In Part 1, you'll learn how to program the server code of the search application. We'll start by developing a basic Django app using a Postgres database in Docker. We'll create the basic data model, code the API, and write tests.

Learning Objectives

1. Describe a high-level system design to a friend or colleague
2. Create a basic Django app that uses a RESTful API
3. Containerize an app using Docker
4. Understand basic Django REST Framework concepts (serializers, filters, views, etc.)
5. Paginate an API
6. Practice Test-driven Development (TDD)

In Part 2, you'll explore Postgres queries while incorporating them into the search application. We'll go step-by-step in transforming the foundational app we created in Part 1 into a robust search service.

We'll start by learning the fundamentals of full-text search, and then we'll slowly build the concepts into our API and demonstrate how they improve the search results. You'll learn how to use database indexes to boost performance, rank results by relevancy, highlight keywords, and offer suggestions for misspelled words. By the end of Part 2, you'll have a good handle on how to write raw SQL queries and the companion Django code.

Learning Objectives

1. List some ways that full-text search matches a user's query better than simple alternatives
2. Implement full-text search in a Django app using a Postgres database
3. Sort full-text search results by relevancy
4. Update a data model to improve full-text search performance
5. Highlight full-text search matches
6. Make suggestions to a user who searches for a misspelled word

In Part 3, you'll code a React app that provides a user interface for the APIs you built in Parts 1 and 2. The UI is broken into two components: a search form and a results list. Along the way, we'll write end-to-end tests using Cypress.

Learning Objectives

1. Create a React app using Create React App
2. Build functional forms with Formik
3. Communicate with the server through HTTP requests
4. Code a React component to fetch and display paginated results
5. Test an application end-to-end using Cypress
6. Build a production version of a React app

In Part 4, you'll learn how to use Elasticsearch in place of Postgres to provide comparable functionality. We'll explore how to replicate the same search techniques, such as relevancy boosting and suggestions, using Elasticsearch's concepts and Query DSL.

We'll start by configuring an Elasticsearch cluster with a single node in Docker. Next, we'll build an index and walk through how to add features and what affects they have on the search results, just like we did in Part 2 for Postgres. We'll cap things off by using Nginx to serve traffic between the frontend and the backend.

Learning Objectives

1. Configure Elasticsearch for local development
2. Create an Elasticsearch index and populate it with documents
3. Perform searches using Query DSL
4. Highlight search matches
5. Make suggestions to a user who searches for a misspelled word