In computer science, a timestamp-based concurrency control algorithm is a optimistic concurrency control method. It is used in some databases to safely handle transactions, using timestamps.
A number of different ways have been used to generate timestamp
Each transaction () is an ordered list of actions (). Before the transaction performs its first action (), it is marked with the current timestamp, or any other strictly totally ordered sequence: . Every transaction is also given an initially empty set of transactions upon which it depends, , and an initially empty set of old objects which it updated, .
Each object in the database is given two timestamp fields which are not used other than for concurrency control: is the time at which the value of object was last read by a transaction, is the time at which the value of the object was last updated by a transaction.
For all :
Whenever a transaction initiated, it receives a timestamp. The transaction's timestamp indicates when the transaction was initiated. These timestamps ensure that transactions affect each object in the same sequence of their respective timestamps. Thus, given two operations that affect the same object from different transactions, the operation of the transaction with the earlier timestamp must execute before the operation of the transaction with the later timestamp. However, if the operation of the wrong transaction is actually presented first, then it is aborted and the transaction must be restarted.
Every object in the database has a read timestamp, which is updated whenever the object's data is read, and a write timestamp, which is updated whenever the object's data is changed.
If a transaction wants to read an object,
If a transaction wants to write to an object,
The behavior is physically unrealizable if the results of transactions could not have occurred if transactions were instantaneous. The following are the only two situations that result in physically unrealizable behavior:
Note that timestamp ordering in its basic form does not produce recoverable histories. Consider for example the following history with transactions and :
This could be produced by a TO scheduler, but is not recoverable, as commits even though having read from an uncommitted transaction. To make sure that it produces recoverable histories, a scheduler can keep a list of other transactions each transaction has read from, and not let a transaction commit before this list consisted of only committed transactions. To avoid cascading aborts, the scheduler could tag data written by uncommitted transactions as dirty, and never let a read operation commence on such a data item before it was untagged. To get a strict history, the scheduler should not allow any operations on dirty items.
This is the minimum time elapsed between two adjacent timestamps. If the resolution of the timestamp is too large (coarse), the possibility of two or more timestamps being equal is increased and thus enabling some transactions to commit out of correct order. For example, assuming that we have a system that can create one hundred unique timestamps per second, and given two events that occur 2 milliseconds apart, they will probably be given the same timestamp even though they actually occurred at different times.
Even though this technique is a non-locking one, in as much as the Object is not locked from concurrent access for the duration of a transaction, the act of recording each timestamp against the Object requires an extremely short duration lock on the Object or its proxy.
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