Query Performance Optimization: The Indexing Dilemma

In real-world database applications, performance is paramount. Slow queries can lead to a poor user experience, increased server costs, and missed business opportunities. A common bottleneck is inefficient data retrieval, often stemming from a lack of appropriate indexes on tables. This challenge focuses on identifying and implementing the correct indexing strategies to significantly speed up a given SQL query.

Problem Description

You are given a database schema with several tables and a specific, frequently executed SQL query that is currently performing poorly. Your task is to analyze the query, understand how the database is likely executing it, and then propose and justify the creation of new indexes (or the modification/removal of existing ones) that will optimize the query's performance. You will need to explain why your proposed indexes are beneficial and how they address the query's bottlenecks.

Key Requirements:

• Identify the columns that are candidates for indexing based on their usage in the provided SQL query.
• Propose the most effective indexing strategy (e.g., single-column, composite, covering indexes).
• Justify your index choices by explaining how they reduce the query execution time (e.g., by facilitating index seeks, reducing table scans, or enabling index-only scans).
• Demonstrate the impact of your proposed indexes, ideally by showing a hypothetical or observed improvement in query execution.

Expected Behavior:

The optimized query, after applying your recommended indexes, should execute significantly faster than the original query.

Edge Cases to Consider:

• Queries involving JOINs on multiple columns.
• Queries with WHERE clauses using various operators (=, >, <, LIKE, IN).
• Queries with ORDER BY or GROUP BY clauses.
• The potential for indexes to slow down write operations (INSERT, UPDATE, DELETE) and the trade-offs involved.

Examples

Example 1: Simple Filtering

Input Database Schema:
TABLE `users` (
  `user_id` INT PRIMARY KEY,
  `username` VARCHAR(50),
  `registration_date` DATE
);

TABLE `orders` (
  `order_id` INT PRIMARY KEY,
  `user_id` INT,
  `order_date` DATE,
  `total_amount` DECIMAL(10, 2)
);

Original Query:
SELECT o.order_id, o.order_date, u.username
FROM orders o
JOIN users u ON o.user_id = u.user_id
WHERE o.order_date >= '2023-01-01';

Output (Proposed Solution):

• Proposed Index: CREATE INDEX idx_orders_order_date ON orders (order_date);
• Explanation: The original query filters orders by order_date. Without an index on order_date, the database would likely perform a full table scan on the orders table. Adding an index on order_date allows the database to quickly locate only the rows matching the WHERE clause condition, significantly reducing the number of rows it needs to process for the join.

Example 2: Composite Index for Join and Filter

Input Database Schema:
TABLE `products` (
  `product_id` INT PRIMARY KEY,
  `product_name` VARCHAR(100),
  `category_id` INT
);

TABLE `categories` (
  `category_id` INT PRIMARY KEY,
  `category_name` VARCHAR(50)
);

TABLE `reviews` (
  `review_id` INT PRIMARY KEY,
  `product_id` INT,
  `rating` INT,
  `review_date` DATE
);

Original Query:
SELECT p.product_name, AVG(r.rating) AS average_rating
FROM products p
JOIN reviews r ON p.product_id = r.product_id
JOIN categories c ON p.category_id = c.category_id
WHERE c.category_name = 'Electronics'
GROUP BY p.product_id, p.product_name
ORDER BY average_rating DESC;

Output (Proposed Solution):

• Proposed Indexes:
  1. CREATE INDEX idx_products_category_id ON products (category_id);
  2. CREATE INDEX idx_reviews_product_id ON reviews (product_id);
  3. CREATE INDEX idx_reviews_product_id_rating ON reviews (product_id, rating); (or idx_reviews_product_id_rating_date if date is also heavily used in filtering/joining)
• Explanation:
  • idx_products_category_id helps the join with categories by quickly finding products belonging to a specific category.
  • idx_reviews_product_id helps the join with products by quickly finding reviews for specific products.
  • idx_reviews_product_id_rating is a composite index. It's particularly useful because the reviews table is joined on product_id and the rating is used in the AVG aggregate function. This index allows the database to potentially read the product_id and rating directly from the index (covering index) for rows matching the WHERE clause, minimizing the need to access the full reviews table data. It also assists in grouping by product_id.

Example 3: Handling LIKE and ORDER BY

Input Database Schema:
TABLE `customers` (
  `customer_id` INT PRIMARY KEY,
  `first_name` VARCHAR(50),
  `last_name` VARCHAR(50),
  `email` VARCHAR(100)
);

TABLE `transactions` (
  `transaction_id` INT PRIMARY KEY,
  `customer_id` INT,
  `transaction_amount` DECIMAL(10, 2),
  `transaction_time` DATETIME
);

Original Query:
SELECT c.first_name, c.last_name, t.transaction_amount
FROM customers c
JOIN transactions t ON c.customer_id = t.customer_id
WHERE c.last_name LIKE 'S%' AND t.transaction_amount > 1000
ORDER BY t.transaction_time DESC
LIMIT 10;

Output (Proposed Solution):

• Proposed Indexes:
  1. CREATE INDEX idx_customers_last_name ON customers (last_name);
  2. CREATE INDEX idx_transactions_customer_id_amount_time ON transactions (customer_id, transaction_amount, transaction_time);
• Explanation:
  • idx_customers_last_name directly supports the WHERE c.last_name LIKE 'S%' condition by allowing for efficient searching of last names starting with 'S'.
  • The composite index idx_transactions_customer_id_amount_time on transactions is crucial. It includes customer_id for the join, transaction_amount for the filtering, and transaction_time for the ORDER BY clause. This index can potentially satisfy the entire query for the transactions table, enabling an index seek for filtering, sorting, and even limiting results without a full table scan or a separate sort operation.

Constraints

• The database system (e.g., PostgreSQL, MySQL, SQL Server) is not specified; assume standard SQL syntax for index creation.
• The input query will involve at least two tables joined by foreign keys.
• The query will contain at least one WHERE clause condition and either an ORDER BY or GROUP BY clause.
• The total number of rows in any given table can be up to 1 billion.
• The number of columns in any given table is at most 50.
• You should aim for a solution that reduces the query execution time by at least 75% compared to the original, unindexed query.

Notes

• Consider the order of columns in composite indexes carefully. The leading columns should be those used most frequently in WHERE clauses.
• Think about whether a "covering index" is possible, where all columns needed by the query are present in the index, eliminating the need to access the base table.
• Be mindful of the trade-off: indexes speed up reads but can slow down writes. Your proposed solution should prioritize significant read performance gains for the given query.
• You are not required to rewrite the query itself, only to propose the indexing strategy.