- Graph database
- Optimistic concurrency control
- Non-lock concurrency control
- Concurrency control
- Timestamp-based concurrency control
- Isolation (database systems)
- Speculative execution
- H. T. Kung
- HTTP ETag
- OCC
- Long-lived transaction
- Optimistic concurrency control - Wikipedia
- Difference Between Pessimistic Approach and Optimistic
- What is an optimistic concurrency control in DBMS?
- Database transactions and optimistic concurrency control in …
- Concurrency Control Techniques - GeeksforGeeks
- Lecture 11: Optimistic Concurrency Control - MIT OpenCourseWare
- Concurrency Control in Distributed Transactions
- Lecture 15: Optimistic Concurrency Control - gatech.edu
- 6.830 Lecture 13 -- Optimistic Concurrency Control
- Lecture #9: Optimistic Methods for Concurrency Control
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Optimistic concurrency control (OCC), also known as optimistic locking, is a non-locking 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. Optimistic concurrency control was first proposed in 1979 by H. T. Kung and John T. Robinson.
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, in which case other concurrency control methods may be better suited. 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
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 time-of-check to time-of-use 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 is 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 response to an initial GET request can include an ETag for subsequent PUT requests to use in the If-Match header. Any PUT requests with an out-of-date ETag in the If-Match header can then be rejected.
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 may include 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.
Bugzilla uses OCC; edit conflicts are called "mid-air collisions".
The Ruby on Rails framework has an API for OCC.
The Grails framework uses OCC in its default conventions.
The GT.M database engine uses OCC for managing transactions (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.
Most revision control systems support the "merge" model for concurrency, which is OCC.
Mimer SQL is a DBMS that only implements optimistic concurrency control.
Google App Engine data store uses OCC.
The Apache Solr search engine supports OCC via the _version_ field.
The Elasticsearch search engine updates its documents via OCC. Each version of a document is assigned a sequence number, and newer versions receive higher sequence numbers. As changes to a document arrive asynchronously, the software can use the sequence number to avoid overriding a newer version with an old one.
CouchDB implements OCC through document revisions.
The MonetDB column-oriented database management system's transaction management scheme is based on OCC.
Most implementations of software transactional memory use OCC.
Redis provides OCC through WATCH command.
Firebird uses Multi-generational architecture as an implementation of OCC for data management.
DynamoDB uses conditional update as an implementation of OCC.
Kubernetes uses OCC when updating resources.
YugabyteDB is a cloud-native database that primarily uses OCC.
Firestore is a NoSQL database by Firebase that uses OCC in its transactions.
Apache Iceberg uses OCC to update tables and run maintenance operations on them.
See also
Server Message Block#Opportunistic locking
References
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. S2CID 61600099.
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.{{cite conference}}: CS1 maint: location (link)
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Optimistic concurrency control - Wikipedia
Optimistic concurrency control (OCC), also known as optimistic locking, is a non-locking 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.
Difference Between Pessimistic Approach and Optimistic
Sep 16, 2024 · What Does Optimistic Concurrency Control mean? Optimistic concurrency control is formulated on postulates that assume that conflicts between transactions are unlikely. It lets transactions go ahead without locking records and involves a conflict check only at the time of committing the transaction.
What is an optimistic concurrency control in DBMS?
Jul 6, 2021 · The optimistic concurrency control has three phases, which are explained below −. Read Phase. Various data items are read and stored in temporary variables (local copies). All operations are performed in these variables without updating the database. Validation Phase
Database transactions and optimistic concurrency control in …
Optimistic concurrency control. Optimistic concurrency control allows you to prevent lost updates and deletes. Concurrent, conflicting operations are subjected to the regular pessimistic locking of the database engine hosted by the logical partition that owns the item.
Concurrency Control Techniques - GeeksforGeeks
Dec 28, 2024 · Techniques like lock-based protocols, timestamp ordering, and optimistic concurrency control ensure that database transactions remain consistent, even when multiple transactions access the same data concurrently. These methods prevent problems such as deadlocks, dirty reads, and lost updates.
Lecture 11: Optimistic Concurrency Control - MIT OpenCourseWare
How would you implement optimistic concurrency control in SimpleDB? Readings and discussion questions for a lecture on concurrency control and optimistic concurrency control.
Concurrency Control in Distributed Transactions
Apr 17, 2024 · Optimistic Concurrency Control (OCC) The problem with pessimistic concurrency control systems is that, if a transaction acquires a lock on a resource so that no other transactions can access it. This will result in reducing concurrency of the overall system.
Lecture 15: Optimistic Concurrency Control - gatech.edu
•Txns read and write objects without locks. •Every object X is tagged with timestamp of the last txn that successfully did read/write: W −TS(X) – Write timestamp on X R−TS(X) – Read timestamp on X •Check timestamps for every operation: If txn tries to access an objectfrom the future, it aborts and restarts. 4 / 57.
6.830 Lecture 13 -- Optimistic Concurrency Control
Optimistic concurrent control (today's topic.) What's the idea? Locking is "pessimistic" in the sense that it acquires locks on things that do not actually conflict! Example: T2 ---RA might "get lucky", actually end up doing RA1, WA1, RA2, WA2... WA.
Lecture #9: Optimistic Methods for Concurrency Control
Optimistic concurrency control provides a low overhead way to enforce consistency in systems where conflicts are rare. The authors make an observation that many workloads, such as read-heavy systems or large B-tree indexes, do exhibit this good property, allowing databases to scale up much better with higher concurrency. ...