How to Avoid and Resolve Deadlocks in Transactions

Learn how to avoid and resolve deadlocks in transactions using locking and timeout strategies. This blog explains the concept of deadlocks, how they occur, and how to detect and resolve them using various algorithms and techniques.

Table of Contents

1. Introduction

In this blog, you will learn how to avoid and resolve deadlocks in transactions. Deadlocks are a common problem that can occur when multiple transactions try to access and modify the same data concurrently. Deadlocks can cause performance degradation, data inconsistency, and system failure.

To understand how deadlocks can occur, you need to know the concept of locking. Locking is a mechanism that prevents concurrent transactions from accessing or modifying the same data at the same time. Locking ensures data integrity and consistency, but it can also lead to deadlocks if not managed properly.

To detect and resolve deadlocks, you need to use deadlock detection algorithms and deadlock resolution strategies. Deadlock detection algorithms are methods that identify the transactions that are involved in a deadlock and report them to the system. Deadlock resolution strategies are actions that the system can take to break the deadlock and allow the transactions to proceed.

To prevent deadlocks, you need to use locking protocols and levels and timeout and deadlock avoidance techniques. Locking protocols and levels are rules that determine when and how transactions can acquire and release locks on data. Timeout and deadlock avoidance techniques are methods that prevent transactions from waiting indefinitely for locks or entering into a deadlock situation.

By the end of this blog, you will be able to demonstrate how deadlocks can occur in transactions and how to prevent and resolve them using locking and timeout strategies. You will also be able to apply these concepts and techniques to your own database applications and improve their performance and reliability.

2. What are Deadlocks and How Do They Occur?

A deadlock is a situation where two or more transactions are waiting for each other to release a lock on a data item, and none of them can proceed. Deadlocks can cause serious problems in a database system, such as:

Performance degradation: Deadlocked transactions waste system resources and time, as they cannot complete their work and block other transactions from accessing the data.
Data inconsistency: Deadlocked transactions may leave the data in an inconsistent state, as they may have partially updated some data items and not others.
System failure: Deadlocked transactions may cause the system to crash or hang, as they may consume all the available memory or CPU resources.

But how do deadlocks occur in the first place? To answer this question, you need to understand the concept of locking.

Locking is a mechanism that prevents concurrent transactions from accessing or modifying the same data item at the same time. Locking ensures data integrity and consistency, as it prevents conflicts and anomalies that may arise from concurrent updates. For example, locking prevents the lost update problem, where one transaction overwrites the changes made by another transaction without knowing it.

There are two types of locks: shared locks and exclusive locks. A shared lock allows a transaction to read a data item, but not to modify it. An exclusive lock allows a transaction to both read and modify a data item, but not to share it with other transactions. A transaction can acquire a lock on a data item before accessing it, and release it after finishing its work. A transaction can also upgrade a shared lock to an exclusive lock, or downgrade an exclusive lock to a shared lock, if needed.

However, locking can also lead to deadlocks, if not managed properly. To understand how locking can cause deadlocks, you need to know the conditions for deadlock formation.

2.1. The Concept of Locking

To illustrate the concept of locking, let’s look at an example. Suppose you have a bank account with a balance of $1000, and you want to withdraw $500. At the same time, another transaction wants to deposit $200 into your account. Without locking, the following scenario could happen:

# Transaction 1: Withdraw $500
balance = get_balance(account) # balance = 1000
balance = balance - 500 # balance = 500
# Transaction 2: Deposit $200
balance = get_balance(account) # balance = 1000
balance = balance + 200 # balance = 1200
# Transaction 1: Update balance
update_balance(account, balance) # balance = 500
# Transaction 2: Update balance
update_balance(account, balance) # balance = 1200

In this scenario, the second transaction overwrites the changes made by the first transaction, and the final balance is $1200. This is incorrect, as the expected balance should be $700. This is an example of the lost update problem, where one transaction loses the effect of its update due to another transaction.

