Optimistic concurrency control

As explained in Architecting an Apache Iceberg Lakehouse

A concurrency-control approach that allows operations to proceed without locks and detects conflicts when changes are committed.

Optimistic concurrency control lets writers proceed without distributed locks, then validates a proposed table update during an atomic commit. A writer publishes a new snapshot only if the table metadata remains unchanged; otherwise, the commit fails. section 6.3.2 section 7.1.1

Disjoint writes, such as appends to different partitions, can succeed concurrently. Overlapping overwrites can conflict, requiring retries, backoff, and sometimes reevaluation against the latest table state. Frequent commits also accumulate snapshots and manifests, making metadata pruning, compaction, or batching operationally important. section 6.3.2

Figure 7.3 Optimistic commits allow engines to write data and metadata and then determine whether the commit is successful by checking its place in the commit sequence in the metadata before and after writing files and committing. If the projected sequence number changes between the two checks, another transaction has occurred and this one should be retried.

The commit sequence illustrates how an engine writes data and metadata, checks the expected sequence, and retries when another transaction changes the table first. section 7.1.1

How Architecting an Apache Iceberg Lakehouse covers it

How deep it goes

Explains atomic compare-and-swap, conflict conditions, retries, and metadata tradeoffs without descending into implementation code.

Examples

Uses concurrent partition appends, overlapping overwrites, streaming micro-batches, and sequence-number checks as concrete examples.

What you can do after

Diagnose commit conflicts, configure retry and backoff behavior, and balance freshness against metadata growth.

How it connects

Connects atomic commits with table snapshots, catalogs, metadata, partitions, retries, backoff, and concurrent overwrites.

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Also explained in

• 

  liveBookmanning.com
  Java Persistence with Spring Data and Hibernate epubOptimistic concurrency control detects conflicting updates by checking that data has not changed since it was read, typically through version information.
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  Data-Oriented Programming: Reduce software complexityOptimistic concurrency control is a concurrency-control technique that allows operations to proceed without locking and detects or reconciles conflicts before committing changes.

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Related concepts

• lockless techniquekind of · Lock-free is a concurrency progress property that guarantees some operation completes within a finite number of steps despite contention, without relying on mutual-exclusion locks.
• catalogused by · A catalog is an organized collection of identifiable items or records and descriptive information that supports their discovery, browsing, or access.
• automatic version checkingimplements · Automatic version checking, or automatic versioning, tracks entity versions and compares the previously read version with the database version during updates or deletions to detect concurrent modification.
• multi-version concurrency controlincludes · Multi-version concurrency control is a database concurrency-control technique that maintains multiple data versions, allowing transactions to read consistent snapshots while concurrent writes proceed with reduced blocking.
• concurrency controlkind of · Concurrency control coordinates simultaneous operations accessing shared state or resources to preserve correctness, consistency, and valid system behavior.
• lockingalternative to · Locking is a synchronization technique that restricts concurrent access to shared resources or protected operations.

This explainer was generated by Manning's knowledge graph from the text of Architecting an Apache Iceberg Lakehouse. The section links point to the parts of the book it draws on.