Last Updated on July 20, 2023 by Mayank Dham
Lock-based protocols provide a systematic approach to managing concurrent access to shared data items in a DBMS. These protocols employ locks, which are mechanisms to control and coordinate the access to data by transactions. By acquiring and releasing locks, transactions can guarantee exclusive or shared access to data, preventing conflicts and maintaining data integrity.
What are lock based protocols in DBMS?
Lock-based protocols in database management systems (DBMS) are concurrency control mechanisms that ensure data integrity by coordinating and managing concurrent access to shared data items. These protocols use locks as a means of controlling and regulating access to data by transactions.
In a multi-user DBMS environment, multiple transactions may simultaneously request access to the same data item, leading to potential conflicts. Without proper coordination, these conflicts can result in data inconsistencies or even data corruption. Lock-based protocols provide a structured approach to address these issues and maintain the consistency of the database.
Locks are acquired and released by transactions to control their access to data. When a transaction wants to read or modify a data item, it first requests a lock for that particular item. The lock can be either an exclusive lock (write lock) or a shared lock (read lock), depending on the transaction’s intent.
Key Points Related to Locking Techniques in DBMS
Lock Modes: Lock modes determine the level of access and exclusivity granted to transactions. Exclusive locks (X-locks) provide write access, ensuring that no other transaction can read or write the locked data item. Shared locks (S-locks) allow multiple transactions to read the same data item simultaneously but prevent any transaction from writing to it.
Lock Granularity: Lock granularity refers to the level at which locks are acquired. It can be at the level of an entire database, a table, a page, or even a specific data item. The choice of lock granularity impacts concurrency and performance.
Transaction Isolation Levels: Isolation levels define the degree to which transactions are isolated from one another. They specify the level of concurrency and data consistency guaranteed by the DBMS. Lock-based protocols are closely tied to isolation levels, as they determine how locks are acquired and released during transaction execution.
Let’s see what are the four types of lock based protocols in DBMS.
Four Types of Lock Based Protocol in DBMS
In database management systems, there are four commonly recognized types of lock-based protocols. These protocols define different strategies for granting and releasing locks during the execution of transactions. The four types of lock-based protocols are:
1. Simplistic Lock Protocol:
A simplistic lock protocol, often referred to as a basic or elementary lock protocol, is a straightforward approach to concurrency control in a database management system (DBMS). It aims to provide a simple mechanism for coordinating access to shared data items by transactions. This type of lock protocol may lack some of the advanced features and optimizations found in more complex protocols but still ensures basic data integrity.
2. Pre-Claiming Lock Protocol:
The Pre-Claiming Lock Protocol is a concurrency control mechanism used in database management systems (DBMS) to minimize the occurrence of conflicts between transactions accessing shared data items. This protocol aims to improve concurrency and reduce waiting times by allowing transactions to pre-claim locks before actually needing them.
In the Pre-Claiming Lock Protocol, transactions have the ability to request and acquire locks on data items they anticipate accessing in the future. By pre-claiming locks, transactions reduce the likelihood of conflicts with other transactions that may require the same data items later.
3. Two-phase Locking Protocol:
The Two-Phase Locking (2PL) protocol is a well-known concurrency control mechanism used in database management systems (DBMS) to ensure data consistency and avoid conflicts between concurrent transactions. It divides the execution of a transaction into two distinct phases: the growing phase and the shrinking phase, governed by specific rules for acquiring and releasing locks.
The Two-Phase Locking protocol operates based on the following principles:
During the growing phase, a transaction can acquire locks but cannot release any locks. The transaction continues to request and acquire locks for data items it needs to read or write. Once a lock is released, no further locks can be acquired.
A transaction can request a lock on a data item when needed. Lock requests can be for shared locks (read access) or exclusive locks (write access) depending on the transaction’s intention. A transaction can acquire a lock only if it does not conflict with existing locks held by other transactions. Conflicts occur when a transaction requests an exclusive lock on a data item already locked by another transaction, or when a transaction requests a shared lock on a data item locked exclusively by another transaction.
A transaction releases all its locks during the shrinking phase. Once a lock is released, it cannot be reacquired. This ensures that the transaction preserves the consistency of the database state, preventing anomalies such as dirty reads, non-repeatable reads, and write skew.
4. Strict Two-Phase Locking Protocol:
The Strict Two-Phase Locking (S2PL) protocol is an enhanced version of the Two-Phase Locking (2PL) protocol, which provides stronger guarantees for concurrency control and data consistency in database management systems (DBMS). S2PL adds additional rules to the 2PL protocol to ensure strictness and prevent anomalies.
The Strict Two-Phase Locking protocol operates based on the following principles:
Similar to the 2PL protocol, the S2PL protocol has a growing phase where a transaction can acquire locks but cannot release any locks. During this phase, a transaction can request and acquire locks for data items it needs to read or write, following the rules of the 2PL protocol.
