Data Anomalies

• errors that can occur within databases when transactions are not isolated properly.
• especially relevant in distributed-systems with concurrent operations.

Types of data anomalies.

• dirty write.
  • when two write operations run in sync and we end up with a mish-mash of their values.
  • very difficult to roll-back.
• dirty read.
  • when an operation reads a value from another uncommitted operation.
• fuzzy read.
  • a value retrieved twice during an operation is different on both retrievals.
• lost update.
  • when two operations try to update the same value, but only one operation wins, while the other one isn’t aware of anything going wrong.
  • 
• read skew.
  • when one operation alters and then commits some data that is needed to be read by another ongoing transaction.
  • 
• write skew.
  • when two operations read the same data but then modify different data.
  • 

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Related.

• isolation
• asynchronism

References.

Categories:: database