Lock-Based Concurrency Control in Databases

1. Introduction

Concurrency control is a technique that ensures the correct and consistent execution of transactions in a database system. Transactions are units of work that access and modify data in a database. Concurrency control ensures that transactions do not interfere with each other and preserve the ACID properties of the database.

There are different methods of concurrency control, such as timestamp-based, validation-based, and lock-based. In this tutorial, you will learn about lock-based concurrency control, which is one of the most widely used techniques in database systems. Lock-based concurrency control uses locks to prevent concurrent transactions from accessing the same data item at the same time.

You will also learn about the two-phase locking protocol, which is a set of rules that transactions must follow to acquire and release locks. Two-phase locking ensures that transactions are serializable, meaning that they produce the same result as if they were executed one after another. You will explore the different variants of two-phase locking, such as basic, strict, and rigorous, and their advantages and disadvantages.

Finally, you will learn about deadlock prevention and detection, which are techniques to deal with the problem of deadlock. Deadlock is a situation where two or more transactions are waiting for each other to release locks, and none of them can proceed. You will learn how to prevent deadlock by imposing some restrictions on the transactions, and how to detect and resolve deadlock by using a wait-for graph.

By the end of this tutorial, you will have a solid understanding of lock-based concurrency control, two-phase locking, and deadlock prevention and detection. You will also be able to apply these concepts to your own database applications and improve their performance and reliability.

2. Lock-Based Concurrency Control

Lock-based concurrency control is a technique that uses locks to prevent concurrent transactions from accessing the same data item at the same time. A lock is a mechanism that grants or denies access to a data item based on some rules. A transaction must acquire a lock on a data item before reading or writing it, and release the lock after finishing its operation. By using locks, transactions can ensure that they do not interfere with each other and preserve the consistency and isolation of the database.

However, lock-based concurrency control also introduces some challenges, such as how to manage the locks, how to avoid conflicts and deadlocks, and how to balance the performance and concurrency of the system. In this section, you will learn about the basic concepts and principles of lock-based concurrency control, such as lock types and modes, lock compatibility matrix, and lock granularity. You will also learn how to apply these concepts to your own database applications and improve their efficiency and reliability.

Let’s start by understanding the different types and modes of locks that are used in lock-based concurrency control.

2.1. Lock Types and Modes

In lock-based concurrency control, there are two main types of locks: binary locks and multiple-mode locks. Binary locks have only two states: locked or unlocked. A transaction can either acquire a lock on a data item or release it. Multiple-mode locks have more than two states, such as shared, exclusive, or update. A transaction can acquire a lock on a data item with a specific mode, depending on the type of operation it wants to perform.

The mode of a lock determines the level of access that a transaction has on a data item. For example, a shared lock allows a transaction to read a data item, but not to write it. An exclusive lock allows a transaction to read and write a data item, but not to share it with other transactions. An update lock allows a transaction to read a data item and prepare to write it, but not to write it until it converts the lock to an exclusive mode.

The advantage of using multiple-mode locks is that they allow more concurrency than binary locks. For example, multiple transactions can acquire shared locks on the same data item and read it concurrently, without interfering with each other. However, multiple-mode locks also introduce more complexity and overhead than binary locks, as they require more rules and mechanisms to manage them.

Let’s see how these rules and mechanisms work by looking at the lock compatibility matrix.

2.2. Lock Compatibility Matrix

The lock compatibility matrix is a table that shows which lock modes are compatible or incompatible with each other. A lock mode is compatible with another lock mode if two transactions can acquire locks on the same data item with those modes without causing a conflict. A lock mode is incompatible with another lock mode if two transactions cannot acquire locks on the same data item with those modes without causing a conflict.

The lock compatibility matrix depends on the type and number of lock modes that are used in the system. For example, if the system uses only binary locks, then the lock compatibility matrix is simple: a lock is compatible with an unlocked state, and incompatible with a locked state. However, if the system uses multiple-mode locks, such as shared, exclusive, and update, then the lock compatibility matrix is more complex: a shared lock is compatible with another shared lock, but incompatible with an exclusive or an update lock; an exclusive lock is incompatible with any other lock mode; and an update lock is compatible with a shared lock, but incompatible with an exclusive or another update lock.

