Concurrency Control Techniques to Know for Programming Techniques III

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.