Deadlock is a critical challenge in database management systems (DBMS) that can cause system failure and impact overall performance. The potential for deadlocks increases in a multi-user environment, where multiple transactions compete for shared resources. Understanding the concept of deadlock and its implications is important for DBMS administrators, developers, and users. If you are new to DBMS or a professional who needs to brush up their skills in DBMS and get proficient in handling deadlock in DBMS, we recommend enrolling for with placement to upskill. Whether you are a novice in DBMS or a professional seeking to enhance your skills and proficiency in handling deadlocks in DBMS in a Database course. The Database course fees for such programs may vary, but they are designed to provide valuable upskilling opportunities to individuals like you.
In this blog, we will discuss the concepts of deadlock in DBMS, its causes, conditions, handling methods, and future scopes.
In a simple and informal explanation, a deadlock in DBMS (Database Management System) occurs when two or more transactions are stuck in a never-ending or indefinite waiting state. Each transaction needs a resource that another transaction holds, which is referred to as the locking of resources, thus, creating a deadlock situation. It's like a "standoff" where no transaction can proceed because they're all waiting for resources to get released. In this situation, both transactions will remain paused until the system detects a deadlock and takes suitable action to eliminate it.
The below figure shows an example where process P2 holds the resource R1, and P1 needs R1. Similarly, P1 holds R2, but P2 needs R2. Both processes are waiting for each other to release the resource to proceed further.
Let’s understand the deadlock with another example, which targets a database.
The example involves two Transactions, T1 and Transaction T2, and two resources, Resource X and Resource Y.
Transaction T1 requests and holds a lock on Resource X in a bank account table.
Similarly, transaction T2 requests and holds the lock Resource Y.
Next, transaction T1 requests Resource Y but is forced to wait since transaction T2 holds it.
Similarly, transaction T2 requests Resource X but is forced to wait since transaction 1 holds it.
Both transactions will wait for each other to release the lock to complete their task resulting in a deadlock.
Let’s now discuss the causes of deadlock in DBMS.
Here are a few common causes of deadlock in DBMS
To better understand deadlock, we will discuss a real-life deadlock in DBMS example.
Imagine a busy city intersection with four vehicles: A, B, C, and D. Each vehicle wants to cross, but only one can fit at a time on a lane. A and B arrive simultaneously, blocking each other's way. C and D approach later, trying for their turn. However, the deadlock situation has now occurred here, leaving all vehicles trapped, unable to move forward. A is waiting for B to proceed, and B is waiting for A to proceed, creating a circular waiting condition for these vehicles.
Besides, there is a cascading effect where vehicles C and D cannot proceed just because the other two cars have held the way (a resource).
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There are four ways to deal with deadlock:
This strategy aims to reduce the possibility of deadlock conditions by designing the algorithms in such a manner that DBMS analyzes the operations and whether they can create a deadlock situation, based on which the transaction executes or stops. Two known schemes prevent deadlock in DBMS by dynamically allocating resources based on process timestamps:
When a process requests a resource, it will wait if its timestamp is less than the process currently holding the resource. The process will wait until the resource becomes available. However, if the requesting process has a higher or equal timestamp than the holding process, it aborts itself, rolls back, and restarts later with a new timestamp.
For example, consider two transactions, T1 and T2, where T1 has a lower timestamp than T2. If T1 requests a resource held by T2, T1 waits until T2 releases the resource since T1's timestamp is lower. On the other hand, if T2 requests a resource held by T1, it would abort itself, roll and restart with a new timestamp later.
This ensures that transaction T1 doesn’t keep waiting indefinitely for the requested resource, thus preventing deadlock situation.
Note that this scheme comes under non-preemptive scheduling as the system kills the process and restarts it later.
A process requesting a resource waits if its timestamp exceeds the timestamp of the process holding the resource. If the requesting process has a lower or equal timestamp, it can preempt the resource from the holding process, causing the holding process to get wounded (preempted) and restarted.
For instance, consider two transactions, T1 and T2, where T1 has a higher timestamp than T2. If T1 requests a resource held by T2, T1 can preempt the resource from T2, causing T2 to get wounded and restarted. However, if T2 requests a resource held by T1, T2 waits for the resource since T2's timestamp is lower.
This approach focuses on carefully allocating the resources to the processes that are requesting it. The system does this by analyzing the resource requests and availability in advance to identify potential deadlock conditions. There are different methods involved in deadlock avoidance, including resource allocation graphs, bankers' algorithm, or dynamic resource scheduling to keep track of resource allocation.
Here is a sample table to show how the system keeps track of the resources requested and allocated to different processes at a time:
Process | Allocated | Requested | ||||
| A | B | C | A | B | C | |
| P1 | 0 | 1 | 0 | 7 | 5 | 3 |
| P2 | 2 | 0 | 0 | 3 | 2 | 2 |
| P3 | 3 | 0 | 2 | 9 | 0 | 2 |
| P4 | 2 | 1 | 1 | 4 | 2 | 2 |
| P5 | 0 | 0 | 2 | 5 | 3 | 3 |
This table estimates the number of resources available and allocates to the requesting processes. Here the legends mean the following:
P1: Process 1
P2: Process 2
P3: Process 3
P4: Process 4
P5: Process 5
While A, B and C are Resource A, Resource B and Resource C respectively.
Refers to a situation where someone is unaware or chooses to ignore a deadlock’s existence or potential consequences. In such a scenario, the system does not implement proper measures to detect, prevent, or resolve deadlocks because they occur less often. This category of an algorithm is the “Ostrich Algorithm” inspired by the Ostrich bird, which hides its face under the sand and “pretends” there is no problem.
Involves periodically examining the state of resources, processes, and their dependencies to determine if a circular waiting condition exists. This condition is a key indicator of a potential deadlock. The system can detect if processes are stuck in a deadlock by analyzing resource allocation graphs or using algorithms such as the Banker’s algorithm. Once the system detects a deadlock, it takes appropriate actions to resolve it, such as terminating specific processes or releasing resources to break the deadlock and restore normal system operation.
Deadlock involves multiple processes being stuck in a circular wait for shared resources. There are four primary conditions for the deadlock: mutual exclusion, hold and wait, no preemption, and circular wait. Deadlock can cause system failure and reduced performance. There are different methods to manage deadlocks, including their prevention, avoidance, detection, or simply ignoring it. The methods for deadlock will become more advanced by incorporating strategies like distributed deadlock management, AI/ML, and adaptive methods.
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The probability of deadlock increases in multi-user environments due to more requests. For this case, you should implement proper algorithms to avoid race conditions, periodically check for the occurrence of deadlock, and preempt the resources at necessary times.
The deadlock results in blocked processes and resources, causing delays in query execution. The database optimizer may need to wait for the resolution of the deadlock before proceeding with optimization.
You should implement distributed transaction management algorithms to handle the transactions across multiple nodes. Besides, using advanced load balancing methods can also help you balance resource usage across multiple nodes.
One common strategy is to prioritize transactions based on their deadlines and criticality. You should also monitor the resource allocation regularly to handle deadlock scenarios.
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