the container image. Modify the Dockerfile to make the build faster.
2. How can you minimize the size of the resulting container image? Modify the Dockerfile or describe your solution.

Part 2: Deploying to Kubernetes

Here you will deploy the application to a local Minikube.

1. Implement a minimal Helm chart for this application.
2. Deploy the chart to Minikube, under the default namespace.
3. Verify that you can call the service from outside the cluster.
4. We want Kubernetes to tolerate a slow start for our app. Implement this behavior in your chart. Bonus points if you can simulate a slow start and test your solution.

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Good luck!