With locking, the scenario would be different. The first transaction would acquire an exclusive lock on the account before reading and updating the balance, and release it after finishing its work. The second transaction would have to wait until the lock is released before accessing the account. The following scenario would happen:

# Transaction 1: Withdraw $500
lock(account, exclusive) # acquire exclusive lock
balance = get_balance(account) # balance = 1000
balance = balance - 500 # balance = 500
update_balance(account, balance) # balance = 500
unlock(account) # release exclusive lock
# Transaction 2: Deposit $200
lock(account, exclusive) # acquire exclusive lock
balance = get_balance(account) # balance = 500
balance = balance + 200 # balance = 700
update_balance(account, balance) # balance = 700
unlock(account) # release exclusive lock

In this scenario, the first transaction completes its work before the second transaction starts, and the final balance is $700. This is correct, as the expected balance is $700. This is an example of how locking can prevent the lost update problem, by ensuring that only one transaction can access and modify the data item at a time.

As you can see, locking is a useful mechanism that can ensure data integrity and consistency in concurrent transactions. However, locking can also cause deadlocks, if not managed properly. To understand how locking can cause deadlocks, you need to know the conditions for deadlock formation.

2.2. The Conditions for Deadlock Formation

Deadlocks can occur when two or more transactions are waiting for each other to release a lock on a data item, and none of them can proceed. But what are the necessary conditions for a deadlock to occur? According to the Coffman conditions, there are four conditions that must hold simultaneously for a deadlock to occur:

Mutual exclusion: Each data item can be locked by only one transaction at a time, and no other transaction can access or modify it until the lock is released.
Hold and wait: A transaction can hold a lock on a data item and wait for another lock on a different data item, without releasing any of its locks.
No preemption: A transaction cannot forcibly take away a lock from another transaction, and a lock can only be released voluntarily by the transaction that holds it.
Circular wait: There is a circular chain of transactions, where each transaction is waiting for a lock held by the next transaction in the chain.

To illustrate these conditions, let’s look at an example. Suppose you have two transactions, T1 and T2, and two data items, A and B. The following scenario could happen:

# Transaction T1
lock(A, exclusive) # acquire exclusive lock on A
read(A) # read A
lock(B, exclusive) # wait for exclusive lock on B
write(B) # write B
unlock(B) # release exclusive lock on B
unlock(A) # release exclusive lock on A
# Transaction T2
lock(B, exclusive) # acquire exclusive lock on B
read(B) # read B
lock(A, exclusive) # wait for exclusive lock on A
write(A) # write A
unlock(A) # release exclusive lock on A
unlock(B) # release exclusive lock on B

In this scenario, a deadlock occurs, as both transactions are waiting for each other to release a lock, and none of them can proceed. The four conditions for deadlock formation are met:

Mutual exclusion: A and B can be locked by only one transaction at a time, and no other transaction can access or modify them until the lock is released.
Hold and wait: T1 holds a lock on A and waits for a lock on B, while T2 holds a lock on B and waits for a lock on A, without releasing any of their locks.
No preemption: T1 cannot forcibly take away the lock on B from T2, and T2 cannot forcibly take away the lock on A from T1, and a lock can only be released voluntarily by the transaction that holds it.
Circular wait: There is a circular chain of transactions, where T1 is waiting for a lock held by T2, and T2 is waiting for a lock held by T1.

As you can see, deadlocks can occur when the four conditions for deadlock formation are met. However, not all deadlocks are easy to detect, as they may involve more than two transactions and more than two data items. To detect and resolve deadlocks, you need to use deadlock detection algorithms and deadlock resolution strategies.

3. How to Detect and Resolve Deadlocks

Deadlocks can occur when two or more transactions are waiting for each other to release a lock on a data item, and none of them can proceed. To avoid the negative consequences of deadlocks, such as performance degradation, data inconsistency, and system failure, you need to know how to detect and resolve them. In this section, you will learn about the two main approaches to deal with deadlocks: deadlock detection and deadlock resolution.

