Computer Study

DBMS – Concurrency Control ,Prevention or Avoidance

Deadlock is a situation in a database management system (DBMS) where two or more transactions are waiting for resources held by each other, resulting in a state where no transaction can proceed. This can result in a complete system halt and can have severe consequences for the system’s performance and reliability.

Deadlock occurs when transactions acquire locks on resources in an inconsistent order. For example, suppose Transaction A has locked resource X and is waiting to acquire resource Y, while Transaction B has locked resource Y and is waiting to acquire resource X. In this situation, neither transaction can proceed, and the system is deadlocked.

DBMS uses several techniques to prevent deadlock, such as:

  1. Lock timeouts: DBMS can specify a timeout value for each lock. If a transaction holds a lock for longer than the timeout value, the lock is automatically released, allowing other transactions to access the resource.
  2. Deadlock detection: DBMS can periodically check for deadlocks by maintaining a wait-for graph that tracks the dependencies between transactions. If a cycle is detected in the graph, then a deadlock is present, and the system can take corrective action.
  3. Deadlock prevention: DBMS can prevent deadlocks from occurring by enforcing strict lock ordering. This means that transactions must acquire locks in a consistent order to avoid conflicts.
  4. Deadlock avoidance: DBMS can use algorithms to predict which resources transactions will need in the future, and allocate resources accordingly to avoid potential deadlocks.

While these techniques can prevent and resolve deadlocks, they also have drawbacks. For example, lock timeouts can lead to inconsistent data if a transaction holds a lock for too long, and deadlock detection can be resource-intensive and affect system performance.

In conclusion, deadlock is a significant issue in DBMS that can have severe consequences for system performance and reliability. DBMS uses various techniques to prevent and resolve deadlocks, such as lock timeouts, deadlock detection, deadlock prevention, and deadlock avoidance. It is essential to understand these techniques and implement them carefully to ensure data consistency and system performance.

Deadlock Prevention

Deadlock prevention is a technique used in database management systems (DBMS) to avoid the occurrence of deadlock by ensuring that transactions acquire resources in a consistent and predictable manner. Prevention is preferred over detection and resolution since it avoids the overhead of detecting and resolving deadlocks, which can have a negative impact on system performance.

There are several methods that DBMS can use to prevent deadlock:

  1. Lock Ordering: In this method, all transactions are required to acquire locks on resources in a consistent order. For example, if Transaction A has locked resource X and requires resource Y, and Transaction B has locked resource Y and requires resource X, then the DBMS can ensure that all transactions always acquire locks in the same order, such as X followed by Y. This ensures that the system will never enter a deadlock state.
  2. Two-Phase Locking: In two-phase locking, all transactions acquire all the locks they need at the beginning of the transaction, and no new locks are acquired after any locks have been released. This prevents the occurrence of circular dependencies that lead to deadlock.
  3. Timeouts: In this method, each lock has a timeout value, and if a transaction holds a lock for longer than the timeout, the lock is automatically released. This ensures that no transaction holds a lock indefinitely, preventing the occurrence of deadlock.
  4. Wait-Die and Wound-Wait: These are two different techniques that handle conflicting lock requests. In wait-die, a transaction with a lower timestamp is forced to wait when trying to acquire a lock held by a transaction with a higher timestamp. In wound-wait, a transaction with a higher timestamp is forced to abort when trying to acquire a lock held by a transaction with a lower timestamp.
  5. Resource Allocation Graphs: A resource allocation graph is a graphical representation of resource allocation between transactions. In this method, a transaction can only acquire a resource if it does not lead to the formation of a cycle in the graph. This ensures that the system will never enter a deadlock state.

deadlock prevention is a crucial technique used in DBMS to avoid the occurrence of deadlock. It is essential to understand the different methods available and choose the one that best suits the requirements of the system. Prevention is preferred over detection and resolution since it avoids the overhead of detecting and resolving deadlocks and can have a positive impact on system performance.

