Concurrency Control

Concurrency control determines how simultaneous operations on shared data interact. Without appropriate coordination, two workers can read the same value, calculate separate updates, and overwrite each other, producing a lost update. Other problems include reading uncommitted changes or making decisions from a view of data that does not protect the application's business rules. The objective is not to prevent all parallel access, but to allow safe overlap while controlling conflicting operations.

Pessimistic concurrency control reserves access before completing an operation, typically through database locks. Depending on the engine and operation, locks can apply to rows, key ranges, pages, or entire tables. Shared and exclusive locks have compatibility rules that determine which operations can proceed together. A lock does not necessarily prevent every kind of access: a row locked against modification may remain readable through a consistent snapshot. Locking behavior must therefore be evaluated against the selected engine, statement, and isolation level.

For example, an application using PostgreSQL on Amazon RDS can begin a transaction, read an inventory row with SELECT FOR UPDATE, verify availability, update the quantity, and commit. Another transaction attempting to modify that row generally waits until the first transaction ends. Ordinary snapshot-based reads can still proceed. An atomic conditional update that decrements stock only when sufficient inventory remains can often achieve the same business requirement with fewer application round trips; the application must check whether the update affected a row.

Optimistic concurrency control allows work to proceed without reserving the data in advance, then verifies that the relevant state has not changed. A worker can read a record and its version, then update it only if the stored version still matches. If another worker has already changed the record, the condition fails and the application must reread and reconsider the operation. This approach works well when conflicts are uncommon, but repeated retries can become expensive under heavy contention.

Transaction isolation defines which effects of concurrent transactions are visible. Multiversion concurrency control maintains versions so that readers can often access a consistent snapshot without blocking writers. However, a stable snapshot does not automatically provide serializable execution or protect every cross-row business rule. Stronger isolation can prevent additional anomalies, sometimes by aborting conflicting transactions rather than making them wait. Applications must handle these failures explicitly.

In Amazon Redshift, concurrent analytical queries and data-loading transactions interact through snapshots, transaction isolation, and table locks. Redshift supports SNAPSHOT and SERIALIZABLE isolation; engineers should verify the database's configured level rather than assume it matches another database. An explicit LOCK command can reserve a table against conflicting access within a transaction, but broad locking can delay ingestion and reporting. Use it when coordination is necessary, not as a routine substitute for understanding transactional behavior.

Operationally, keep transactions short, acquire multiple locks in a consistent order, and avoid holding locks while waiting for external services. Monitor lock waits, blocking sessions, deadlocks, and transaction aborts. A deadlock occurs when transactions wait cyclically for resources; an engine commonly resolves it by aborting a participant. Roll back and retry the complete transaction when appropriate, using bounded backoff and idempotent handling of external side effects.

Concurrency control is not authorization: database locks coordinate legitimate operations but do not replace permissions or encryption. It is also distinct from limiting the number of running jobs, although reducing overlapping writers can reduce contention. For data-store selection, match lock granularity, isolation guarantees, conflict handling, and workload characteristics to the required correctness and performance rather than assuming that a managed database removes concurrency concerns.