Deadlock detection is a method that identifies the transactions that are involved in a deadlock and reports them to the system. Deadlock detection can be done by using various algorithms, such as the banker’s algorithm, the wait-for graph, or the resource allocation graph. These algorithms use different data structures and techniques to represent the state of the transactions and the locks, and to check for the existence of a cycle or a deadlock situation.

Deadlock resolution is a method that breaks the deadlock and allows the transactions to proceed. Deadlock resolution can be done by using various strategies, such as aborting, preempting, or restarting one or more transactions involved in the deadlock. These strategies have different trade-offs and implications, such as the amount of work lost, the impact on data consistency, and the possibility of starvation.

By using deadlock detection algorithms and deadlock resolution strategies, you can detect and resolve deadlocks in transactions and improve the performance and reliability of your database system. However, detecting and resolving deadlocks can also be costly and complex, as they require additional system resources and overhead. To prevent deadlocks from occurring in the first place, you need to use locking protocols and levels and timeout and deadlock avoidance techniques.

3.1. Deadlock Detection Algorithms

Deadlock detection algorithms are methods that identify the transactions that are involved in a deadlock and report them to the system. Deadlock detection algorithms can be classified into two categories: static and dynamic.

Static deadlock detection algorithms are methods that analyze the transactions and the data items before they start executing, and determine if there is a possibility of a deadlock. Static deadlock detection algorithms use information such as the number of transactions, the number of data items, the types of locks, and the order of lock requests. Static deadlock detection algorithms can prevent deadlocks from occurring, but they can also be conservative and pessimistic, as they may reject some transactions that are not actually involved in a deadlock.

Dynamic deadlock detection algorithms are methods that monitor the transactions and the data items during their execution, and detect if there is an actual deadlock. Dynamic deadlock detection algorithms use information such as the current state of the transactions, the current state of the locks, and the wait-for relationships among the transactions. Dynamic deadlock detection algorithms can detect deadlocks as they occur, but they can also be costly and complex, as they require additional system resources and overhead.

There are various algorithms that can be used for static or dynamic deadlock detection, such as the banker’s algorithm, the wait-for graph, or the resource allocation graph. These algorithms use different data structures and techniques to represent the state of the transactions and the locks, and to check for the existence of a cycle or a deadlock situation.

In the next section, you will learn about the different strategies that can be used to resolve deadlocks, once they are detected by the deadlock detection algorithms.

3.2. Deadlock Resolution Strategies

Deadlock resolution strategies are methods that break the deadlock and allow the transactions to proceed. Deadlock resolution strategies can be classified into three categories: aborting, preempting, and restarting.

Aborting is a method that terminates one or more transactions involved in the deadlock and releases their locks. Aborting can be done by using various criteria, such as the priority, the age, the progress, or the cost of the transactions. Aborting can resolve the deadlock quickly, but it can also cause work loss, data inconsistency, and starvation.

Preempting is a method that temporarily takes away a lock from a transaction and gives it to another transaction that is waiting for it. Preempting can be done by using various techniques, such as wait-die, wound-wait, or timestamp ordering. Preempting can resolve the deadlock without aborting any transactions, but it can also cause cascading aborts, data inconsistency, and overhead.

Restarting is a method that rolls back a transaction to a previous state and restarts it with a different execution order. Restarting can be done by using various mechanisms, such as checkpoints, logs, or undo/redo operations. Restarting can resolve the deadlock without losing any work, but it can also cause performance degradation, overhead, and starvation.

By using deadlock resolution strategies, you can break the deadlock and allow the transactions to proceed. However, deadlock resolution strategies can also have negative consequences, such as work loss, data inconsistency, performance degradation, overhead, and starvation. To prevent these consequences, you need to use deadlock prevention techniques, such as locking protocols and levels and timeout and deadlock avoidance techniques.

4. How to Prevent Deadlocks

Deadlocks can occur when two or more transactions are waiting for each other to release a lock on a data item, and none of them can proceed. To avoid the negative consequences of deadlocks, such as performance degradation, data inconsistency, and system failure, you need to know how to prevent them. In this section, you will learn about the two main techniques to prevent deadlocks: locking protocols and levels and timeout and deadlock avoidance techniques.

