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Elasticsearch for a Search API

·173 words·1 min
Jettro Coenradie
Author
Jettro Coenradie
Software architect and search enthusiast. I write about AI, search, cloud, and software development.

This is a hands-on liveProject series published by Manning, where you take on the role of a software engineer at a fictional e-commerce company called Sneakers to the Max. Across four projects you build and refine a search engine using Python and Elasticsearch.

The series covers the full lifecycle of a search implementation:

  1. Create a Product Index — Write a Python import pipeline using Elasticsearch Bulk, set up index aliases for zero-downtime updates, and build a Kibana dashboard to verify data quality.
  2. Optimize Mapping — Analyze and customize Elasticsearch mappings, create custom analyzers, handle typos and incomplete search terms, and add synonym support.
  3. Improve Search Support — Build a FastAPI-based search API with multi-term queries, filters for color and price, and pagination.
  4. Boost Specific Results — Fine-tune result ordering by boosting recent and popular products, demoting out-of-stock items, and tuning relevance using a judgment list.

The series is aimed at intermediate Python developers with basic Elasticsearch knowledge and takes approximately four weeks at 5–7 hours per week.