I'm analyzing a schedule of database transactions to identify potential anomalies such as lost updates, non-repeatable reads, and dirty reads. Below is the schedule represented in a tabular format, with transactions (T1 through T5) and their respective operations on resources X and Q.
Operations are denoted as follows: $R(X)$ means read X, $W(X)$ means write X, and similar for Q. "Commit" marks the end of a transaction, and "abort" indicates a transaction failure.
Given the anomalies identified:
- A lost update occurs on Q for transactions T3 and T4.
- A dirty read occurs on X for transactions T2 and T5.
- A dirty read occurs on X for transactions T2 and T3.
I'd appreciate some guidance on why these specific anomalies are correct based on the transaction schedule provided. My understanding of these concepts is quite basic, and I'm trying to solidify my grasp on how these anomalies manifest in real-world scenarios. Specifically:
Lost Update on Q for T3 and T4: How does the interaction between T3 and T4 lead to a lost update on Q? My current understanding is that a lost update refers to a scenario where two transactions read and write to the same data element, causing one of the writes to be overwritten and lost. However, I'm unclear on the precise sequence of events for T3 and T4 that results in this anomaly.
Dirty Read on X for T2 and T5: Why is this scenario considered a dirty read? I know a dirty read occurs when a transaction reads data that has been written by another transaction that has not yet committed, potentially leading to inconsistencies if the writing transaction later aborts. But, looking at the schedule, I'm struggling to see the direct relationship between T2 and T5 that leads to this anomaly.
Dirty Read on X for T2 and T3: Similar to the previous point, I'm trying to understand the basis for identifying this as a dirty read. Given that T2 and T3 both interact with X, what specific actions result in T2 reading uncommitted changes made by T3, or vice versa?
Understanding the reasons behind these classifications will greatly help me in analyzing transaction schedules more effectively and in recognizing these anomalies in different contexts.
