In the bustling digital world of today, Database Management Systems (DBMS) . The bedrock upon which countless applications and services are built.
From online banking and e-commerce to social media and scientific research, almost every interaction we have involves data being stored, retrieved, and modified within a database.
A critical challenge in managing
these complex systems arises when multiple accurate cleaned numbers list from frist database users or applications attempt to access and manipulate the same data simultaneously.
This is where concurrency control mechanisms come into play, acting Concurrency Control Mechanisms as the invisible traffic cops of the database, ensuring data integrity, consistency, and efficiency in a multi-user environment.
Without proper concurrency control, a database part of conceptual or logical models would quickly descend into chaos. Imagine two users trying to update the same bank account balance at the same time.
If both read the initial balance, perform their calculations.
And then write the new balance independently, one update would inevitably overwrite the other, leading to an incorrect final balance.
This is just one example of the “anomalies” that concurrency control aims to prevent. These anomalies typically fall into categories such as:
- Lost Updates: As illustrated above, one transaction’s update is overwritten by another’s.
- Dirty Reads (Uncommitted Dependency): A transaction european data reads data written by another transaction.
- That has not yet committed (and might later rollback), leading to the reading of invalid data.
- Non-Repeatable Reads: A transaction Concurrency Control Mechanisms reads the same data twice and gets different values because another committed transaction modified the data between the two reads.
- Phantom Reads: A transaction re-executes a query returning a set of rows and finds that the set of rows satisfying the query has changed due to another committed transaction inserting or deleting rows.