Durability
Durability is the capacity of data, systems, or other artifacts to persist or remain usable over time despite failures or change.
Kafka for Architects
Durability is the property that acknowledged data survives failures and remains available for later retrieval.
- How it is used
- Frames the choice between protecting acknowledged Kafka data and keeping partitions available during broker failures.
- How deep it goes
- Explains persistence, replication, acknowledgments, offset commits, and failure trade-offs; stops short of a low-level storage walkthrough.
- Examples
- Contrasts payment processing with log collection and compares their data-safety and uptime priorities in a settings table.
- What you can do after
- Operate/configure it by choosing replica, acknowledgment, and offset-commit settings according to acceptable data-loss and downtime risks.
The Art of Code
Durability in code is the capacity to remain understandable, useful, and resilient as the surrounding system changes over time.
- How it is used
- Frames durability as the shared purpose of design, resilience, testing, and trustworthy interfaces.
- How deep it goes
- Explains durability through architecture, separation of concerns, resilience, testing, and public contracts, with practical design guidance.
- Examples
- Uses spaghetti, lasagna, ravioli, hexagonal architecture, null checks, explicit absence, APIs, and semantic versioning.
- What you can do after
- Build durable systems by separating concerns, limiting coupling, preserving behavior, and stabilizing public contracts.
Amazon Web Services in Action, Third Edition: An in-depth guide to AWS
Durability is the likelihood that stored data remains intact and recoverable over a specified period despite failures.
- How it is used
- Guides storage and recovery decisions for AWS web applications facing instance or Availability Zone failures.
- How deep it goes
- Defines durability, contrasts it with availability, and explains EBS recovery strategies without detailing durability algorithms or guarantees beyond examples.
- Examples
- Uses EBS-hosted Jenkins, cross-zone instance replacement, EBS snapshots, S3, and EFS file storage as concrete examples.
- What you can do after
- Evaluate storage choices and configure recovery using managed services, EBS snapshots, AMIs, or multi-zone file storage.
Think Distributed Systems
Durability is a transaction property requiring committed effects to persist despite subsequent failures.
- How it is used
- Protects committed transaction results and dependable promises in business processes.
- How deep it goes
- Defines permanence after commit and contrasts reliable theoretical systems with failure-prone practical systems.
- Examples
- Uses payment confirmation followed by shipping as its concrete failure scenario.
- What you can do after
- Evaluate whether committed transactions can safely support later actions despite component failure.
MongoDB 8.0 in Action, Third Edition
Data durability is the property that committed data remains recoverable after process termination or system failure.
- How it is used
- Protects committed MongoDB transaction data during crashes while supporting reliable applications and high-availability deployments.
- How deep it goes
- Defines durability through ACID, explains journal-and-checkpoint recovery, and extends treatment to replication and concern configuration.
- Examples
- Uses a flight-and-hotel booking, journal replay after an unexpected exit, replica failures, and majority concern settings.
- What you can do after
- Configure journaling-related storage settings, replication, and read/write concerns for application-specific durability and availability requirements.
Read how MongoDB 8.0 in Action, Third Edition explains durability →
Apache Kafka in Action
Data durability is the ability of stored data to remain preserved and recoverable despite failures or restarts.
- How it is used
- Frames Kafka reliability around preserving stored data through replication and producer mechanisms during broker failures.
- How deep it goes
- Defines durability, distinguishes it from availability and consistency, and previews replication, acknowledgments, idempotence, and transactions without detailing their configuration.
- Examples
- Uses a three-broker cluster, a sudden broker failure, and horizontal scaling as concrete durability scenarios.
- What you can do after
- Evaluate how replication and failure-oriented design preserve Kafka data when individual brokers fail.
Related concepts
- ACIDpart of · ACID is the set of database transaction properties—atomicity, consistency, isolation, and durability—that characterize reliable transactional behavior.
- uptimecontrasts with · Uptime is the duration or proportion of a specified period during which a system or service is operational and available to provide its intended function.
- acks=0measured by · acks=0 is a Kafka producer setting that requires no acknowledgment from the broker and provides no delivery or persistence guarantee.
- write-behind cachingcontrasts with · Write-behind caching is a technique that records updates in a cache, acknowledges them, and later propagates them to a backing store.
- replicationaddressed by · Replication is the creation or maintenance of multiple copies of data, state, or functionality across separate locations or components.
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