Optimistic concurrency control

Optimistic concurrency control (OCC) is a concurrency control method applied to transactional systems such as relational database management systems and software transactional memory. OCC assumes that multiple transactions can frequently complete without interfering with each other. While running, transactions use data resources without acquiring locks on those resources. Before committing, each transaction verifies that no other transaction has modified the data it has read. If the check reveals conflicting modifications, the committing transaction rolls back and can be restarted.[1] Optimistic concurrency control was first proposed by H.T. Kung and John T. Robinson.[2]

OCC is generally used in environments with low data contention. When conflicts are rare, transactions can complete without the expense of managing locks and without having transactions wait for other transactions' locks to clear, leading to higher throughput than other concurrency control methods. However, if contention for data resources is frequent, the cost of repeatedly restarting transactions hurts performance significantly; it is commonly thought that other concurrency control methods have better performance under these conditions. However, locking-based ("pessimistic") methods also can deliver poor performance because locking can drastically limit effective concurrency even when deadlocks are avoided.

Phases of Optimistic concurrency control

More specifically, Optimistic concurrency control transactions involve these phases:

• Begin: Record a timestamp marking the transaction's beginning.
• Modify: Read database values, and tentatively write changes.
• Validate: Check whether other transactions have modified data that this transaction has used (read or written). This includes transactions that completed after this transaction's start time, and optionally, transactions that are still active at validation time.
• Commit/Rollback: If there is no conflict, make all changes take effect. If there is a conflict, resolve it, typically by aborting the transaction, although other resolution schemes are possible. Care must be taken to avoid a TOCTTOU bug, particularly if this phase and the previous one are not performed as a single atomic operation.

Web usage

The stateless nature of HTTP makes locking infeasible for web user interfaces. It's common for a user to start editing a record, then leave without following a "cancel" or "logout" link. If locking is used, other users who attempt to edit the same record must wait until the first user's lock times out.

HTTP does provide a form of built-in OCC: The GET method returns an ETag for a resource and subsequent PUTs use the ETag value in the If-Match headers; while the first PUT will succeed, the second will not, as the value in If-Match is based on the first version of the resource.[3]

Some database management systems offer OCC natively - without requiring special application code. For others, the application can implement an OCC layer outside of the database, and avoid waiting or silently overwriting records. In such cases, the form includes a hidden field with the record's original content, a timestamp, a sequence number, or an opaque token. On submit, this is compared against the database. If it differs, the conflict resolution algorithm is invoked.

Examples

• MediaWiki's edit pages use OCC.[4]
• Bugzilla uses OCC; edit conflicts are called "mid-air collisions".[5]
• The Ruby on Rails framework has an API for OCC.[6]
• The Grails framework uses OCC in its default conventions.[7]
• The GT.M database engine uses OCC for managing transactions[8] (even single updates are treated as mini-transactions).
• Microsoft's Entity Framework (including Code-First) has built-in support for OCC based on a binary timestamp value.[9]
• Mimer SQL is a DBMS that only implements optimistic concurrency control.[10]
• Pyrrho is a DBMS that uses optimistic concurrency control.[11]^:30
• Google App Engine data store uses OCC.[12]
• The Apache Solr search engine supports OCC via the _version_ field.[13]
• The Elasticsearch search engine supports OCC via the version attribute.[14]
• CouchDB implements OCC through document revisions.
• The MonetDB column-oriented database management system's transaction management scheme is based on OCC.[15]
• Most implementations of software transactional memory use OCC.
• Redis provides OCC through WATCH command.[16]
• MySQL implements OCC in Group Replication configuration.
• Firebird uses Multi-generational architecture as implementation of OCC for data management.
• DynamoDB uses Conditional Update as implementation of OCC.
• Kubernetes uses OCC when updating resource.

See also

• Server Message Block#Opportunistic locking

References

1. Johnson, Rohit (2003). "Common Data Access Issues". Expert One-on-One J2EE Design and Development. Wrox Press. ISBN 978-0-7645-4385-2.
2. H.T.Kung, J.T.Robinson (1981). "On Optimistic Methods for Concurrency Control". ACM Transactions on Database Systems.
3. "Editing the Web - Detecting the Lost Update Problem Using Unreserved Checkout". W3C Note. 10 May 1999.
4. Help:Edit conflict
5. "Bugzilla: FAQ: Administrative Questions". MozillaWiki. 11 April 2012.
6. "Module ActiveRecord::Locking". Rails Framework Documentation.
7. "Object Relational Mapping (GORM)". Grails Framework Documentation. Archived from the original on 2014-08-15.
8. "Transaction Processing". GT.M Programmers Guide UNIX Edition.
9. "Tip 19 – How to use Optimistic Concurrency with the Entity Framework". MSDN Blogs. 19 May 2009.
   • Most revision control systems support the "merge" model for concurrency, which is OCC.
10. "Transaction Concurrency - Optimistic Concurrency Control". Mimer Developers - Features. 26 February 2010. Archived from the original on 21 March 2013. Retrieved 6 May 2013.
11. Laiho, Martti; Dervos, Dimitris A.; Silpiö, Kari. SQL Transactions Theory and hands-on exercises (PDF). ISBN 978-952-93-2420-0. Archived (PDF) from the original on 18 September 2017.
12. "The Datastore". What Is Google App Engine?. 27 August 2010.
13. "Updating Parts of Documents". Retrieved 2018-06-28.
14. "Elasticsearch - Guide - Index API". Elasticsearch Guide. 22 March 2012.
15. "Transactions - MonetDB". 16 January 2013.
16. "Transactions in Redis".

External links

• Kung, H. T.; John T. Robinson (June 1981). "On optimistic methods for concurrency control". ACM Transactions on Database Systems. 6 (2): 213–226. CiteSeerX 10.1.1.101.8988. doi:10.1145/319566.319567.
• Enterprise JavaBeans, 3.0, By Bill Burke, Richard Monson-Haefel, Chapter 16. Transactions, Section 16.3.5. Optimistic Locking, Publisher: O'Reilly, Pub Date: May 16, 2006,Print ISBN 0-596-00978-X,
• Hollmann, Andreas (May 2009). "Multi-Isolation: Virtues and Limitations" (PDF). Multi-Isolation (what is between pessimistic and optimistic locking). 01069 Gutzkovstr. 30/F301.2, Dresden: Happy-Guys Software GbR. p. 8. Retrieved 2013-05-16.CS1 maint: location (link)