SQL Programming Resources on Telegram: “EXPLAIN SELECT * FROM orders WHERE customer

EXPLAIN
SELECT *
FROM orders
WHERE customer_id = 101;

If supported by your database:

EXPLAIN ANALYZE
SELECT *
FROM orders
WHERE customer_id = 101;

🔥 Mini Challenge

You have an "orders" table containing 50 million

rows:

• order_id

• customer_id

• order_date

• status

• region

• order_amount

The following query is running slowly:

SELECT
    order_id,
    order_date,
    order_amount
FROM orders
WHERE customer_id = 5001
  AND order_date >= DATE '2026-01-01'
  AND status = 'Completed';

Think about:

1. Which columns are being filtered?

2. Would a composite index be worth investigating?

3. Which column should come first?

4. Should you use "SELECT *"?

5. How would you inspect the execution plan?

6. Would the index always be used?

7. What happens to performance when the table receives millions of new rows?

Possible candidate to investigate:

CREATE INDEX idx_orders_customer_date
ON orders(customer_id, order_date);

Then inspect the query:

EXPLAIN
SELECT
    order_id,
    order_date,
    order_amount
FROM orders
WHERE customer_id = 5001
  AND order_date >= DATE '2026-01-01'
  AND status = 'Completed';

Don't automatically assume this is the optimal index.

Use the execution plan, data distribution, workload, and database-specific behavior to determine whether it actually helps.

🎯 Key Takeaway

Remember:

INDEX → Helps the database find data efficiently

COMPOSITE INDEX → Index on multiple columns

EXPLAIN → Inspect how the database plans to execute a query

SELECTIVITY → How much a filter narrows the data

And the most important principle:

More indexes ≠ Always faster

Good indexing + Good query design + Execution-plan analysis = Better SQL performance

A strong Data Analyst doesn't just write SQL that produces the correct answer.

They also understand how that SQL behaves when the data grows from thousands of rows to millions or billions. 🚀

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