The lock compatibility matrix is important for managing the locks and ensuring the correctness and consistency of the transactions. The lock manager is a component of the database system that maintains the lock compatibility matrix and grants or denies lock requests from the transactions. The lock manager uses the lock compatibility matrix to check if a lock request is compatible with the existing locks on the data item. If the lock request is compatible, the lock manager grants the lock to the transaction. If the lock request is incompatible, the lock manager denies the lock to the transaction and puts it in a wait queue until the conflicting lock is released.

Here is an example of a lock compatibility matrix for a system that uses shared, exclusive, and update locks:

As you can see, the lock compatibility matrix helps the lock manager to decide whether to grant or deny a lock request, and thus to prevent conflicts and ensure serializability of the transactions.

2.3. Lock Granularity

Lock granularity is the level of detail at which locks are applied to the data items in the database. Lock granularity can range from fine-grained to coarse-grained, depending on the size and number of data items that are locked by a single lock. For example, a fine-grained lock granularity means that a lock is applied to a single record or attribute in the database, while a coarse-grained lock granularity means that a lock is applied to a whole table or file in the database.

The choice of lock granularity affects the performance and concurrency of the system. A fine-grained lock granularity allows more concurrency, as transactions can access different parts of the data without conflicting with each other. However, a fine-grained lock granularity also introduces more overhead, as transactions need to acquire and release more locks, and the lock manager needs to keep track of more lock information. A coarse-grained lock granularity reduces the overhead, as transactions need to acquire and release fewer locks, and the lock manager needs to keep track of less lock information. However, a coarse-grained lock granularity also reduces the concurrency, as transactions may block each other unnecessarily when accessing different parts of the data.

Therefore, the optimal lock granularity depends on the characteristics of the transactions and the data in the system. For example, if the transactions tend to access small and disjoint parts of the data, then a fine-grained lock granularity may be preferable. On the other hand, if the transactions tend to access large and overlapping parts of the data, then a coarse-grained lock granularity may be preferable.

Some database systems allow the lock granularity to be adjusted dynamically, depending on the workload and the contention level in the system. This is called lock escalation or de-escalation. Lock escalation means that the lock manager can combine several fine-grained locks into a single coarse-grained lock, to reduce the lock overhead and contention. Lock de-escalation means that the lock manager can split a coarse-grained lock into several fine-grained locks, to increase the concurrency and availability.

3. Two-Phase Locking Protocol

The two-phase locking protocol is a set of rules that transactions must follow to acquire and release locks in a lock-based concurrency control system. The two-phase locking protocol ensures that transactions are serializable, meaning that they produce the same result as if they were executed one after another. The two-phase locking protocol also prevents some types of anomalies, such as dirty reads, non-repeatable reads, and phantom reads, that may occur when transactions access the same data concurrently.

The two-phase locking protocol consists of two phases: the growing phase and the shrinking phase. In the growing phase, a transaction can acquire locks on the data items that it needs, but it cannot release any lock. In the shrinking phase, a transaction can release locks on the data items that it no longer needs, but it cannot acquire any new lock. The point where the transaction switches from the growing phase to the shrinking phase is called the lock point. The lock point is usually the same as the commit point, which is the point where the transaction commits its changes to the database. However, the lock point can also be earlier or later than the commit point, depending on the variant of the two-phase locking protocol.

Here is an example of a transaction that follows the two-phase locking protocol:

-- Transaction T1
BEGIN TRANSACTION;
-- Growing phase
SELECT A FROM R WHERE B = 10; -- Acquire shared lock on R
UPDATE R SET A = A + 1 WHERE B = 10; -- Convert shared lock to exclusive lock on R
SELECT C FROM S WHERE D = 20; -- Acquire shared lock on S
-- Lock point and commit point
COMMIT; -- Release all locks
-- Shrinking phase (empty)
END TRANSACTION;

As you can see, the transaction acquires and converts locks in the growing phase, and releases all locks at the commit point, which is also the lock point. The transaction does not acquire or release any lock in the shrinking phase.