Locking protocols and levels are rules that determine when and how transactions can acquire and release locks on data items. Locking protocols and levels can prevent deadlocks by ensuring that the four conditions for deadlock formation are not met. For example, locking protocols and levels can prevent deadlocks by:

Enforcing a strict order of lock requests, such as the two-phase locking protocol, which requires a transaction to acquire all its locks before releasing any of them.
Using different levels of locks, such as the granularity levels, which allow a transaction to lock a data item at different levels of detail, such as a record, a page, or a table.
Using different modes of locks, such as the multiple granularity locking protocol, which allows a transaction to lock a data item in different modes, such as shared, exclusive, or intention.

Timeout and deadlock avoidance techniques are methods that prevent transactions from waiting indefinitely for locks or entering into a deadlock situation. Timeout and deadlock avoidance techniques can prevent deadlocks by:

Setting a time limit for a transaction to wait for a lock, such as the timeout technique, which aborts a transaction if it does not acquire a lock within a specified time.
Using a timestamp for each transaction, such as the timestamp-based concurrency control technique, which assigns a unique timestamp to each transaction and uses it to determine the order of lock requests and conflict resolution.
Using a priority for each transaction, such as the priority-based concurrency control technique, which assigns a priority to each transaction and uses it to determine the order of lock requests and conflict resolution.

By using locking protocols and levels and timeout and deadlock avoidance techniques, you can prevent deadlocks from occurring in transactions and improve the performance and reliability of your database system. However, these techniques can also have some drawbacks, such as increased complexity, reduced concurrency, and increased overhead. In the next section, you will learn how to conclude your blog and summarize the main points of the tutorial.

4.1. Locking Protocols and Levels

Locking protocols and levels are rules that determine when and how transactions can acquire and release locks on data items. Locking protocols and levels can prevent deadlocks by ensuring that the four conditions for deadlock formation are not met. These conditions are:

Mutual exclusion: A data item can be locked by only one transaction at a time.
Hold and wait: A transaction can hold a lock on a data item and wait for another lock on a different data item.
No preemption: A lock on a data item can be released only by the transaction that holds it.
Circular wait: A cycle of transactions exists, where each transaction is waiting for a lock held by the next transaction in the cycle.

By following a locking protocol, a transaction can avoid creating a deadlock situation by adhering to a certain order or pattern of lock requests and releases. For example, the two-phase locking protocol requires a transaction to acquire all its locks before releasing any of them. This protocol prevents the hold and wait and the circular wait conditions, as a transaction cannot request a new lock after releasing an old one, and cannot form a cycle with other transactions.

By using a different level of lock, a transaction can avoid creating a deadlock situation by locking a data item at a different level of detail. For example, the granularity levels allow a transaction to lock a data item at different levels, such as a record, a page, or a table. This level can affect the degree of concurrency and the likelihood of deadlock. A finer level of lock, such as a record, can allow more concurrency, but also increase the chance of deadlock. A coarser level of lock, such as a table, can reduce the chance of deadlock, but also decrease the concurrency.

By using a different mode of lock, a transaction can avoid creating a deadlock situation by locking a data item in a different mode. For example, the multiple granularity locking protocol allows a transaction to lock a data item in different modes, such as shared, exclusive, or intention. These modes can indicate the type and the level of the lock, and the compatibility with other locks. A shared lock allows a transaction to read a data item, but not to modify it. An exclusive lock allows a transaction to both read and modify a data item, but not to share it with other transactions. An intention lock indicates that a transaction intends to acquire a lock on a lower level data item. For example, an intention shared lock on a table means that a transaction intends to acquire a shared lock on a record in that table.

By using locking protocols and levels, you can prevent deadlocks from occurring in transactions and improve the performance and reliability of your database system. However, these techniques can also have some drawbacks, such as increased complexity, reduced concurrency, and increased overhead. In the next section, you will learn about another technique to prevent deadlocks: timeout and deadlock avoidance techniques.

