amz_author_all_books

This is an Amazon Author All Books table. It has the following fields: - id: a unique identifier for each row in the table, starting from 1 for each book in alphabetical order of the author’s surname - order: the page number where this book appears (1 being at the top) - created_at: the date and time at which this book was added to the table. This field is stored as an ISO-8601 datetime string using UTC timezone convention, meaning the time in each country’s local timezone will differ from the time in UTC. - author_id: a unique identifier for the author of the book, also starting at 1 and increasing alphabetically by the author’s surname - book_id: a unique identifier for the particular book that is being described - media_url_id: a unique identifier for the Media URL associated with this book

The table stores information about all books written by any of the authors in Amazon Author All Books. It contains metadata such as the author name, page number, creation date and ISBN number for each book. The Media URL associated with each book is used to view the cover image on a Kindle device.

Consider the following scenario: As a Systems Engineer, you are tasked with optimizing the Amazon Author All Books table query in MongoDB based on the following rules: 1) You have the ability to split large tables into smaller ones. 2) Each sub-table can be divided horizontally either by author or media URL id fields. 3) Your task is to divide these tables in such a way that it gives more efficient queries, and thus saves storage space too. 4) After division, every book must have an equivalent index on the same set of attributes (e.g., If we split it into two on author ID, each table should still contain an “author_id” field).

Question: What strategy would you adopt to divide these tables in order to optimize storage space and query speed? And which sub-tables would you create if applicable?

Apply the property of transitivity. If we assume dividing by media URL and by author ID have equal implications on overall performance, we should try to minimize the number of these divisions.

Use a direct proof method: Consider two possible strategies. The first is to only divide horizontally into one table - an “author” based or a “media_url” based division.

Inductively expand upon this strategy considering its limitations, e.g., when dealing with more than 10,000 books per artist, the index queries could take a high amount of time due to overlapping searches, whereas media URL-based division keeps data separated into several tables causing redundancy in storage for these attributes.

Implementing proof by contradiction - assume that horizontal split based only on Media URLs leads to better performance than splitting it horizontally based on authors’ names.

Using the ‘Tree of thought reasoning’, visualize and analyze the impact of each division strategy on query time, index size, storage, and data redundancy for different sizes of books (e.g., 500 words or more) based on both strategies.

Finally, after having analyzed all these possibilities using deductive logic, we can see that the most effective solution is a combination of splitting the table horizontally into author-based divisions as it addresses issues related to query efficiency and data redundancy efficiently. Answer: The strategy will be to divide a single base table into separate “author” or “media_url” based sub-tables, with an equal distribution among both strategies to minimize potential performance losses from the split.