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Friday, September 19, 2014

Best practices for concurrency control with respect to databases?

Today almost all the service – oriented businesses have grown highly dependent on reliable and speedy access to their data. Most of the global enterprises need this access to databases on a 24x7 level without interruptions. These reliability, availability and performance needs of the organizations are met by database management systems (DBMS). Thus, a DBMS is responsible for two things. Firstly, for protecting data that it stores and for providing correct, reliable and all – time access to this data. The concurrency control and recovery mechanisms of DBMS are responsible for carrying out these functions properly.
Concurrency control mechanism ensures that you get to see the execution of only your transaction even though 100s of users are accessing the database at the same time. The recovery mechanism ensures that the database is able to recover from all the faults. It is because of the existence of these functionalities that the programmers feel free to add new parts to the system without having much to worry about. A transaction is nothing but a unit of work which consists of several operations and updates. Every transaction is expected to obey the ACID rules. In this article we discuss about some best practices for concurrency control.
- Two phase locking: Locking is perhaps the most widely used technique for maintaining control over concurrency matters. This mechanism provides two types of locks namely, the shared lock (S), and the exclusive lock (X). The compatibility matrix defines the compatibility of these two locks. According to the compatibility matrix, S locks can be held by two different transactions at the same time but this is not possible in the case of X locks for the same data item. With this policy multiple read operations can be carried out concurrently. In other words read access to an item is protected by the S locks. On the other side, the write access is protected by the exclusive locks. In simple words, no other transaction can obtain a lock on a data item which has already been locked by another transaction if the two locks are conflicting. A transaction requesting for a lock at an instant when it cannot be granted, it is blocked by the mechanism until the other transaction releases its lock.
- Hierarchical locking: Practically, the notion of the conflicting locks works at different levels of granularity. Deciding for proper granularity for locking an item generates a locking overhead and might interfere with concurrency. Locking at the granularity of a tuple (one row) allows the system to keep concurrency at the maximum level. The disadvantage of this locking mechanism is that for a transaction to access multiple tuples, it needs to lock all those tuples. This will require issuing same number of calls to the lock manager generating a substantial overhead. This can be avoided by considering a coarser granularity but at the expense of more false conflicts.

The two phase locking is categorized under the pessimistic technique as it assumes that there will be interference among the transactions and takes measures against the same. Optimistic concurrency control provides an alternative to this. With this, the transactions can carry out the operations without having to acquire locks. For ensuring that there is no violation of serializability, a validation phase is performed before the transactions can commit. A number of optimistic protocols have been proposed. The validation process makes sure that the read and write operations of two transactions running concurrently do not conflict. If such a conflict is determined during validation, the transaction is immediately aborted and forced to restart. Thus, for ensuring isolation, optimistic mechanisms rely on restarting the transaction whereas the locking policies use blocking strategy.

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