Optimizing Database Performance: An Indexing Strategy Challenge

You've been tasked with improving the performance of a large e-commerce database. Slow query execution is leading to a poor user experience and impacting sales. Your goal is to design an efficient indexing strategy that significantly speeds up common data retrieval operations. This challenge will test your understanding of how indexes work and how to apply them effectively to optimize query performance.

Problem Description

You are given a simplified schema for an e-commerce platform. This schema consists of two primary tables: Products and Orders. You need to analyze common query patterns and propose an indexing strategy that minimizes query execution time for these patterns.

Tables:

• Products Table:

  • product_id (integer, primary key)
  • name (string)
  • category (string)
  • price (decimal)
  • stock_quantity (integer)

• Orders Table:

  • order_id (integer, primary key)
  • user_id (integer)
  • order_date (date)
  • total_amount (decimal)
  • status (string: "Pending", "Shipped", "Delivered", "Cancelled")

Key Requirements:

1. Analyze Query Patterns: Identify and prioritize common read operations on these tables.
2. Propose Indexes: For each identified query pattern, determine which columns should be indexed and what type of index (e.g., single-column, composite) would be most beneficial.
3. Justify Your Strategy: Explain why your chosen indexes will improve performance for the specified queries, considering factors like query selectivity and data distribution.

Expected Behavior:

Your output should be a clear description of your proposed indexing strategy. This should include:

• A list of specific SQL CREATE INDEX statements (using pseudocode for database system specifics).
• For each index, a clear explanation of which query patterns it supports and why it's effective.

Edge Cases/Considerations:

• Write Performance: While the focus is on read performance, briefly consider how your indexing strategy might impact write operations (insertions, updates, deletions) and any potential trade-offs.
• Index Maintenance: Acknowledge that indexes require storage space and maintenance.

Examples

Example 1: Fetching Products by Category

• Query Pattern: Users frequently browse products by category (e.g., finding all "Electronics" products).
• Hypothetical SQL:

  SELECT product_id, name, price
  FROM Products
  WHERE category = 'Electronics';
  

• Proposed Index (Pseudocode):

  CREATE INDEX idx_products_category ON Products (category);
  

• Explanation: An index on the category column will allow the database to quickly locate all rows matching a specific category without scanning the entire Products table. This is highly effective if there are many distinct categories and queries frequently filter by them.

Example 2: Finding Orders for a Specific User

• Query Pattern: Users often view their order history.
• Hypothetical SQL:

  SELECT order_id, order_date, total_amount, status
  FROM Orders
  WHERE user_id = 12345;
  

• Proposed Index (Pseudocode):

  CREATE INDEX idx_orders_user_id ON Orders (user_id);
  

• Explanation: An index on user_id in the Orders table will enable fast retrieval of all orders associated with a given user_id.

Example 3: Querying Recent Shipped Orders for a Specific User

• Query Pattern: A user wants to see their recently shipped orders.
• Hypothetical SQL:

  SELECT order_id, order_date, total_amount
  FROM Orders
  WHERE user_id = 12345 AND status = 'Shipped' AND order_date >= '2023-10-01';
  

• Proposed Index (Pseudocode):

  CREATE INDEX idx_orders_user_status_date ON Orders (user_id, status, order_date);
  

• Explanation: A composite index on (user_id, status, order_date) is ideal here. The database can efficiently filter by user_id first, then by status, and finally by order_date within the already narrowed down result set. This significantly reduces the number of rows to examine. The order of columns in the composite index is crucial for optimal performance.

Constraints

• The database system is assumed to support standard B-tree indexing.
• Your proposed solution should focus on the most common and impactful query patterns for an e-commerce scenario.
• The number of rows in Products can be up to 10 million.
• The number of rows in Orders can be up to 100 million.
• You should aim to create no more than 5 indexes in total across both tables to balance performance gains with maintenance overhead.

Notes

• Consider queries that involve joins between Products and Orders (e.g., finding all orders for products in a specific category). However, for this challenge, focus primarily on single-table queries unless a join is absolutely critical to demonstrate an indexing concept.
• Think about the selectivity of columns. Columns with a high number of distinct values (high cardinality) are generally good candidates for indexing, especially when used in WHERE clauses.
• Be mindful of the order of columns in composite indexes. The most frequently filtered columns should typically come first.
• The goal is to reduce the amount of data the database needs to scan for common operations.