Lock Ordering with example

Lock Ordering is another technique used for preventing deadlocks in database management systems. It is based on the concept of assigning a unique order to the locks that transactions need to acquire. In this way, transactions are forced to request locks in a specific order, thereby preventing circular dependencies that can lead to deadlocks.

Here’s an example of how the Lock Ordering technique can prevent deadlocks in a DBMS:

Suppose we have two transactions, T1 and T2, that need to access two resources, R1 and R2, respectively. Both transactions need to acquire locks on both resources to complete their tasks. If T1 requests a lock on R2 while T2 already holds a lock on R2, and T2 requests a lock on R1 while T1 already holds a lock on R1, a deadlock can occur.

To prevent a deadlock, the DBMS assigns a unique order to the locks that transactions need to acquire. In this case, the DBMS can assign a lock order of R1, R2 to both transactions. This means that T1 must acquire a lock on R1 before requesting a lock on R2, and T2 must acquire a lock on R1 before requesting a lock on R2.

Suppose T1 requests a lock on R2 while T2 already holds a lock on R2. Since T1 has not yet acquired a lock on R1, it must wait until T2 releases its lock on R2 before it can request a lock on R2. Once T2 releases its lock on R2, T1 can acquire a lock on R2 and proceed to acquire a lock on R1.

In this way, the Lock Ordering technique prevents a deadlock from occurring by forcing transactions to request locks in a specific order. By assigning a unique order to the locks that transactions need to acquire, the DBMS can prevent circular dependencies and ensure that transactions can acquire their required locks in a way that does not lead to deadlocks.

Two-Phase Locking with example

Two-Phase Locking (2PL) is a popular technique used in concurrency control in database management systems. It consists of two phases, the growing phase and the shrinking phase, and ensures that transactions acquire all the necessary locks before proceeding and release all the locks before committing.

Here’s an example of how the Two-Phase Locking technique can be applied in a DBMS:

Suppose we have two transactions, T1 and T2, that need to access two resources, R1 and R2, respectively. Both transactions need to acquire locks on both resources to complete their tasks.

During the growing phase, each transaction acquires locks on the required resources. For example, suppose T1 requests a lock on R1 first and then requests a lock on R2. At this point, T1 holds both locks and can proceed with its task. Similarly, suppose T2 requests a lock on R2 first and then requests a lock on R1. At this point, T2 holds both locks and can proceed with its task.

During the shrinking phase, each transaction releases the locks it holds. For example, suppose T1 releases the lock on R2 before releasing the lock on R1. At this point, T1 has released one lock and still holds another lock. Similarly, suppose T2 releases the lock on R1 before releasing the lock on R2. At this point, T2 has released one lock and still holds another lock.

If T1 attempts to request a lock on R2 after releasing the lock on R1, it will be blocked since it violates the Two-Phase Locking protocol. Similarly, if T2 attempts to request a lock on R1 after releasing the lock on R2, it will also be blocked. Both transactions must release all the locks they hold before committing, ensuring that no locks are held after committing.

In this way, the Two-Phase Locking technique prevents conflicts between transactions and ensures that each transaction acquires all the necessary locks before proceeding and releases all the locks before committing. By enforcing this protocol, the DBMS can prevent anomalies such as dirty reads, non-repeatable reads, and phantom reads, and maintain the consistency and integrity of the data.

Wait-Die Scheme

Wait-Die is a technique used in deadlock prevention in database management systems. It is a method of handling conflicting lock requests where a transaction with a lower timestamp is forced to wait when trying to acquire a lock held by a transaction with a higher timestamp.

In Wait-Die, when a transaction requests a lock on a resource that is already held by another transaction, the DBMS checks the timestamps of the two transactions. If the requesting transaction has a higher timestamp than the transaction holding the lock, it is allowed to proceed and acquire the lock. However, if the requesting transaction has a lower timestamp than the transaction holding the lock, it is forced to wait until the other transaction releases the lock.

