Imagine two users simultaneously try to update the same row in a table. How can you prevent the "lost update" problem where one user's changes overwrite the other's?

The lost update problem occurs when multiple users update the same row without proper concurrency control, leading to one update overwriting another without knowledge of the previous change.

Approaches to Prevent Lost Updates

1. Optimistic Concurrency Control (OCC)
This approach checks if the row has changed before committing an update.

Steps:

1. Fetch the row with its version number or timestamp.
2. Attempt to update only if the version number hasn’t changed.
3. If another user has modified the row, reject the update.

SQL Implementation (Using Version Number)

ALTER TABLE users ADD COLUMN version INT DEFAULT 1;

-- User 1 reads the row
SELECT user_id, name, version FROM users WHERE user_id = 1;

-- User 1 tries to update, ensuring the version matches
UPDATE users 
SET name = 'Alice Updated', version = version + 1 
WHERE user_id = 1 AND version = 1;

-- If no rows were updated, another user modified the data first.

Pros:

• Works well when conflicts are rare.
• Does not block other users from reading.

Cons:

• Requires application logic to handle retries.

2. Pessimistic Locking (Explicit Row Locking)
This approach locks the row until a transaction is committed, preventing others from modifying it.

SQL Implementation (Using SELECT ... FOR UPDATE)

BEGIN;
SELECT * FROM users WHERE user_id = 1 FOR UPDATE;
UPDATE users SET name = 'Alice Updated' WHERE user_id = 1;
COMMIT;

The FOR UPDATE lock ensures no other transaction can modify this row until the first one commits.

Pros:

• Guarantees no lost updates.
• Works well when conflicts are frequent.

Cons:

• Can cause deadlocks if multiple users lock different rows.
• Reduces concurrency, making the system slower under heavy load.

3. Using Transaction Isolation Levels
Databases provide isolation levels to manage concurrency. The highest level, SERIALIZABLE, prevents lost updates but can reduce performance.

Setting SERIALIZABLE Isolation Level

SET TRANSACTION ISOLATION LEVEL SERIALIZABLE;
BEGIN;
SELECT * FROM users WHERE user_id = 1;
UPDATE users SET name = 'Alice Updated' WHERE user_id = 1;
COMMIT;

Ensures transactions are executed one at a time, preventing conflicts.

Pros:

• No need for explicit locking.
• Ensures strong consistency.

Cons:

• Reduces concurrency, leading to performance bottlenecks.
• Not suitable for high-traffic applications.

Nuances to Consider

• Choose the right approach based on workload
  • Optimistic locking works best when conflicts are rare.
  • Pessimistic locking is safer but can block transactions.
• Deadlocks can occur with locks
  • If multiple transactions lock different rows, deadlocks may arise.
• Application-level handling
  • Some databases like PostgreSQL support ON CONFLICT to handle update collisions gracefully.

Tecyfy Takeaway
Preventing the lost update problem requires choosing the right concurrency control mechanism.

• Optimistic locking is best for occasional conflicts.
• Pessimistic locking ensures safety but can slow performance.
• SERIALIZABLE isolation prevents issues but is costly in high-load environments.
  **Choosing the right method depends on the balance between performance and consistency.