Concurrency Control in DBMS

What is Concurrency Control in DBMS?

Concurrency control, to put it simply, is a method that guarantees that different users may access and modify the same data without causing conflicts. Changes made by one user in an environment may be overwritten by another before the first user has an opportunity to verify their work when numerous users are accessing the same data simultaneously. Data corruption and other concurrency control issues in DBMSs might arise from this.

By guaranteeing that only one user has access to a data item at any given time, concurrency control techniques in database management systems (DBMS) aid in the prevention of these issues. Locks are associated with data elements to achieve this. A data item that has been locked by one user is inaccessible to other users until the lock is released. There are two categories for locks: exclusive locks and shared locks. A shared lock permits reading of the data item but prevents modification.

Any other user cannot access or edit the data item due to an exclusive lock. An example of concurrency control in a database management system (DBMS) is a banking system where two users try to take money out of the same account at the same time. A race situation may arise if there was insufficient concurrency management, allowing both actions to view the account balance simultaneously. A mistaken account balance might arise from the first transaction deducting the amount from the starting balance and committing the change, while the second transaction does the identical operation without knowing about the first transaction's update.

Concurrency control is therefore necessary in DBMS. To prevent this, employ concurrency control techniques like locks or isolation layers. By limiting the number of transactions that may see and modify an account's balance at once, these protocols help to maintain the integrity of system data and avoid data inconsistencies.

Concurrency Control Problems

Concurrency control in a DBMS allows several transactions to utilize the same data concurrently in a concurrent execution environment. Concurrent access in DBMSs can lead to various concurrency control issues if it is not appropriately controlled. These issues include the following.

1. Deadlocks: When two or more transactions are waiting on one another to finish, a deadlock occurs. When two transactions try to lock the same data item, this might happen.
2. Data Corruption: When two or more transactions change the same piece of data simultaneously and one of them overwrites the other's modifications, data corruption occurs.
3. Inconsistency:  Inconsistency occurs when two or more transactions read the same data at different times and observe different values for the same data.
4. Lost Updates: Lost updates happen when many transactions try to alter the same data at once. The modifications from the initial transaction are lost if one transaction overwrites the changes made by another before they are committed. Inaccurate results and inconsistent data follow as a result.

Phantom Read Problem

A particular kind of concurrency issue known as a "phantom read" problem arises when a transaction reads a set of data, then reads the same set of data again, only to find that the second read has extra rows that weren't there in the first read. If data is inserted into the table by another transaction in the interval between the two reads, this can happen. 

Let's use an example to better grasp this: Imagine a transaction that scans every row in an employee table. Before stopping to complete additional duties, the transaction analyzes the first 100 rows. Ten more entries are added to the table in the meantime by another transaction. The 10 new rows that the other transaction added are not visible to the first transaction when it resumes; instead, it reads the next 100 rows. Phantom reading is what's happening here.

Phantom readings can cause errors in results, which is why they might be troublesome. There are several ways to stop phantom readings. Using a greater transaction isolation level is one strategy. A transaction that is reading data cannot have its data altered by other transactions thanks to a higher isolation level. Phantom readings will be prevented as a result.

Lost Update Problem

A particular kind of concurrency problem known as a lost update problem arises when two transactions make changes to the same data item and one of the updates is lost. This can happen if a database management system (DBMS) allows the concurrent execution of two transactions.

 While they both read the identical data item, only one makes any changes. Let's use an example to better grasp this: Think about two transactions that change a bank account's balance. The initial transaction amounts to $200 and reads the account balance. The first transaction then updates the balance to $300. The account balance is read in the second transaction, which is still $200. The second transaction updates the balance to $400. Then, modifications made in the first transaction are committed, but those in the second are not. 

The update from the initial transaction was missed, even if the account's final amount is $400. Issues with missing updates can be avoided in a few ways. One choice for concurrency control in DBMSs is the locking strategies. The same data item cannot be updated by two transactions at the same time thanks to a locking mechanism. By doing this, lost update problems will be prevented.

Concurrency Control Protocols

In database management systems, concurrency control protocols are processes that keep data consistent and intact even when several operations are going on at once. In order to prevent conflicts and preserve the correctness of the database, these protocols control how transaction actions are interspersed. Concurrency control protocols use several techniques to accomplish these goals. Concurrency in DBMSs is primarily used to enable concurrent transaction processing while maintaining the ACID features of the database.

Time-based Protocols: One kind of DBMS concurrency control that uses timestamps to arrange transactions such that older ones take precedence over newer ones is time-based protocols. This guarantees consistency of the database and prevents conflicts between concurrent transactions. With this method, every transaction receives a distinct timestamp indicating the order in which it was executed. Timestamps are used by transactions to determine if they are authorized to read or write data items. The Thomas Write Rule, which defines guidelines for read and write operations based on transaction timestamps, is the most widely used timestamp-based protocol.

There are two categories for time-based protocols.

1. Timestamp Ordering Protocol: A transaction is given a distinct timestamp when it starts. Older timestamp transactions are prioritized above more recent timestamp transactions. The transaction with the oldest timestamp prevails if two transactions attempt to access the same data item. Until the earlier transaction has completed accessing the data item, the more recent transaction is stopped.
    
2. Multiversion Concurrency Control MVCC: Every data item is maintained in several versions via the multi version concurrency control in DBMSs. Each version of a data item has a timestamp attached to it. Without impacting other transactions, transactions are free to read from and write to their own copies of the data item. The DBMS checks to see whether any other transactions have read the same data item before attempting to write to it. In the event that there are, the data item is updated by the DBMS so that the transaction may write to it. This makes sure that the write operation of the transaction doesn't affect any other transactions that have read the data item.

Qualities of an Effective Concurrency Protocol

A strong concurrency protocol need to possess the subsequent attributes.

1. Correctness: The protocol has to guarantee that, even when several transactions access the database simultaneously, it remains consistent.
2. Robustness: The protocol must be able to withstand setbacks and fail gracefully.
3. Flexibility: A broad range of applications should be compatible with the protocol due to its adaptability.
4. Scalability: In order to operate with large databases, the protocol must be scalable.
5. Efficiency: Both time and space should be spared in the procedure. It shouldn't put the system under undue strain.

Concurrency Method: Why Use It?

When several transactions occur simultaneously in a database management system (DBMS), concurrency control strategies are essential to preserving data consistency and integrity. These protocols preserve the ACID characteristics of the database while enabling the reliable and efficient execution of transactions. A lot of factors, such as the kind of database, the volume of transactions, and the necessary performance level, influence the choice of protocol.

There are several essential characteristics of an effective concurrency control protocol. By averting conflicts and guaranteeing data concurrency in DBMS, it maintains consistency. Deadlocks may be prevented and detected using the tools in the protocol, which guarantees system responsiveness. 

Frequently Asked Questions (FAQs)

Q 1. How does concurrency control affect database performance?

Answer. Concurrency control in a database makes sure that several transactions may occur simultaneously without interfering with one another. Implementing concurrency management mechanisms correctly is essential to safeguarding data integrity and maximizing performance in situations with a lot of simultaneous processing.

Q 2. How might database query optimization help developers achieve better concurrency control?

Answer. By limiting the amount of data locked, utilizing suitable isolation levels, and applying proper locking strategies, database queries may be optimized for better concurrency control.