Concurrency Control

Concurrency Control is a mechanism in database management systems (DBMS) that ensures correct and consistent transaction execution in a multi-user environment. It prevents issues such as data inconsistencies and anomalies by managing simultaneous access to the database.

Key Concepts

• Transaction: A sequence of database operations that are executed as a single logical unit of work.
• Isolation: Ensures that each transaction is executed independently without interference.
• Consistency: Guarantees that the database remains in a consistent state after a transaction, even in the presence of concurrent transactions.

Problems Addressed by Concurrency Control

Concurrency control mechanisms resolve the following issues:

• Dirty Reads: A transaction reads uncommitted changes made by another transaction.
• Non-Repeatable Reads: A transaction reads the same data twice and gets different results due to concurrent updates.
• Phantom Reads: A transaction sees a different set of rows in repeated queries due to concurrent inserts or deletes.
• Lost Updates: Two transactions simultaneously update the same data, and one update overwrites the other.
• Write Skew: Concurrent updates create an inconsistent database state.

Techniques for Concurrency Control

The following techniques are commonly used for concurrency control:

Technique	Description	Advantages	Limitations
Lock-Based Protocols	Transactions acquire locks on data items to control access.	Prevents conflicts, widely used.	Can lead to deadlocks, reduced concurrency.
Timestamp-Based Protocols	Assigns timestamps to transactions to enforce serializability.	No locks, avoids deadlocks.	Requires global timestamp management.
Optimistic Concurrency Control	Validates transactions at commit time to ensure no conflicts.	High performance in low-contention systems.	Rollbacks can be expensive in high-contention systems.
Multiversion Concurrency Control (MVCC)	Maintains multiple versions of data to provide consistent snapshots.	Supports high concurrency, avoids read-write conflicts.	Storage overhead due to multiple versions.

Example: Lock-Based Protocol

Consider two transactions accessing the same account balance:

• Transaction A

 * Reads account balance.
 * Deducts $100.
 * Updates the balance.

• Transaction B

 * Reads account balance.
 * Adds $50.
 * Updates the balance.

Without locking, these transactions could overwrite each other's updates. Using locks:

1. Transaction A acquires a write lock on the balance.
2. Transaction B waits until Transaction A releases the lock.
3. Transaction A updates the balance and releases the lock.
4. Transaction B acquires the lock, reads the updated balance, and applies its changes.

Deadlock Handling

Lock-based protocols can lead to deadlocks, where two or more transactions wait indefinitely for each other to release locks. Common deadlock handling techniques include:

• Timeouts: Abort transactions that wait too long.
• Deadlock Detection: Use wait-for graphs to identify and resolve deadlocks.
• Deadlock Prevention: Ensure transactions acquire locks in a predefined order.

Applications of Concurrency Control

Concurrency control is essential in:

• Banking Systems: Preventing anomalies in financial transactions.
• E-Commerce: Ensuring consistency in inventory and orders.
• Distributed Databases: Managing consistency across multiple nodes.
• Real-Time Systems: Providing low-latency, conflict-free operations.

See Also

• Transaction Management
• Isolation Level
• Lock-Based Protocols
• Timestamp-Based Protocols
• Multiversion Concurrency Control
• Deadlock Detection
• Database Anomalies