What is a Phantom Read in database transactions?

A Phantom Read occurs when two identical queries within the same transaction return different results because another transaction inserted or deleted rows in the meantime.

How can Phantom Reads be prevented?

Phantom Reads can be prevented by using the SERIALIZABLE isolation level, which ensures that concurrent transactions behave as if they were executed one after another, avoiding phantom rows.

What is the difference between Phantom Read and Non-Repeatable Read?

In a Phantom Read, new rows appear or existing rows disappear between queries. In a Non-Repeatable Read, an existing row's data changes between queries. Both are concurrency issues but differ in scope.

Phantom Read in Database Transactions Interactive Demo

👻 What is Phantom Read?

Phantom Read: When a transaction re-executes a query and finds new rows that didn't exist during the first execution, inserted by another concurrent transaction.

🥭 Mango Pickle Order Scenario

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Transaction 1: Count orders WHERE product = 'Mango Pickle' → Result: 5

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Transaction 2: INSERT new 'Mango Pickle' order → COMMIT

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Transaction 1: Same count query → Result: 6 (Phantom Row!)

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Problem: Inconsistent results within same transaction

Click above buttons to explore Phantom Read phenomenon!

⚡ Live Transaction Simulator

Simulate: Watch phantom reads happen in real-time with concurrent transactions

🔍 Transaction 1 (Reader)

Status: Ready to start

➕ Transaction 2 (Writer)

Status: Waiting for Transaction 1

🔒 Transaction Isolation Levels & Phantom Read

Isolation Level	Dirty Read	Non-Repeatable Read	Phantom Read	Performance	Use Case
READ UNCOMMITTED	❌ Possible	❌ Possible	❌ Possible	🚀 Fastest	Analytics, Reporting
READ COMMITTED	✅ Prevented	❌ Possible	❌ Possible	⚡ Fast	Most web applications
REPEATABLE READ	✅ Prevented	✅ Prevented	❌ Possible	🐌 Slower	Financial calculations
SERIALIZABLE	✅ Prevented	✅ Prevented	✅ Prevented	🐢 Slowest	Critical transactions

💻 SQL Code Examples

-- 👻 Phantom Read Demonstration
-- Transaction 1 (Connection 1)
SET TRANSACTION ISOLATION LEVEL READ COMMITTED;
BEGIN TRANSACTION;
-- First count query
SELECT COUNT(*) FROM Orders WHERE Product = 'Mango Pickle';
-- Result: 5 orders
-- ... doing other work ...
WAITFOR DELAY '00:00:05'; -- Wait 5 seconds
-- Meanwhile, Transaction 2 inserts and commits!
-- Second count query (same as first)
SELECT COUNT(*) FROM Orders WHERE Product = 'Mango Pickle';
-- Result: 6 orders (Phantom Read!)
COMMIT;

-- Transaction 2 (Connection 2) - Runs concurrently
SET TRANSACTION ISOLATION LEVEL READ COMMITTED;
BEGIN TRANSACTION;
-- Insert new order while Transaction 1 is running
INSERT INTO Orders (Product, Quantity, Customer)
VALUES ('Mango Pickle', 2, 'Babita Ji');
COMMIT; -- This creates the phantom row

-- 🛡️ SERIALIZABLE Solution - Prevents Phantom Reads
-- Transaction 1 with SERIALIZABLE isolation
SET TRANSACTION ISOLATION LEVEL SERIALIZABLE;
BEGIN TRANSACTION;
-- First count query - locks the range
SELECT COUNT(*) FROM Orders WHERE Product = 'Mango Pickle';
-- Result: 5 orders
-- Range lock prevents new inserts matching the condition
-- ... doing other work ...
WAITFOR DELAY '00:00:05';
-- Second count query
SELECT COUNT(*) FROM Orders WHERE Product = 'Mango Pickle';
-- Result: 5 orders (Consistent!)
COMMIT;
-- Now Transaction 2 can proceed

-- Transaction 2 - Will be blocked until Transaction 1 commits
SET TRANSACTION ISOLATION LEVEL SERIALIZABLE;
BEGIN TRANSACTION;
-- This INSERT will be blocked by Transaction 1's range lock
INSERT INTO Orders (Product, Quantity, Customer)
VALUES ('Mango Pickle', 2, 'Babita Ji');
-- Waits until Transaction 1 completes
COMMIT;

✅ Benefits of SERIALIZABLE:

Prevents all concurrency phenomena (dirty read, non-repeatable read, phantom read)
Ensures consistent results within a transaction
Range locks prevent phantom rows

⚠️ Trade-offs:

Reduced concurrency (transactions may wait)
Higher chance of deadlocks
Lower performance

-- 🌍 Real World Example: E-commerce Inventory Management
-- Scenario: Checking low-stock items for reorder
-- Transaction 1: Daily inventory check
BEGIN TRANSACTION;
-- Find all low-stock items (< 10 units)
SELECT ProductID, ProductName, Stock
FROM Inventory
WHERE Stock < 10;
-- Result: 3 products need reordering
-- Calculate total reorder cost
SELECT SUM(ReorderCost) as TotalCost
FROM Inventory
WHERE Stock < 10;
-- Result: $1,500
-- ... manager reviews the list ...
-- Re-run the same queries for final confirmation
SELECT COUNT(*) FROM Inventory WHERE Stock < 10;
-- Without SERIALIZABLE: Could show 4 products (Phantom!)
-- With SERIALIZABLE: Shows 3 products (Consistent)
COMMIT;

-- Transaction 2: Concurrent sale reduces stock
BEGIN TRANSACTION;
-- Customer purchases last units of a product
UPDATE Inventory
SET Stock = Stock - 5
WHERE ProductID = 'WIDGET-123';
-- This brings stock from 12 to 7 (now < 10!)
COMMIT;
-- Creates a phantom row for Transaction 1's queries

🎯 Business Impact:

Without SERIALIZABLE: Inventory report shows inconsistent data, wrong reorder amounts
With SERIALIZABLE: Consistent snapshot of inventory throughout the transaction
Use Case: Financial reports, audit trails, inventory management

💡 Best Practices for Handling Phantom Reads

✅ Do This

Use SERIALIZABLE for critical financial operations
Choose appropriate isolation level based on consistency needs
Consider application-level consistency checks
Use optimistic locking for better performance
Test your application with concurrent load

❌ Avoid This

Using READ UNCOMMITTED for financial data
Ignoring phantom reads in reporting systems
Always using SERIALIZABLE (performance impact)
Not handling isolation level exceptions
Mixing different isolation levels carelessly