On the other hand, if a transaction holding a lock requests a new lock that conflicts with the lock held by another transaction, the DBMS checks the timestamps of the two transactions. If the requesting transaction has a lower timestamp than the transaction holding the conflicting lock, the transaction holding the conflicting lock is forced to wait until the requesting transaction releases the lock. However, if the requesting transaction has a higher timestamp than the transaction holding the conflicting lock, the transaction holding the conflicting lock is aborted, and the requesting transaction is allowed to proceed.

Wait-Die is a conservative approach to deadlock prevention because it never allows a transaction with a lower timestamp to wait for a lock held by a transaction with a higher timestamp. It ensures that the system will never enter a deadlock state but may lead to some transactions being forced to wait unnecessarily, which can negatively impact system performance.

In summary, the Wait-Die scheme is an effective method for handling conflicting lock requests and preventing deadlocks in DBMS. It ensures that the system will never enter a deadlock state by forcing transactions with lower timestamps to wait when trying to acquire a lock held by a transaction with a higher timestamp. However, it may lead to reduced concurrency and some transactions being forced to wait unnecessarily, which can impact system performance.

Here is an example of how the Wait-Die scheme can prevent deadlocks in a database management system:

Consider two transactions, T1 and T2, that need to access two resources, R1 and R2, respectively. Suppose T1 requests a lock on R2 while T2 already holds a lock on R2, and T2 requests a lock on R1 while T1 already holds a lock on R1. This situation can lead to a deadlock if both transactions continue to hold their current locks and wait for the other transaction to release its lock.

To prevent a deadlock, the DBMS uses the Wait-Die scheme. The DBMS checks the timestamps of the two transactions and allows the transaction with the higher timestamp to proceed while forcing the transaction with the lower timestamp to wait. Suppose T1 has a lower timestamp than T2, which means T1 must wait for T2 to release its lock on R2.

However, suppose T2 also has a lower timestamp than T1 when it requests a lock on R1, which means it cannot wait for T1 to release its lock. In this case, the Wait-Die scheme forces T2 to abort and releases its lock on R2. This allows T1 to proceed and acquire the lock on R2 and then acquire the lock on R1.

In this way, the Wait-Die scheme prevents a deadlock from occurring by forcing transactions with lower timestamps to wait when trying to acquire locks held by transactions with higher timestamps. If a transaction cannot wait and needs to acquire a conflicting lock held by a transaction with a lower timestamp, the lower timestamp transaction is forced to abort to release its locks and allow the higher timestamp transaction to proceed.

Wound-Wait Scheme with example

The Wound-Wait scheme is another technique used in deadlock prevention in database management systems. It is similar to the Wait-Die scheme, but it allows transactions with lower timestamps to force transactions with higher timestamps to abort and release their locks.

Here’s an example of how the Wound-Wait scheme can prevent deadlocks in a DBMS:

Suppose we have two transactions, T1 and T2, that need to access two resources, R1 and R2, respectively. T1 requests a lock on R2 while T2 already holds a lock on R2, and T2 requests a lock on R1 while T1 already holds a lock on R1. If both transactions continue to hold their current locks and wait for the other transaction to release its lock, a deadlock can occur.

To prevent a deadlock, the DBMS uses the Wound-Wait scheme. The DBMS checks the timestamps of the two transactions and allows the transaction with the higher timestamp to proceed while forcing the transaction with the lower timestamp to wait. Suppose T1 has a lower timestamp than T2, which means T1 must wait for T2 to release its lock on R2.

However, suppose T1 later requests a lock on R2 while T2 still holds a lock on R2. Since T1 has a lower timestamp than T2, it is allowed to proceed and force T2 to abort and release its lock on R2. T1 can then acquire the lock on R2 and proceed to acquire the lock on R1.