In the S2PL protocol, once a transaction acquires a shared lock on a data item, it cannot upgrade the lock to an exclusive lock. This means that a transaction cannot transition from a shared lock to an exclusive lock on the same data item during its execution. This restriction prevents a transaction from seeing an intermediate (inconsistent) state of another transaction’s modifications.
During the shrinking phase, a transaction releases all its locks in a single step. Once the locks are released, they cannot be reacquired. This ensures that the transaction preserves the strictness property, preventing the possibility of seeing uncommitted changes made by other transactions.
Starvation and Deadlock in DBMS
Starvation and deadlock are two critical issues that can occur in a database management system (DBMS) in relation to concurrency control and resource management.
Starvation refers to a situation where a transaction or process is unable to proceed or make progress due to the unfair allocation of resources. In the context of DBMS, it can happen when one or more transactions continuously get deprived of the resources they require, such as locks or system resources, while other transactions continuously acquire those resources, resulting in the starved transactions being unable to proceed.
Starvation can lead to decreased system performance, decreased concurrency, and potential delays in transaction processing. It can occur due to improper resource management policies or when certain transactions with higher priorities monopolize resources, preventing other transactions from making progress.
Preventing starvation requires implementing fairness mechanisms and resource allocation policies that ensure all transactions have a fair chance to acquire the necessary resources. Techniques such as priority-based scheduling, aging mechanisms, and timeout mechanisms can be employed to mitigate starvation and ensure equitable resource allocation.
Deadlock is a situation where two or more transactions or processes are unable to proceed because each is waiting for a resource that is held by another transaction. In other words, each transaction is waiting for a resource that is locked by another transaction, leading to a circular dependency and a state of impasse.
A deadlock can occur in a DBMS when transactions acquire locks on resources in a way that creates a circular wait dependency. For example, Transaction A holds a lock on Resource X and is waiting for Resource Y, while Transaction B holds a lock on Resource Y and is waiting for Resource X. This circular dependency creates a deadlock, and neither transaction can proceed until the other releases its lock.
Deadlocks can severely impact system performance and halt transaction processing. They require specific techniques for detection, prevention, and resolution. Deadlock detection algorithms can identify the presence of deadlocks in the system, while deadlock prevention mechanisms aim to prevent the occurrence of deadlocks by carefully managing resource allocations. Deadlock resolution techniques involve breaking the circular wait condition by either aborting one or more transactions or performing resource preemption to release resources held by certain transactions.
DBMSs employ various strategies and algorithms, such as deadlock detection algorithms (e.g., resource allocation graphs) and deadlock prevention techniques (e.g., using strict lock ordering or resource ordering), to handle deadlocks and minimize their occurrence.
In conclusion, lock-based protocols play a crucial role in ensuring data integrity and concurrency control in database management systems (DBMS). By coordinating the access to shared data items through locks, these protocols prevent conflicts and maintain the consistency of the database.
Throughout this article, we explored the concept of lock-based protocols, including the principles of shared locks and exclusive locks. We discussed the Two-Phase Locking (2PL) protocol, which divides the transaction execution into growing and shrinking phases, and the Strict Two-Phase Locking (S2PL) protocol, which adds further rules to ensure strictness and prevent anomalies. These protocols provide systematic approaches to managing concurrency and guaranteeing serializability, preventing phenomena like dirty reads and write skew.
FAQs Related To Lock Based Protocol in DBMS:
Here are some faq related to lock based protocol in DBMS, locking in DBMS, and locking techniques in DBMS.
Q1: Why is concurrency control important in a DBMS?
A1: Concurrency control ensures that multiple transactions can access and modify shared data concurrently while maintaining data integrity and consistency. It prevents conflicts, data inconsistencies, and anomalies that may occur when multiple transactions access the same data simultaneously.
Q2: What is the purpose of locks in lock-based protocols?
A2: Locks are mechanisms used in lock-based protocols to control and coordinate the access of transactions to shared data items. They provide exclusive or shared access to the data, preventing conflicts and maintaining data consistency.
Q3: What is the difference between shared locks and exclusive locks?
A3: Shared locks (S-locks) allow multiple transactions to read the same data item simultaneously, ensuring concurrent read access. Exclusive locks (X-locks) provide exclusive write access, ensuring that only one transaction can modify a data item at a time.
Q4: What is the Two-Phase Locking (2PL) protocol?
A4: The Two-Phase Locking protocol divides the execution of a transaction into two phases: the growing phase and the shrinking phase. During the growing phase, transactions acquire locks and can request additional locks. In the shrinking phase, transactions release locks but cannot request new locks. 2PL ensures serializability and prevents phenomena like dirty reads and write skew.
Q5: What is the Strict Two-Phase Locking (S2PL) protocol?
A5: The Strict Two-Phase Locking protocol is an extension of the 2PL protocol. It enforces additional rules, such as prohibiting lock conversions, to ensure strictness. S2PL guarantees that no transaction sees intermediate (inconsistent) states of another transaction’s modifications, ensuring strict serializability.