Question

Why is two-phase locking not used as a concurrency control method for indexes \(\operatorname{such}\) as \(\mathrm{B}^{+}\) -trees?

Short answer

Two-Phase Locking is not used as a concurrency control method for indexes such as B+-trees, mainly because it substantially increases the chances for deadlocks. B+-trees have many disk read and write operations, and the potential for tree-wide locks due to page splits or consolidation can cause significant waiting times for concurrent transactions, leading to an increased likelihood of deadlock scenarios.

Step-by-step solution

Understanding Two-Phase Locking

Two-Phase Locking (2PL) is a concurrency control method that ensures serializability in the order of transactions. It operates in two phases: expansion, where locks are acquired and no locks are released, and the shrinking phase, where locks are released and no new ones are acquired.

Understanding B+-trees as Indexes

B+-tree is a type of data structure that extends Binary Search trees and allows for efficient insertion, deletion and search operations. It's mainly used for disk read and write operations in the DBMS, where the data blocks are stored in the leaf nodes.

Why Two-Phase Locking is Not Used With B+-trees

Two-Phase Locking can make the system highly concurrent and enhance system throughput, but it doesn't suit well for B+-tree. The reason is 2PL can augment the chance of deadlocks where transactions wait for each other to release the resources they need. As B+-trees are dynamically maintained with disk read and write operations, the page splits due to insertion or consolidation due to deletion can lead to entire tree locked by a transaction, causing concurrent transactions to wait for a longer time and thus increasing the chances of deadlocks.

Key concepts

Two-Phase Locking

Two-phase locking, often referred to as 2PL, is a protocol that databases use to maintain consistent transactions and prevent issues that arise from concurrency. Concurrency control is crucial in multi-user database environments where simultaneous transactions can lead to conflicts.

2PL works by dividing the transaction into two distinct phases: the growing phase and the shrinking phase. During the growing phase, a transaction can obtain as many locks as it needs but cannot release any. Once the transaction acquires all the necessary locks and starts to release them, it signals the beginning of the shrinking phase, during which no new locks can be acquired. This mechanism ensures the 'serializability' of transactions, meaning they are executed in a sequence that prevents conflicts effectively leading to the same result as if the transactions were processed one at a time. However, while 2PL facilitates serializability, it also brings a higher risk of deadlocks which can be a downside in systems with heavy concurrency requirements.

B+-trees

B+-trees are a type of data structure that serves as an index mechanism to facilitate rapid access to disk-based data. Unlike simple binary trees, B+-trees are multi-level, balanced trees that keep data sorted and allow searches, insertions, and deletions in logarithmic time.

The structure of a B+-tree is such that all the values are stored at the leaf nodes, and these leaves are linked, which provides a high-speed pathway for sequential access. Internal nodes act as pointers to these leaves, making the tree an efficient structure for range queries and sequential data access. B+-trees are particularly favored in database management systems (DBMS) due to their ability to handle large volumes of data while maintaining a balanced tree, which minimizes the disk read and write operations.

Serializability

Serializability is the gold standard for database concurrency control. It is a concept that ensures the consistency of the database by making sure that the outcome of schedules (or sequences) of multiple transactions is the same as if the transactions had been executed serially, one after the other, without overlapping.

For a schedule to be serializable, it must be equivalent in effect to some serial execution of the same set of transactions. To determine if a schedule is serializable, databases employ various algorithms, possibly involving locking mechanisms like the two-phase locking protocol. However, achieving serializability can come at the cost of reduced system throughput due to the needed restrictions on transaction execution.

Deadlocks

Deadlocks are a specific and rather troublesome issue in the context of database systems where multiple transactions are waiting for each other to release locks, creating a circular dependency with no possibility of proceeding.

When a deadlock occurs, there are at least two transactions where each is holding a resource the other wants, and both are waiting for the other to release its resource before proceeding. Since neither can continue, this can lead to a complete system standstill. Deadlocks are detected through various methods including timeout mechanisms, where a transaction is rolled back if it cannot acquire a resource within a certain time, or through deadlock detection algorithms that periodically check for circular wait conditions. Once a deadlock is detected, one or more transactions may have to be aborted and rolled back to break the cycle and allow resource allocation to continue.