In this way, the Wound-Wait scheme prevents a deadlock from occurring by allowing transactions with lower timestamps to force transactions with higher timestamps to abort and release their locks. If a transaction cannot wait and needs to acquire a conflicting lock held by a transaction with a higher timestamp, the higher timestamp transaction is forced to abort to release its locks and allow the lower timestamp transaction to proceed. However, this approach can lead to cascading rollbacks, where one transaction’s abort causes other transactions to abort as well, which can negatively impact system performance.

Deadlock Avoidance

Deadlock Avoidance is a technique used in database management systems to prevent deadlocks from occurring by ensuring that transactions request locks in a way that avoids circular dependencies. It is a proactive approach that allows the system to anticipate potential deadlocks and avoid them before they occur.

One way to achieve Deadlock Avoidance is to use a resource allocation graph. The resource allocation graph is a directed graph that represents the current state of the system, including the transactions and the resources they hold or request. Each transaction is represented by a node, and each resource is represented by an edge.

Here’s an example of how Deadlock Avoidance using resource allocation graph works in a DBMS:

Suppose we have two transactions, T1 and T2, that need to access two resources, R1 and R2, respectively. Both transactions need to acquire locks on both resources to complete their tasks.

Initially, both transactions request a lock on R1, and both transactions are granted the lock. At this point, the resource allocation graph looks like this:

T1 – R1
|
V
T2 – R1

Now, T1 requests a lock on R2. Before granting the lock, the DBMS checks the resource allocation graph to determine if granting the lock would cause a cycle. In this case, granting the lock to T1 would cause a cycle since T2 already holds a lock on R1, and T1 cannot proceed until it acquires a lock on R2. To avoid the deadlock, the DBMS blocks T1 and waits for T2 to release the lock on R1 before granting the lock to T1. At this point, the resource allocation graph looks like this:

T1 —- R2
| ^
V |
T2 – R1 -|

Once T2 releases the lock on R1, the DBMS grants the lock to T1, and T1 can proceed with its task.

In this way, Deadlock Avoidance using resource allocation graph allows the DBMS to anticipate potential deadlocks and avoid them before they occur. By analyzing the resource allocation graph and preventing cycles, the DBMS can ensure that transactions acquire locks in a way that avoids circular dependencies and prevents deadlocks from occurring.

Wait-for Graph

Wait-for Graph is another technique used in database management systems to detect and resolve deadlocks. It is a directed graph that represents the waiting relationships among transactions.

In a wait-for graph, each transaction is represented by a node, and an edge is drawn from transaction Ti to Tj if Ti is waiting for a resource held by Tj. The wait-for graph can be constructed dynamically as transactions request and release resources.

wait_for_graph

Here’s an example of how Wait-for Graph works in a DBMS:

Suppose we have three transactions, T1, T2, and T3, that need to access three resources, R1, R2, and R3, respectively. Initially, T1 acquires a lock on R1, T2 acquires a lock on R2, and T3 requests a lock on R1, but it is blocked by T1. At this point, the wait-for graph looks like this:

T3 —> T1

T3 is waiting for a lock on R1 held by T1. Now, suppose T1 requests a lock on R2, but it is blocked by T2. At this point, the wait-for graph looks like this:

 T3 —> T1
^
|
T2

T1 is waiting for a lock on R2 held by T2, and T3 is waiting for a lock on R1 held by T1. This waiting relationship creates a cycle in the wait-for graph, indicating that a deadlock has occurred.

To resolve the deadlock, the DBMS needs to choose one of the transactions to abort. In this case, the DBMS can choose to abort either T1 or T2. Suppose the DBMS chooses to abort T1. T1 releases its lock on R1, and T3 can acquire the lock on R1 and complete its task. At this point, the wait-for graph looks like this:

T3 —> T2

T3 is no longer waiting for any resources, and T2 holds the lock on R2. The deadlock has been resolved.

In this way, Wait-for Graph allows the DBMS to detect and resolve deadlocks by representing the waiting relationships among transactions in a directed graph. By identifying cycles in the wait-for graph, the DBMS can detect deadlocks and take appropriate action to resolve them.

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