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Methods in Concurency Control

Many methods for concurrency control exist. Most of them can be implemented within either main category above. The major methods,[1] which have each many variants, and in some cases may overlap or be combined, are:

  • Locking (e.g., Two-phase locking – 2PL) – Controlling access to data by locks assigned to the data. Access of a transaction to a data item (database object) locked by another transaction may be blocked (depending on lock type and access operation type) until lock release.
  • Serialization graph checking (also called Serializability, or Conflict, or Precedence graph checking) – Checking for cycles in the schedule’s graph and breaking them by aborts.
  • Timestamp ordering (TO) – Assigning timestamps to transactions, and controlling or checking access to data by timestamp order.
  • Commitment ordering (or Commit ordering; CO) – Controlling or checking transactions’ chronological order of commit events to be compatible with their respective precedence order.

Other major concurrency control types that are utilized in conjunction with the methods above include:

  • Multiversion concurrency control (MVCC) – Increasing concurrency and performance by generating a new version of a database object each time the object is written, and allowing transactions’ read operations of several last relevant versions (of each object) depending on scheduling method.
  • Index concurrency control – Synchronizing access operations to indexes, rather than to user data. Specialized methods provide substantial performance gains.
  • Private workspace model (Deferred update) – Each transaction maintains a private workspace for its accessed data, and its changed data become visible outside the transaction only upon its commit (e.g., Weikum and Vossen 2001). This model provides a different concurrency control behavior with benefits in many cases.

Major goals of concurrency control mechanisms

Concurrency control mechanisms firstly need to operate correctly, i.e., to maintain each transaction’s integrity rules (as related to concurrency; application-specific integrity rule are out of the scope here) while transactions are running concurrently, and thus the integrity of the entire transactional system. Correctness needs to be achieved with as good performance as possible. In addition, increasingly a need exists to operate effectively while transactions are distributed over processes, computers, and computer networks. Other subjects that may affect concurrency control are recovery and replication.


Concurrency control typically also ensures the Recoverability property of schedules for maintaining correctness in cases of aborted transactions (which can always happen for many reasons). Recoverability (from abort) means that no committed transaction in a schedule has read data written by an aborted transaction. Such data disappear from the database (upon the abort) and are parts of an incorrect database state. Reading such data violates the consistency rule of ACID. Unlike Serializability, Recoverability cannot be compromised, relaxed at any case, since any relaxation results in quick database integrity violation upon aborts. The major methods listed above provide serializability mechanisms. None of them in its general form automatically provides recoverability, and special considerations and mechanism enhancements are needed to support recoverability.


With the fast technological development of computing the difference between local and distributed computing over low latency networks or buses is blurring. Thus the quite effective utilization of local techniques in such distributed environments is common, e.g., in computer clusters and multi-core processors. However the local techniques have their limitations and use multi-processes (or threads) supported by multi-processors (or multi-cores) to scale. This often turns transactions into distributed ones, if they themselves need to span multi-processes. In these cases most local concurrency control techniques do not scale well.

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