Redundancy
Redundancy is the presence of duplicate or overlapping information, components, or resources, whether intentional or unnecessary.
Machine Learning System Design
The condition in which multiple input features provide overlapping or duplicative information.
- How it is used
- Balances predictive feature quality and organizational resilience against overfitting, maintenance cost, fragility, and inefficient staffing.
- How deep it goes
- Defines redundancy and gives practical balancing guidance, stopping short of mathematical feature analysis or detailed staffing models.
- Examples
- Uses correlated features, reserve team resources, bus-factor calculations, documentation, pair programming, and review practices as concrete cases.
- What you can do after
- Evaluate feature sets and team structures, calculate a bus factor, and choose knowledge-sharing or capacity measures that reduce fragility.
Read how Machine Learning System Design explains redundancy →
Think Like a CTO
Redundancy is the provision of duplicate or alternative components or paths so a system can continue operating when one fails.
- How it is used
- Frames redundancy as a cloud resilience and disaster-recovery decision shaped by acceptable downtime.
- How deep it goes
- Explains failure signs and two-zone recovery constraints, but stops short of a configuration procedure.
- Examples
- Uses cloud data loss, disruptive hardware failure, duplicate recovery servers, and database synchronization across geographic zones.
- What you can do after
- Evaluate/understand whether a proposed redundancy strategy can meet a chosen downtime tolerance at an acceptable cost and complexity.
Amazon Web Services in Action, Third Edition: An in-depth guide to AWS
Redundancy is the provision of multiple interchangeable components or paths so that failure of one does not interrupt service.
- How it is used
- Uses multiple components and paths to remove single points of failure in fault-tolerant web applications.
- How deep it goes
- Explains component replication, Availability Zones, decoupling, and replacement, but stops short of detailed configuration steps.
- Examples
- Uses multiple pie-production lines and EC2 instances distributed behind a load balancer.
- What you can do after
- Apply redundancy by distributing EC2 instances across Availability Zones and connecting them through load balancing or SQS.
Generative AI in Action
A redundancy strategy provides duplicate or alternate system resources so service can continue when a component fails or becomes unavailable.
- How it is used
- Supports reliable, scalable generative AI applications by keeping services available during component, endpoint, or regional failures.
- How deep it goes
- Connects redundancy to multi-region deployment, load balancing, failover, disaster recovery, testing, and monitoring without detailing recovery algorithms.
- Examples
- Uses multi-region model deployments, cloud load balancing, and multiple-model classifiers as concrete redundancy patterns.
- What you can do after
- Operate/configure production AI deployments with alternate resources, planned failover, recovery procedures, monitoring, and availability testing.
Bootstrapping Microservices, Second Edition: With Docker, Kubernetes, GitHub Actions, and Terraform
Redundancy is the use of multiple equivalent instances or components so operation can continue when one fails.
- How it is used
- Maintains continuous microservice service during intermittent instance failures.
- How deep it goes
- Explains replication, load balancing, Kubernetes restarts, and deployment configuration, while deferring performance-focused replication.
- Examples
- Uses a gateway Deployment with three replicas and a load-balanced microservice infrastructure diagram.
- What you can do after
- Operate/configure Kubernetes Deployments with multiple replicas and diagnose failures without losing service continuity.
ScyllaDB in Action
Redundancy is the inclusion of duplicate data or components so that a system can continue operating when one becomes unavailable.
- How it is used
- Supports continued requests and scaling when distributed data holders or components become unavailable.
- How deep it goes
- Explains duplicate copies, holder coordination, unavailable nodes, scaling, and response-count trade-offs.
- Examples
- Uses ABC Data employees, three data holders, an unavailable Charlotte, and one-, two-, or three-holder responses.
- What you can do after
- Evaluate how response requirements balance immediate availability against the newest available data.
Related concepts
- reliabilityproduces · Reliability is the ability of a system or process to perform its intended functions consistently and dependably under specified conditions and over time.
- database normalizationaddressed by · Database normalization is a relational database design technique that organizes data into related tables to reduce redundancy and prevent update or dependency anomalies.
- normalizationaddressed by · Normalization is the process of transforming a value, representation, or data structure to satisfy a specified standard, scale, or structural constraint.
- idempotencyaddressed by · Idempotency is the property of an operation whereby repeating it has the same effective result or externally observable effect as performing it once.
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