Building an inverted index encourages a deeper understanding of the search engines that power services like Google and Elasticsearch. Let's implement our own using Python. The layout is similar to how we implement dictionaries and hash maps in other data structures.

The inverted index we build will be simple but still captures the essential features that make this data structure powerful. We will construct an index where keys are words in a document, and values are sets containing the identifiers of documents containing these words.

Imagine creating a search engine for books. It's similar to creating portfolios of assets in finance. Each word can be viewed as an asset, and each book as a portfolio. We can map each word to a collection of books in which it appears, just like a stock index! Our senior finance-oriented engineers might find this analogy particularly meaningful.

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if __name__ == "__main__": 

  inverted_index = {}

​

  documents = {

    'book1': 'The cat is brown',

    'book2': 'The brown dog jumps',

    'book3': 'The quick brown fox',

    'book4': 'The brown fox jumps over the lazy dog',

  }

​

  for book, contents in documents.items():

    for word in contents.split():

      word = word.lower()

      if word in inverted_index:

        inverted_index[word].add(book)

      else:

        inverted_index[word] = {book}

​

  for word, books in inverted_index.items():

    print(f''{word}' appears in: {books}')