Concurrency-Control

Overview Both are concurrency-control strategies for preventing lost updates when multiple transactions touch the same data, but they differ in when they deal with conflict. Optimistic locking assumes collisions are rare and only performs a version check at commit time, while pessimistic locking assumes collisions are likely and takes an exclusive lock before any read or write proceeds. Comparison Diagram Optimistic Pessimistic Record (v1) no lock taken Txn A Txn B v1 → v2 committed v1 ≠ v2 conflict, retry conflict caught at commit time Record blocked Txn A holds lock Txn B waiting commits & releases lock acquires lock then runs conflict prevented up front Comparison Table Aspect Optimistic Locking Pessimistic Locking Access phase No lock taken; any transaction can read or begin writing the row immediately Lock acquired (e.g. SELECT FOR UPDATE) before the transaction reads or writes the row Concurrent access Other transactions freely read and write the same row in parallel Other transactions attempting the same row must wait for the lock holder to finish Conflict detection timing Deferred until commit, via a version number, timestamp, or hash comparison Not needed as a separate step; the lock physically prevents overlapping access On conflict Commit is rejected; the transaction is rolled back and typically retried No conflict occurs; the waiting transaction simply blocks until the lock is released Implementation mechanism Application-level version column checked in the UPDATE’s WHERE clause Database-level row or table locks managed by the lock manager Throughput under low contention High; no blocking overhead when collisions are rare Lower; locking overhead is paid even when no real conflict would occur Behavior under high contention Retry storms and wasted work as many transactions repeatedly fail and re-run Orderly queueing keeps correctness but serializes work and limits parallelism Deadlock risk None, since no locks are ever held Possible when transactions acquire multiple locks in inconsistent order Key Differences Optimistic locking detects conflicts at commit time; pessimistic locking prevents them via upfront locking. Optimistic locking never blocks other transactions, it only forces a retry on collision. Pessimistic locking holds an exclusive lock for the transaction’s full duration, serializing access to the row. Optimistic locking carries no deadlock risk because it never holds locks. Pessimistic locking trades raw throughput for predictability when contention is heavy. When to Use Each Optimistic Locking ...

Overview Serializable isolation guarantees that concurrent transactions produce a result equivalent to some serial order, eliminating every possible race condition. Weaker isolation levels like Read Committed or Snapshot trade that guarantee for higher throughput, deliberately allowing certain read/write anomalies that application code must tolerate or guard against. Comparison Diagram SerializableWeaker IsolationT1T2time (T1 fully precedes T2)No overlap in effect =equivalent to a serial runT1T2time (T1 and T2 overlap)Overlap window =dirty/non-repeatable reads,phantoms, or write skew possible Comparison Table Aspect Serializable Weaker Isolation Isolation guarantee Result equivalent to some serial execution of all transactions Allows specific interleavings; guarantee varies by level (Read Committed, Repeatable Read, Snapshot) Concurrency control Full conflict serializability via strict two-phase locking, serializable snapshot isolation (SSI), or predicate locks Row-level locks or MVCC snapshots that only block on narrower conflict sets (e.g. write-write) Anomalies prevented Dirty reads, non-repeatable reads, phantom reads, and write skew all eliminated Only a subset prevented; phantoms and write skew commonly remain possible Contention behavior Higher rate of lock waits, deadlocks, or serialization-failure aborts under concurrent access Readers rarely block writers (MVCC) or lower lock scope, so contention is reduced Throughput impact Lower throughput and higher latency as concurrency increases, especially with hot rows Higher throughput and better scalability under contention Application responsibility Application can assume correctness; only needs to retry on serialization-failure errors Application must reason about anomalies and add explicit checks (e.g. version columns, SELECT FOR UPDATE) Conflict detection timing Detected either at lock-acquisition time or at commit time (optimistic serializable schemes) Detected only for the narrower conflicts the level covers, often just at commit for MVCC writes Default in major databases Rarely the default; must be explicitly requested (e.g. SET TRANSACTION ISOLATION LEVEL SERIALIZABLE) Default in most systems out of the box (PostgreSQL/Oracle default to Read Committed, MySQL InnoDB to Repeatable Read) Key Differences Serializable enforces true serial equivalence; weaker levels only rule out a defined subset of anomalies. Weaker isolation relies on MVCC snapshots or narrow locks, cutting contention compared to serializable’s broader locking or SSI conflict tracking. Under Serializable, correctness bugs shift into retry loops on abort; under weaker levels, they shift into missed application-level checks. Write skew is the classic anomaly Serializable closes that even Snapshot Isolation leaves open. Choosing a level is a runtime trade-off between guaranteed correctness and throughput, not a one-time schema decision. When to Use Each Serializable ...