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Concurrency Effects

Users modifying data can affect other users who are reading or modifying the same data at the same time. These users are said to be accessing the data concurrently. If a data storage system has no concurrency control, users could see the following side effects:

  • Lost updates.
  • Uncommitted dependency (dirty read).
  • Inconsistent analysis (nonrepeatable read).
  • Phantom reads.

Lost updates occur when two or more transactions select the same row and then update the row based on the value originally selected. Each transaction is unaware of the other transactions. The last update overwrites updates made by the other transactions, which results in lost data.

For example, two editors make an electronic copy of the same document. Each editor changes the copy independently and then saves the changed copy thereby overwriting the original document. The editor who saves the changed copy last overwrites the changes made by the other editor. This problem could be avoided if one editor could not access the file until the other editor had finished and committed the transaction.

Uncommitted dependency occurs when a second transaction selects a row that is being updated by another transaction. The second transaction is reading data that has not been committed yet and may be changed by the transaction updating the row.

For example, an editor is making changes to an electronic document. During the changes, a second editor takes a copy of the document that includes all the changes made so far, and distributes the document to the intended audience. The first editor then decides the changes made so far are wrong and removes the edits and saves the document. The distributed document contains edits that no longer exist and should be treated as if they never existed. This problem could be avoided if no one could read the changed document until the first editor does the final save of modifications and commits the transaction.

Inconsistent analysis occurs when a second transaction accesses the same row several times and reads different data each time. Inconsistent analysis is similar to uncommitted dependency in that another transaction is changing the data that a second transaction is reading. However, in inconsistent analysis, the data read by the second transaction was committed by the transaction that made the change. Also, inconsistent analysis involves multiple reads (two or more) of the same row, and each time the information is changed by another transaction; thus, the term nonrepeatable read.

For example, an editor reads the same document twice, but between each reading the writer rewrites the document. When the editor reads the document for the second time, it has changed. The original read was not repeatable. This problem could be avoided if the writer could not change the document until the editor has finished reading it for the last time.

Phantom reads occur when an insert or delete action is performed against a row that belongs to a range of rows being read by a transaction. The transaction's first read of the range of rows shows a row that no longer exists in the second or succeeding read as a result of a deletion by a different transaction. Similarly, the transaction's second or succeeding read shows a row that did not exist in the original read as the result of an insertion by a different transaction.

For example, an editor makes changes to a document submitted by a writer, but when the changes are incorporated into the master copy of the document by the production department, they find that new unedited material has been added to the document by the author. Similar to the nonrepeatable read situation, this problem could be avoided if no one could add new material to the document until the editor and production department finish working with the original document.

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