What is concurrency control with respect to databases?

Concurrency control is a technique applied in the fields of operating systems, databases, computer programming, and multi processing etc. for ensuring that concurrent operations produce the correct results in as less time as possible. Both the hardware and the software parts of the computer are made up of smaller modules and components where each of them is designed and programmed respectively to work correctly according to some consistency rules. There is concurrent interaction between these components through messages, shared data, which can lead to a result which is in violation of those rules.
The basic idea of concurrency control is to provide methodologies, theories and rules for enforcing consistency in the whole system. Implementation of concurrency control reduces the performance because we apply some constraints on the components which does have the effect of reducing the overall speed. However one thing that should be taken care of is to achieve consistency with as much efficiency as possible and without reducing the performance below minimum levels.
The drawbacks of concurrency control include additional complexity and generation of more overhead in using a concurrent algorithm. The concurrent algorithms generate more overhead when compared to their sequential algorithm counterparts. If the concurrency control mechanism fails, it can lead to torn read and write operations and can corrupt the data.
In this article we talk about concurrency with respect to the databases. Concurrency control is implemented in DBMS, distributed applications etc. for ensuring that the concurrent data transactions are accomplished without causing damage to the data integrity. Distributed applications include cloud computing and grid computing. Concurrency control is also used in some other transactional objects.
It is an essential part of the systems where two or more transactions overlap over the same time instant and can operate on the same data. This happens in almost any general purpose database management system.
Since the advent of database systems, research has been going on related to this concept. Serializability theory is the best established theory that helps define the concept of concurrency control.  This theory also lets us in designing as well as analyzing the concurrency control methods as well as mechanism as effectively as possible.  There is another theory that does not emphasize upon concurrency control over the abstract data types but rather over atomic transactions. However this theory though having a wider scope and more refined, it adds more complexity to the system. Both the theories have their advantages and disadvantages. Merging these two theories might help because they are complementary to some extent.
For ensuring proper concurrency control and correct execution of the transactions, only the serializable transactions and schedules are generated by the system and executed. In some cases, the serializability might be relaxed intentionally by the system for increasing the performance. But, this is done only in those cases where it won’t generate incorrect output.
There are many cases when the transactions fail. Here the system needs to have a recoverability property for recovering from the damage. A good database system also ensures that the result of the transactions that have committed is not lost if the system is switched off accidentally or crashes. On the other hand it also ensures that the incomplete results of the aborted transactions are erased and the actions are rolled back. The ACID rules (mentioned below) characterize the transactions:
- Atomicity: Each thread consists of a single transaction.
- Consistency: This characteristic depends largely on user.
- Isolation: Every transaction should be executed in isolation i.e., should not interfere with others.
 - Durability: The results of the committed actions should persist.
 Nowadays as, database systems are becoming more distributed, the focus is more upon the distribution of the concurrency control mechanism.  

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.