There are different variants of the two-phase locking protocol, such as basic, strict, and rigorous, that impose different restrictions on the lock point and the release of locks. In the next sections, you will learn about these variants and their advantages and disadvantages.

3.1. Basic Two-Phase Locking

Basic two-phase locking (2PL) is a protocol that transactions must follow to acquire and release locks in lock-based concurrency control. The protocol consists of two phases: the growing phase and the shrinking phase. In the growing phase, a transaction can acquire locks on data items, but cannot release any lock. In the shrinking phase, a transaction can release locks, but cannot acquire any new lock. The transaction must switch from the growing phase to the shrinking phase after it acquires all the locks it needs.

The main advantage of basic 2PL is that it ensures serializability, which means that the concurrent execution of transactions produces the same result as if they were executed one after another. This is because any two transactions that access the same data item must have overlapping lock intervals, and therefore, one of them must wait for the other to finish. The order of the transactions is determined by the order of their lock requests.

However, basic 2PL also has some disadvantages, such as:

• It may cause long waiting times and low concurrency, as transactions may hold locks for a long time and block other transactions from accessing the same data items.
• It may cause cascading aborts, which means that if a transaction aborts and releases its locks, it may cause other transactions that have read the same data items to abort as well. This may lead to a chain of aborts and wasted work.
• It does not prevent deadlocks, which are situations where two or more transactions are waiting for each other to release locks, and none of them can proceed. Deadlocks may cause the system to hang and require intervention to resolve.

In the next sections, you will learn about some variants of 2PL that try to overcome these disadvantages, such as strict 2PL and rigorous 2PL.

3.2. Strict Two-Phase Locking

Strict two-phase locking (S2PL) is a variant of 2PL that imposes an additional restriction on the transactions. In S2PL, a transaction must hold all its exclusive locks until it commits or aborts. This means that a transaction cannot release any exclusive lock in the shrinking phase, and must release them all at once at the end of the transaction. An exclusive lock is a lock that allows a transaction to write a data item, and prevents any other transaction from reading or writing the same data item.

The main advantage of S2PL is that it prevents cascading aborts, which are a major drawback of basic 2PL. Cascading aborts occur when a transaction aborts and releases its locks, causing other transactions that have read the same data items to abort as well. This may lead to a chain of aborts and wasted work. By holding the exclusive locks until the end, S2PL ensures that no transaction can read a data item that is written by an uncommitted transaction. Therefore, if a transaction aborts, it does not affect any other transaction.

However, S2PL also has some disadvantages, such as:

• It may cause longer waiting times and lower concurrency, as transactions may hold exclusive locks for a long time and block other transactions from accessing the same data items.
• It does not prevent deadlocks, which are situations where two or more transactions are waiting for each other to release locks, and none of them can proceed. Deadlocks may cause the system to hang and require intervention to resolve.

In the next section, you will learn about another variant of 2PL that tries to overcome these disadvantages, such as rigorous 2PL.

3.3. Rigorous Two-Phase Locking

Rigorous two-phase locking (R2PL) is another variant of 2PL that imposes a stricter restriction on the transactions. In R2PL, a transaction must hold all its locks, both shared and exclusive, until it commits or aborts. This means that a transaction cannot release any lock in the shrinking phase, and must release them all at once at the end of the transaction. A shared lock is a lock that allows a transaction to read a data item, and allows other transactions to read the same data item, but prevents any transaction from writing it.