4.2. Timeout and Deadlock Avoidance Techniques

Timeout and deadlock avoidance techniques are methods that prevent transactions from waiting indefinitely for locks or entering into a deadlock situation. Timeout and deadlock avoidance techniques can prevent deadlocks by:

Setting a time limit for a transaction to wait for a lock, such as the timeout technique, which aborts a transaction if it does not acquire a lock within a specified time.
Using a timestamp for each transaction, such as the timestamp-based concurrency control technique, which assigns a unique timestamp to each transaction and uses it to determine the order of lock requests and conflict resolution.
Using a priority for each transaction, such as the priority-based concurrency control technique, which assigns a priority to each transaction and uses it to determine the order of lock requests and conflict resolution.

The timeout technique is a simple and effective method to prevent deadlocks, as it ensures that a transaction does not wait forever for a lock. However, the timeout technique can also have some drawbacks, such as:

Choosing an appropriate timeout value can be difficult, as it depends on various factors, such as the workload, the system performance, and the user expectations.
Setting a timeout value too low can cause unnecessary aborts, as a transaction may be aborted even if it could have acquired the lock soon.
Setting a timeout value too high can cause long delays, as a transaction may wait for a long time before being aborted.

The timestamp-based concurrency control technique is a sophisticated and elegant method to prevent deadlocks, as it uses a logical order of transactions based on their timestamps. However, the timestamp-based concurrency control technique can also have some drawbacks, such as:

Generating and maintaining timestamps can be costly and complex, as it requires additional system resources and overhead.
Handling timestamp conflicts can be challenging, as it requires different policies and mechanisms, such as the Thomas’ write rule or the multiversion concurrency control.
Ensuring global synchronization of timestamps can be difficult, as it requires coordination and communication among different nodes or servers.

The priority-based concurrency control technique is a flexible and adaptive method to prevent deadlocks, as it uses a priority value for each transaction based on various criteria, such as the age, the progress, or the cost of the transactions. However, the priority-based concurrency control technique can also have some drawbacks, such as:

Assigning and updating priorities can be costly and complex, as it requires additional system resources and overhead.
Choosing an appropriate priority criterion can be difficult, as it depends on various factors, such as the workload, the system performance, and the user expectations.
Ensuring fairness and consistency of priorities can be difficult, as it requires coordination and communication among different nodes or servers.

By using timeout and deadlock avoidance techniques, you can prevent deadlocks from occurring in transactions and improve the performance and reliability of your database system. However, these techniques can also have some drawbacks, such as increased complexity, reduced concurrency, and increased overhead. In the next section, you will learn how to conclude your blog and summarize the main points of the tutorial.

5. Conclusion

In this blog, you have learned how to avoid and resolve deadlocks in transactions. Deadlocks are a common problem that can occur when multiple transactions try to access and modify the same data concurrently. Deadlocks can cause performance degradation, data inconsistency, and system failure.

To avoid and resolve deadlocks, you need to know the following concepts and techniques:

The concept of locking, which is a mechanism that prevents concurrent transactions from accessing or modifying the same data item at the same time.
The conditions for deadlock formation, which are mutual exclusion, hold and wait, no preemption, and circular wait.
The deadlock detection algorithms, which are methods that identify the transactions that are involved in a deadlock and report them to the system.
The deadlock resolution strategies, which are methods that break the deadlock and allow the transactions to proceed. These strategies include aborting, preempting, and restarting.
The deadlock prevention techniques, which are methods that prevent transactions from waiting indefinitely for locks or entering into a deadlock situation. These techniques include locking protocols and levels and timeout and deadlock avoidance techniques.

By applying these concepts and techniques, you can prevent and resolve deadlocks in transactions and improve the performance and reliability of your database system. However, these concepts and techniques can also have some drawbacks, such as increased complexity, reduced concurrency, and increased overhead. Therefore, you need to choose the appropriate ones for your specific situation and requirements.

We hope you have enjoyed this blog and learned something useful. If you have any questions or feedback, please leave a comment below. Thank you for reading!