10 SQL Tricks That Make Queries 10x Faster

Slow SQL queries are frustrating.

You write the correct query… but it takes forever to run.

If you want to stand out as a data analyst or BI professional, understanding performance optimization is a serious advantage.

Here are 10 SQL tricks that can dramatically improve query speed.

Use Indexes Properly

Indexes are the #1 performance booster.

Without indexes, SQL scans the entire table (Full Table Scan).
With indexes, it jumps directly to the needed rows.

Example:

CREATE INDEX idx_customer_id 
ON orders(customer_id);

Index columns that are frequently used in:

• WHERE
• JOIN
• ORDER BY

But don’t over-index because too many indexes slow down inserts and updates.

Avoid SELECT *

This is a beginner mistake.

Instead of:

SELECT * FROM customers;

Use:

SELECT name, email FROM customers;

Selecting only necessary columns reduces memory usage and speeds execution.

Filter Early (Use WHERE Before GROUP BY)

Always reduce data before aggregation.

Bad:

SELECT country, COUNT(*)
FROM customers
GROUP BY country;

Better:

SELECT country, COUNT(*)
FROM customers
WHERE active = 1
GROUP BY country;

Filtering early reduces rows processed.

Use EXISTS Instead of IN (For Large Subqueries)

For large datasets, EXISTS often performs better than IN.

Instead of:

SELECT *
FROM customers
WHERE customer_id IN (
    SELECT customer_id FROM orders
);

Use:

SELECT *
FROM customers c
WHERE EXISTS (
    SELECT 1 
    FROM orders o 
    WHERE c.customer_id = o.customer_id
);

This can significantly improve performance in large tables.

Avoid Functions in WHERE Clause

This prevents index usage.

Bad:

WHERE YEAR(order_date) = 2025;

Better:

WHERE order_date BETWEEN '2025-01-01' AND '2025-12-31';

When you apply functions to indexed columns, SQL cannot use the index efficiently.

Use Proper JOIN Types

Unnecessary JOINs slow queries.

• Use INNER JOIN when possible
• Avoid joining unused tables
• Ensure JOIN columns are indexed

JOIN performance directly impacts dashboards in tools like Microsoft Power BI and Tableau.

Use LIMIT (or TOP)

If you don’t need all rows, don’t retrieve them.

SELECT *
FROM orders
ORDER BY order_date DESC
LIMIT 100;

This is especially useful when testing queries.

Use Proper Data Types

Choosing the right data type matters.

For example:

• Use INT instead of VARCHAR for numeric IDs
• Avoid oversized VARCHAR fields
• Use DATE instead of TEXT for date columns

Smaller, optimized data types improve speed.

Analyze Query Execution Plan

Most databases allow you to check execution plans.

Example:

EXPLAIN SELECT * FROM orders;

This shows:

• Index usage
• Table scans
• Join methods

Understanding execution plans is a major skill for mid-level analysts.

Avoid Unnecessary Subqueries

Sometimes JOIN is faster than subqueries.

Instead of nesting multiple subqueries, rewrite using JOIN when appropriate.

Complex nested queries can dramatically slow performance.

Partition Large Tables

For very large datasets, table partitioning improves performance by dividing data into manageable chunks.

This is more advanced but extremely powerful in enterprise environments.

Why SQL Performance Matters

Slow queries affect:

• Dashboard loading times
• Data pipeline refresh speed
• Business reporting reliability
• Stakeholder confidence

If your Power BI or Tableau dashboard is slow, the SQL behind it is often the real issue.

Interview Tip

If asked:

“How do you optimize SQL queries?”

Mention:

• Indexing
• Avoiding SELECT *
• Using EXISTS
• Checking execution plans
• Filtering early

That’s a strong answer.

Writing correct SQL is step one.

Writing efficient SQL makes you valuable.

Performance tuning is what separates:

Beginner analysts → Professional analysts

Start applying these tricks in your daily practice. Over time, query optimization will become second nature.

FAQs