The main advantage of R2PL is that it inherits the benefits of S2PL, such as preventing cascading aborts, and also improves the performance and concurrency of the system. By holding the shared locks until the end, R2PL ensures that no transaction can write a data item that is read by an uncommitted transaction. Therefore, if a transaction aborts, it does not affect any other transaction. Moreover, by releasing all the locks at once, R2PL reduces the lock overhead and the number of lock requests, which may improve the throughput and response time of the system.

However, R2PL also has some disadvantages, such as:

• It may cause longer waiting times and lower concurrency, as transactions may hold locks for a long time and block other transactions from accessing the same data items.
• It does not prevent deadlocks, which are situations where two or more transactions are waiting for each other to release locks, and none of them can proceed. Deadlocks may cause the system to hang and require intervention to resolve.

In the next section, you will learn about some techniques to prevent and detect deadlocks, which are a common problem in lock-based concurrency control.

4. Deadlock Prevention and Detection

Deadlock is a situation where two or more transactions are waiting for each other to release locks, and none of them can proceed. Deadlock is a serious problem in lock-based concurrency control, as it may cause the system to hang and require intervention to resolve. Therefore, it is important to have some techniques to prevent and detect deadlocks, and to resolve them if they occur.

Deadlock prevention techniques are methods that prevent deadlocks from occurring in the first place, by imposing some restrictions on the transactions. For example, some deadlock prevention techniques are:

• Assigning a unique priority to each transaction, and allowing a transaction to lock a data item only if it has a higher priority than any other transaction that holds or requests a lock on the same data item.
• Requiring a transaction to request all the locks it needs before it starts its execution, and granting the locks only if they are available.
• Ordering the data items according to some criterion, and requiring a transaction to lock the data items in that order.

Deadlock prevention techniques can guarantee that deadlocks will never occur, but they may also reduce the concurrency and performance of the system, as they may force some transactions to wait or abort unnecessarily.

Deadlock detection techniques are methods that detect deadlocks after they occur, by monitoring the state of the system and the lock requests. For example, some deadlock detection techniques are:

• Using a timeout mechanism, and aborting a transaction if it waits for a lock for more than a specified time.
• Using a wait-for graph, which is a graph that shows the dependencies among the transactions based on their lock requests. A cycle in the wait-for graph indicates a deadlock.
• Using a deadlock detection algorithm, which periodically checks the state of the system and the lock requests, and identifies the transactions that are involved in a deadlock.

Deadlock detection techniques can allow more concurrency and flexibility in the system, but they may also incur some overhead and delay in resolving the deadlocks. Once a deadlock is detected, the system must choose a victim transaction to abort and release its locks, and restart it later.

In the next section, you will learn how to apply these techniques to your own database applications and improve their efficiency and reliability.

4.1. Deadlock Prevention Techniques

Deadlock is a situation where two or more transactions are waiting for each other to release locks, and none of them can proceed. Deadlock can cause the system to waste resources and reduce performance. Therefore, it is important to prevent deadlock from occurring in the first place. There are several techniques to prevent deadlock, such as:

• Timeout: A timeout is a limit on how long a transaction can wait for a lock. If the timeout expires, the transaction aborts and releases all its locks. This way, deadlock can be avoided by preventing transactions from waiting indefinitely. However, timeout can also cause unnecessary aborts and retries, which can affect the system’s throughput and efficiency.
• Wound-wait: Wound-wait is a policy that compares the timestamps of transactions when they request locks. If an older transaction requests a lock held by a younger transaction, the younger transaction is aborted and the lock is granted to the older transaction. This is called wounding. If a younger transaction requests a lock held by an older transaction, the younger transaction waits until the lock is released. This is called waiting. Wound-wait prevents deadlock by ensuring that transactions are executed in timestamp order. However, wound-wait can also cause cascading aborts, where one transaction’s abort causes other transactions to abort as well.
• Wait-die: Wait-die is a policy that is similar to wound-wait, but with a different action when an older transaction requests a lock held by a younger transaction. Instead of aborting the younger transaction, the older transaction waits until the lock is released. This is called dying. Wait-die prevents deadlock by ensuring that transactions do not wait for younger transactions. However, wait-die can also cause starvation, where older transactions may never get the locks they need.
• No-wait: No-wait is a policy that does not allow any transaction to wait for a lock. If a transaction requests a lock that is already held by another transaction, the transaction aborts and releases all its locks. This way, deadlock can be avoided by preventing cycles of waiting transactions. However, no-wait can also cause a high abort rate and low concurrency, as transactions may fail to acquire the locks they need.

