When the speed of data processing is increasing, databases are essential in the work of businesses, applications, and online services. Due to the enhanced use of databases in decision-making processes, the database must accommodate an array of operations concurrently. Thus, if several unauthorized users or different applications are working in parallel with the same database, issues connected with database consistency and data integrity arise.
This blog will describe concurrency control, the problems it solves, Why is Concurrency Control Important in a DBMS, and how it works in practice. We will also explain different approaches that can be applied to control concurrent access and common strategies for database consistency.
Concurrency control in the context of a Database Management System (DBMS) is one of the important confidential control measures aimed at preventing conflicts arising from parallel transactions. It guarantees that no matter whether two or more users or processes request the same data at the same time, the database ends up in a valid state.
What is Concurrency Control?
Table of Contents
Concurrency control means a way of handling multiple transactions that can occur at the same time and, in this way, prevent any conflict that may bring about more problems in the database. In its broadest sense, it may be described as the rules, procedures and methodologies that a DBMS uses to ensure that a number of concurrently running transactions do not adversely affect each other in terms of the reliability of the database.
A database transaction is a collective group of processes that are to be completed as a single activity. Concurrency control ensures that all transactions adhere to the ACID properties:
Atomicity: Each transaction is atomic; it is either fully executed or it is not completed at all and has no impact on the database.
Consistency: The database moves from one consistently valid state to another while preserving constraints.
Isolation: All transactions work in isolation, meaning that another transaction does not see the changes until they have been completed.
Durability: Once a transaction has been committed, any changes to the database are permanent regardless of whether or not a system failure occurred.
For these purposes, concurrency control must efficiently monitor the effectiveness of the transactions and permit simultaneous access to the database while maintaining its integrity.
| Also Read: Why Database Management System Is Important |
Challenges in Concurrency
Concurrency in a DBMS has some issues that, if not well managed, can compromise the database integrity. These problems come up due to the combined effects of concurrent transactions and the data they work with. The following are the most common issues:
Lost Updates:
Concurrent update problems arise when two or more transactions simultaneously read the same data and try to update it, and in the process, one update overwrites or is lost. This is commonly felt when the updates done by one transaction cancel out the update of another. In essence, where one transaction makes some changes to an object data, no form of notification is given to another transaction that it also modified the same object data.
Example:
Think of two people who are entering the same figure into a banking system, for instance, the account balance. User A tops up $100 for the account, while User B deducts $50 from the same account. Without concurrency control, the last recorded balance can be the value of only one update, and thus, there can be data loss.
Dirty Reads:
A dirty read occurs when a transaction reads data that was changed by another transaction, but this change, which updated the data, is not yet committed at that time. This causes problems when one of the two transactions is rolled back, as the data read by the first transaction may be ineffective in giving the expected result.
Example:
For example, suppose User A updates an account balance, and User reads this balance before the execution of this transaction by User A.
If the transaction by User A is rolled back, then User B might be using the wrong data.
Non-Repeatable Reads:
When a transaction reads the same data multiple times and gets different values each time, the second reading occurred because another transaction altered the data in the interim.
Example:
A user searches for an item in a certain online store, and then, between the two-time instances, another user buys the final piece of the item. The first user for each check sees different availability.
Phantom Reads:
Phantom reads occur when a transaction uses a query that returns data from the table and another transaction inputs or erases records into the same table.
Example:
A report that puts into a query for all the items in inventory in an inventory management system can give two different results if new items have been put into inventory or when some items are pulled out of inventory during the time the report is being run.
Why is Concurrency Control Important in DBMS?
The first rationale of concurrency control is to avoid a situation whereby many users or transactions simultaneously access or modify a similar database. The importance of concurrency control in a DBMS cannot be overstated, and it is crucial for several reasons:
Ensures Data Integrity:
Concurrency control prevents any pair of transactions from violating the consistency of the data being processed. Otherwise, problems like lost updates, dirty reads, or phantoms would occur, and the data would become mixed up.
Preserves Transaction Isolation:
Concurrency control ensures that only one transaction runs at a time. This ensures that no two transactions interfere with one another, whereby each transaction’s state in the middle of completion cannot be seen by other transactions to complete it.
Optimizes Performance:
In cases where several transactions are processed simultaneously, they do not consume system resources. A good concurrency control strategy ensures that many transactions can take place concurrently without much time waste or interference, which actually improves system throughput.
Enhances User Experience:
Concurrency control allows users to access only current data. For example, in a bank, online users can perform more than one transaction, such as checking a balance, withdrawing cash or transferring money to another account, without interference from other users.
