SQL Query Optimization | SQL Query Optimization Techniques | SQL Basics | SQL Query | Simplilearn

Indexes For SQL Query Optimization
Indexing in SQL acts as a shortcut that helps the database find and retrieve data
much faster.

Types Of Indexes
Clustered Indexes - Clustered indexes physically sort and store
data based on actual column values.
Non Clustered Index Clustered Index

Types Of Indexes
Non Clustered Index Clustered Index
Non-Clustered Indexes –They create a separate lookup structure that points to
the actual data.

Types Of Indexes
Full-Text Indexes –Full-text indexes help by storing word positions, making
searches much faster instead of scanning the entire text.

How Indexing Speeds Up Queries
SELECT * FROM customers WHERE name = 'John Doe';
Without an index, the query scans every row:
But by creating an index on the ‘name’ column,
the query runs much faster.
CREATE INDEX idx_customer_name ON customers(name);

Avoid SELECT * And Use Necessary Columns
When you use **SELECT *** in your queries, you’re asking the database to retrieve
every single column, even if you only need a few.
SELECT * FROM customers.customer_profiles;
SELECT customer_name, customer_email, customer_phone FROM
customers.customer_profiles;

Optimizing JOIN Operations
If you use the wrong type of join, you might end up with duplicate records, slow query
performance, or unnecessary data bloating your results. Understanding INNER JOIN, OUTER
JOIN, LEFT JOIN, and RIGHT JOIN is crucial for writing efficient queries.
Outer Joins – Use with Caution!
OUTER JOIN is like keeping everything from both, even the unrelated data.
It can create duplicates and bloat your dataset unnecessarily.

INNER JOIN only returns records where there’s a match between both tables.
It keeps your dataset clean and ensures you only get the necessary data.
Inner Joins – Your Best Friend!

SELECT
customer_orders.customer_id,
order_details.order_id,
order_details.order_date
FROM customers.customer_orders customer_orders
INNER JOIN orders.order_details order_details
ON customer_orders.customer_id = order_details.customer_id
AND customer_orders.customer_order_id = order_details.order_id;

LEFT JOIN Vs RIGHT JOIN

Use EXISTS Instead Of IN
When checking if a value exists in a smaller dataset, EXISTS is usually much faster than IN ,
because EXISTS stops searching as soon as it finds a match, whereas IN scans the entire
dataset before deciding—making it slower, especially with large tables.
SELECT * FROM orders WHERE customer_id IN (SELECT id FROM customers
WHERE active = TRUE);
SELECT * FROM orders o
WHERE EXISTS (SELECT 1 FROM customers c WHERE c.id = o.customer_id AND
c.active = TRUE);

Minimize Subqueries & Use CTEs Instead
1️
1️
⃣ Subqueries Can Get Messy – Using too many subqueries in SQL makes your code
hard to read, debug, and optimize, just like untangling a messy ball of wires.
2️
⃣ CTEs Make Queries Cleaner – Common Table Expressions (CTEs) break queries into
readable sections, making them easier to understand, troubleshoot, and maintain—
just like writing clear step-by-step instructions.

Minimize Subqueries & Use CTEs Instead
SELECT MAX(customer_signup) AS most_recent_signup
FROM (SELECT customer_name, customer_phone, customer_signup
FROM customer_details
WHERE YEAR(customer_signup) = 2023);
WITH
2023_signups AS (
SELECT customer_name, customer_phone, customer_signup
FROM customer_details
WHERE YEAR(customer_signup) = 2023
),
Most_recent_signup AS (
FROM 2023_signups
)

Avoid Queries Inside A Loop
When you run SQL queries inside a loop—it makes
execution unnecessarily slow and inefficient.
Loops like FOR, WHILE, and DO-WHILE run queries one at a time, causing
repeated parsing, compiling, and execution, which hurts performance and
scalability.

FOR EACH customer IN customer_list LOOP
UPDATE customers SET status = 'Active' WHERE id = customer.id;
END LOOP;
UPDATE customers
SET status = 'Active'
WHERE id IN (SELECT id FROM customer_list);
Avoid Queries Inside A Loop

Avoid Redundant Data Retrieval
The larger the dataset, the slower the query, and the more resources you
waste—which can increase costs, especially in cloud-based databases.
Select Only the Needed Columns – We’ve already covered why **SELECT *** is
inefficient, but it’s also important to limit the number of rows returned, not just
columns.
Use LIMIT to Control Output – Queries that return thousands or millions of rows
can drag down performance, so always limit the results when needed.

Avoid Redundant Data Retrieval
SELECT customer_name FROM customer_details
ORDER BY customer_signup DESC
LIMIT 100;
Instead of pulling all customers, retrieve only the latest 100 signups:
SELECT customer_name FROM customer_details
ORDER BY customer_signup DESC
LIMIT 100 OFFSET 20;
Want to skip the first 20 rows and get the next 100? Use OFFSET:

How to Create A Stored Procedure?
Let’s say we want to find the most recent customer signup. Instead of writing the query
every time, we can save it as a stored procedure:
CREATE PROCEDURE find_most_recent_customer
AS BEGIN
FROM 2023_signups;
END;
EXEC find_most_recent_customer;

How to Create A Stored Procedure?
Adding Parameters for Flexibility
CREATE PROCEDURE find_most_recent_customer @store_id
INT
AS BEGIN
FROM 2023_signups
WHERE store_id = @store_id;
END;
EXEC find_most_recent_customer @store_id = 1188;

Normalize Your Database Tables
1. First Normal Form (1NF) – No Nested Data!

2. Second Normal Form (2NF) – Split Multi-Value Fields

3. Third Normal Form (3NF) – Remove Column Dependencies

Monitoring Query Performance
When you run SQL queries without monitoring their performance—you have no idea
which queries are slowing down your database or costing you extra resources.
Identifies Slow Queries
Saves Costs in Cloud Databases
Prevents System Slowdowns

Tools for Monitoring Query Performance
Query Profiling
Query Execution Plans
Database Logs & Server Monitoring

Let’s say you have a slow query, and you want to analyze its
performance. You can use MySQL’s EXPLAIN statement:
EXPLAIN SELECT customer_name FROM customers
WHERE city = 'New York';

Use UNION ALL Instead Of UNION
If you use UNION, you’re double-checking every name to remove duplicates
before finalizing the list. But if you use UNION ALL, you simply combine both lists
as they are—faster, simpler, and without extra processing.
UNION – Combines results from two tables and removes duplicates (which takes
extra processing time).
UNION ALL – Combines results without checking for duplicates, making it faster
and more efficient.

Use UNION ALL Instead Of UNION
SELECT customer_name FROM customers_2023
UNION
SELECT customer_name FROM customers_2024;
SELECT customer_name FROM customers_2023
UNION ALL
SELECT customer_name FROM customers_2024;

Leverage Cloud Database-Specific Features
Cloud providers like Snowflake, BigQuery, and Redshift come with built-in
optimizations and custom SQL functions designed to improve performance,
simplify queries, and handle data more efficiently.
Built-in Functions
Optimized Performance
Cost Efficiency

Leverage Cloud Database-Specific Features
Example: Snowflake’s JSON Functions
SELECT
json_column:customer.name::STRING AS customer_name,
json_column:customer.email::STRING AS customer_email
FROM orders;

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