These are some of the common deadlock prevention techniques that can be used in lock-based concurrency control. In the next section, you will learn about another technique to deal with deadlock, which is deadlock detection and resolution.

4.2. Deadlock Detection and Resolution

Deadlock detection and resolution is another technique to deal with deadlock, which is based on the idea of detecting and resolving deadlock after it occurs. This technique does not impose any restrictions on the transactions, and allows them to acquire and release locks as they wish. However, it requires the system to periodically check for the existence of deadlock, and to take some actions to break the deadlock when it is detected.

There are two main components of deadlock detection and resolution: the deadlock detection algorithm and the deadlock resolution policy. The deadlock detection algorithm is a procedure that identifies the transactions that are involved in a deadlock. The most common way to implement the deadlock detection algorithm is to use a wait-for graph, which is a directed graph that represents the waiting relationships between transactions. A node in the graph represents a transaction, and an edge from node A to node B represents that transaction A is waiting for transaction B to release a lock. A cycle in the graph indicates that there is a deadlock among the transactions in the cycle.

The deadlock resolution policy is a strategy that decides which transaction(s) to abort in order to break the deadlock. There are different criteria that can be used to select the victim transaction(s), such as:

• Rollback cost: The rollback cost is the amount of work that a transaction has done so far, and that will be lost if the transaction is aborted. The system can choose to abort the transaction with the lowest or the highest rollback cost, depending on the trade-off between performance and fairness.
• Wait time: The wait time is the duration that a transaction has been waiting for a lock. The system can choose to abort the transaction with the shortest or the longest wait time, depending on the trade-off between efficiency and starvation.
• Priority: The priority is a value that indicates the importance or urgency of a transaction. The system can choose to abort the transaction with the lowest or the highest priority, depending on the trade-off between quality of service and resource utilization.

These are some of the common deadlock detection and resolution techniques that can be used in lock-based concurrency control. In the next section, you will learn how to conclude your tutorial and provide some references for further reading.

5. Conclusion

In this tutorial, you have learned about lock-based concurrency control, which is a technique that uses locks to prevent concurrent transactions from accessing the same data item at the same time. You have learned about the basic concepts and principles of lock-based concurrency control, such as lock types and modes, lock compatibility matrix, and lock granularity. You have also learned about the two-phase locking protocol, which is a set of rules that transactions must follow to acquire and release locks. You have explored the different variants of two-phase locking, such as basic, strict, and rigorous, and their advantages and disadvantages. Finally, you have learned about deadlock prevention and detection, which are techniques to deal with the problem of deadlock. You have learned how to prevent deadlock by using timeout, wound-wait, wait-die, and no-wait policies, and how to detect and resolve deadlock by using a wait-for graph and a deadlock resolution policy.

By completing this tutorial, you have gained a solid understanding of lock-based concurrency control, two-phase locking, and deadlock prevention and detection. You have also acquired the skills and knowledge to apply these concepts to your own database applications and improve their performance and reliability.

If you want to learn more about lock-based concurrency control and related topics, you can check out the following resources:

• Lock Based Protocols in DBMS: A comprehensive article that explains the lock-based protocols and their types, with examples and diagrams.
• Two Phase Locking Protocol | DBMS: A video tutorial that covers the two-phase locking protocol and its variants, with examples and animations.
• DBMS – Concurrency Control: A chapter from a DBMS course that covers the concurrency control techniques, such as timestamp-based, validation-based, and lock-based, with examples and exercises.

We hope you enjoyed this tutorial and found it useful. Thank you for reading and happy learning!