Concurrency Control Techniques

Concurrency control techniques are essential for managing multiple transactions in programming. They ensure data integrity and consistency while allowing simultaneous access. Key methods include Two-Phase Locking, Timestamp Ordering, and Optimistic Concurrency Control, each with unique strengths and challenges.

1. Two-Phase Locking (2PL)

   • Ensures serializability by requiring transactions to acquire locks before accessing data.
   • Divided into two phases: the growing phase (acquiring locks) and the shrinking phase (releasing locks).
   • Can lead to deadlocks if transactions wait indefinitely for each other to release locks.

2. Timestamp Ordering

   • Assigns a unique timestamp to each transaction to determine the order of execution.
   • Ensures that transactions are executed in the order of their timestamps, maintaining consistency.
   • Can lead to transaction aborts if a transaction tries to access data that has been modified by a younger transaction.

3. Optimistic Concurrency Control

   • Assumes that conflicts are rare and allows transactions to execute without locking resources.
   • Consists of three phases: read, validate, and write; validation checks for conflicts before committing.
   • Suitable for environments with low contention but can lead to increased abort rates under high contention.

4. Multiversion Concurrency Control (MVCC)

   • Maintains multiple versions of data items to allow concurrent access without locking.
   • Readers access the snapshot of the data at the time they started, while writers create new versions.
   • Reduces contention and improves performance, especially in read-heavy workloads.

5. Serialization Graph Testing

   • A method to check if a schedule of transactions is conflict-serializable by constructing a serialization graph.
   • Nodes represent transactions, and edges represent conflicts between them.
   • If the graph is acyclic, the schedule is conflict-serializable; otherwise, it is not.

6. Deadlock Prevention and Detection

   • Prevention techniques include resource ordering and wait-die or wound-wait schemes to avoid circular wait conditions.
   • Detection involves periodically checking for cycles in the wait-for graph to identify deadlocked transactions.
   • Once detected, deadlocks can be resolved by aborting one or more transactions.

7. Isolation Levels

   • Defines the degree to which the operations in one transaction are isolated from those in other transactions.
   • Common levels include Read Uncommitted, Read Committed, Repeatable Read, and Serializable.
   • Higher isolation levels provide more consistency but can lead to reduced concurrency and performance.

8. Lock Granularity

   • Refers to the size of the data item being locked, which can range from a single row to an entire database.
   • Fine-grained locking (e.g., row-level) allows for higher concurrency but increases overhead.
   • Coarse-grained locking (e.g., table-level) reduces overhead but can lead to contention and lower concurrency.

9. Read-Write Locks

   • Allows multiple transactions to read a data item simultaneously while ensuring exclusive access for writing.
   • Promotes higher concurrency by differentiating between read and write operations.
   • Writers may be blocked by readers, leading to potential starvation if not managed properly.

10. Conflict Serializability

    • A schedule is conflict-serializable if it can be transformed into a serial schedule by swapping non-conflicting operations.
    • Ensures that the final state of the database is the same as if the transactions were executed one after the other.
    • Important for maintaining data integrity and consistency in concurrent transaction processing.