Supports Scalability:
Due to the growing number of required transactions (for example, in applications of large systems such as social networks or online stores), concurrency control allows the system to work effectively with subscriptions and do this in parallel with maintaining the integrity of data.
Concurrency Control Mechanisms
Various schemes and protocols exist for controlling concurrency in a DBMS, and they all have advantages and disadvantages. These make certain that all database transactions will take place conflict-free, and they possess ACID properties.
Lock-Based Protocols:
Concurrency control is mostly managed by locking, which is one of the well-known methods. Locks limit the ability of all transactions to read or write to a specific data item, meaning that only one can do this at a time.
Shared Locks: One or many transactions can have shared locks on the same data, enabling them to read from that data simultaneously.
Exclusive Locks: An exclusive lock denies any other transaction the right to access the data item until the lock has been unlocked.
Two-Phase Locking (2PL):
Two-phase locking ensures that all transactions are locked before they can perform any operation and only released after the entire transaction has been closed. This protocol ensures that the final state seen by the database will allow transactions one by one and that the outcomes must be serializable.
Timestamp-Based Protocols:
The timestamp method of concurrency control involves allocating a timestamp number to a transaction whenever it is formed. Operations are performed on a first-in-first-out basis, which implies that the transaction with the previous timestamp represents the first transaction to be processed.
Example:
When two transactions attempt to modify the same data, the one with the pre-timestamp will take precedence. This leads to sequential and, therefore, repeated order of execution.
Optimistic Concurrency Control:
Optimistic concurrency control, on the other hand, supposes that transaction conflicts are uncommon. It occurs in parallel to locks without locking but validates before committing change. If contradictions are identified during validation, that is, if other transactions change the data in the same tables, then the transaction is rolled back and repeated.
Multi-Version Concurrency Control (MVCC):
To read and write data simultaneously, MVCC keeps multiple versions of a data item while the writer changes the current version. This results in non-blocking reads and also enhances transaction throughput in large traffic conditions.
Example:
In PostgreSQL or MySQL’s InnoDB engine, users can read a transactionally consistent copy of the data while others are writing to the database.
Deadlock Prevention and Detection:
Deadlocks exist when two or more transactions have to wait for the others to free the held resource, and since none can continue, they are stuck. Deadlock prevention mechanisms identify and break such conditions by aborting one or more transactions. Other cases are self-detecting, where timeout or resource graph methods are used to detect deadlocks within the system.
Applications, Examples, and Use-Related Benefits
Concurrency control mechanisms are used wherever multiple users and processes are interacting with the database, or at least wherever a possibility of simultaneous interactions is possible. Some examples include:
Banking Systems:
Concurrency control in a banking system means that several clients can withdraw money from their accounts or make a transfer simultaneously without affecting other clients’ balances or creating conditions such as overdrawn or wrong account balances.
E-Commerce Platforms:
Many stores using e-commerce experience high traffic at certain times, such as during a sale or a special promotion. Concurrency control prevents different users from trying to buy the same item at the same time, and the inventory is updated accurately as the transactions run.
Online Ticket Booking Systems:
In order-selling or ticket-selling systems, such as selling event tickets or flight tickets, the concurrency control inhibits ticket duplication. You can guarantee that as soon as someone books a seat, no other person can do it again.
Best Practices for Concurrency Control in DBMS
Select the Right Isolation Level:
Transaction visibility is knowing how various transaction isolation levels exercise their influence on each other. There is a need to select the best isolation level that will ensure consistency and system performance at the same time. For instance, the Serializable level offers the most level of transaction isolation, which might affect system throughput, while the Read Committed level might allow instance, the Serializable level gives the most level of transaction isolation that may reduce the throughput of the system while the Read Committed may allow
Optimize Locking Strategies:
You should design your system based on fine-grained network locks and try to reduce contention by keeping transactions brief and avoiding extended locks.
Design for Minimal Conflict:
Concurrency conflicts have costs, so avoid accessing the same data at the same time whenever possible.
Monitor and Handle Deadlocks:
Containment strategies: Develop ways to detect deadlocks in transaction processing and control which transactions may be sentenced to abortion or repeat execution in case of conflict.
Conclusion
Concurrency control is a very important feature of a database. It allows a number of users or programs to access a given database without causing data inconsistency or impairing database performance. Concurrency control is crucial when transaction rates are high, for instance, in banking systems, e-commerce and social media applications.
Concurrency control measures such as handling lost updates, dirty reads and deadlocks enable DBMS to deliver the four key transaction attributes or the ACID properties, thus making data processed always up-to-date and accurate. This holds especially true as systems are developed, enhanced, and made larger and more complex since understanding and deploying concurrency control is a foundational and critical model for efficient and scalable application development.
