---
title: "Concurrency Control in DBMS: How Locking, MVCC and Optimistic Strategies Keep Data Consistent"
description: "The guide explains how database management systems coordinate simultaneous transactions to preserve consistency and serializability, preventing dirty reads, lost updates, non-repeatable reads and phantom reads. It compares lock-based control, including shared and exclusive locks and two-phase locking, with MVCC, which gives transactions consistent snapshots and lets readers and writers proceed without blocking. The discussion covers pessimistic and optimistic strategies, isolation levels, deadlocks, retries, lock-granularity trade-offs and MVCC’s storage and garbage-collection costs, including PostgreSQL’s VACUUM. For lakehouse workloads, Delta Lake uses a read-validate-commit process with optimistic concurrency, snapshot isolation and conflict detection for concurrent writes. The source presents Databricks Lakebase as a managed implementation offering optimistic concurrency control, snapshot isolation for reads and write-serializable isolation for writes without lock or deadlock management."
---

# Concurrency Control in DBMS: How Locking, MVCC and Optimistic Strategies Keep Data Consistent

[Databricks](https://yomu.fyi/company/databricks) · Databricks Staff · Apr 21, 2026

**Type:** Explainer

## Summary

The guide explains how database management systems coordinate simultaneous transactions to preserve consistency and serializability, preventing dirty reads, lost updates, non-repeatable reads and phantom reads. It compares lock-based control, including shared and exclusive locks and two-phase locking, with MVCC, which gives transactions consistent snapshots and lets readers and writers proceed without blocking. The discussion covers pessimistic and optimistic strategies, isolation levels, deadlocks, retries, lock-granularity trade-offs and MVCC’s storage and garbage-collection costs, including PostgreSQL’s VACUUM. For lakehouse workloads, Delta Lake uses a read-validate-commit process with optimistic concurrency, snapshot isolation and conflict detection for concurrent writes. The source presents Databricks Lakebase as a managed implementation offering optimistic concurrency control, snapshot isolation for reads and write-serializable isolation for writes without lock or deadlock management.

## Context

Concurrent transactions can interleave reads and writes in ways that produce dirty reads, lost updates, non-repeatable reads, phantom reads and other inconsistent results. The guide frames concurrency control as the mechanism that enforces isolation and serializability while balancing correctness, throughput, blocking, storage overhead and retry costs across different workloads.

## Approach / What changed

The guide compares locking and two-phase locking with multi-version concurrency control, then contrasts pessimistic and optimistic strategies. It also discusses isolation levels, deadlock handling and workload trade-offs before describing Delta Lake’s read-validate-commit process and Lakebase’s managed optimistic concurrency, snapshot isolation and write-serializable isolation.

## Takeaways

- Strict two-phase locking holds all locks until commit, preventing cascading rollbacks but introducing blocking and potential deadlocks.
- MVCC allows readers and writers to proceed independently through consistent snapshots, but requires extra storage, obsolete-version garbage collection and possible retries under write contention.
- Delta Lake validates concurrent writes against the latest table version and can automatically resolve non-overlapping changes, including concurrent MERGE, UPDATE and DELETE operations.

**Tags:** [Databases](https://yomu.fyi/topic/databases), [Delta Lake](https://yomu.fyi/topic/delta-lake), [Lakehouse](https://yomu.fyi/topic/lakehouse), [MVCC](https://yomu.fyi/topic/mvcc)

- Source: [Databricks](https://www.databricks.com/blog/concurrency-control)
- Source URL: https://www.databricks.com/blog/concurrency-control
- Ingested by Yomu: 2026-08-31T03:42:16.313Z

[Read original post](https://www.databricks.com/blog/concurrency-control)