Uptime
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.
Amazon Web Services in Action, Third Edition: An in-depth guide to AWS
High availability is the property of a system remaining operational with minimal interruption despite component failures.
- How it is used
- Uses uptime as a web application's availability target and high availability as a way to limit interruptions after failures.
- How deep it goes
- Explains component failures, replication, failover, a CloudFormation setting, and monitoring; does not establish that the example architecture achieves its uptime target.
- Examples
- Uses a WordPress uptime target, a Multi-AZ database setting, and a scheduled external website health check.
- What you can do after
- Operate/configure it by enabling RDS Multi-AZ and setting up scheduled checks and alarms for a website.
Read how Amazon Web Services in Action, Third Edition: An in-depth guide to AWS explains uptime →
100 SQL Server Mistakes and How to Avoid Them
High availability is the capability of a system to remain accessible and operational despite failures or maintenance events.
- How it is used
- Protects database access from production-server failures through automatic failover and supports planning against broader availability risks.
- How deep it goes
- Defines HA, contrasts it with DR, and gives planning cautions while stopping short of implementation guidance.
- Examples
- Uses same-site availability groups, geographically separated replicas, data-center loss, off-site backups, and annual DR tests.
- What you can do after
- Evaluate/understand whether a proposed HA or DR design matches failure scope, workload, cost, and tested recovery requirements.
Read how 100 SQL Server Mistakes and How to Avoid Them explains uptime →
Elixir in Action, Third Edition
A high-availability system is a system designed to provide service continuously with minimal downtime.
- How it is used
- Frames concurrency, fault tolerance, distribution, and scalability as production requirements for systems that must provide continuous service.
- How deep it goes
- Explains operational requirements and connects them to BEAM processes, supervision, and distribution, while deferring databases and network partitions.
- Examples
- Uses telephone networks, web applications, concurrent server workloads, a to-do system, and the BEAM process-scheduling model.
- What you can do after
- Evaluate systems against availability requirements and design toward isolation, concurrency, graceful degradation, supervision, and machine-level failover.
Google Anthos in Action
High availability is the property of a system remaining operational with minimal interruption despite component failures or maintenance.
- How it is used
- Frames service reliability as both a measurable availability objective and a deployment requirement for applications that cannot tolerate outages.
- How deep it goes
- Defines availability as response frequency and explains zone-failure recovery, but stops short of a complete multicloud deployment procedure.
- Examples
- Uses financial, health care, and traffic-management applications, a banking outage scenario, and an Anthos multicloud service-mesh architecture.
- What you can do after
- Evaluate/understand it by matching zone or provider redundancy to outage risks and setting a realistic, time-bounded availability objective.
PostgreSQL Mistakes and How to Avoid Them
A system design property that minimizes service interruption and maintains access during failures or maintenance.
- How it is used
- Keeps PostgreSQL accessible during failures and maintenance while minimizing downtime and data loss.
- How deep it goes
- Explains goals, failure scope, backup practices, redundancy, and tool choices without detailing replication or failover internals.
- Examples
- Uses untested backups, single-server deployments, standby replication, full restores, and tools including Barman, pgBackRest, RepMgr, Patroni, and CloudNativePG.
- What you can do after
- Operate/configure a PostgreSQL HA plan with tested backups, redundant copies, standby nodes, and proven failover management.
Read how PostgreSQL Mistakes and How to Avoid Them explains uptime →
Data-Driven Cybersecurity
The degree or proportion of time that a system is operational and accessible when needed.
- How it is used
- Uses uptime to communicate operational continuity and the business effect of security investments.
- How deep it goes
- Defines uninterrupted running time and illustrates executive reporting, but provides no uptime calculation or collection procedure.
- Examples
- Shows a healthcare report pairing 99.9% uptime with response time falling from 24 hours to 4 hours.
- What you can do after
- Evaluate/understand it as an operational signal and report it alongside incident response and business effects.
Kubernetes for Developers
High availability is the property of an application remaining operational despite failures of individual infrastructure components or failure domains.
- How it is used
- Keeps applications operational through node or availability-zone failures by combining replicas with placement across failure domains.
- How deep it goes
- Works through replica-sizing trade-offs and a Deployment topology-spread configuration, without defining a numerical uptime measure.
- Examples
- Uses a 36-worker replica-sizing scenario and a three-replica Kubernetes Deployment spread by node hostname.
- What you can do after
- Operate/configure it by choosing a replica count and setting topologySpreadConstraints when default Pod spreading is insufficient.
Machine Learning System Design
The property that required data and related systems can be obtained or accessed reliably by a system and its engineers.
- How it is used
- Keeps required data ready for a system and frames whether distributed-store requests receive valid responses.
- How deep it goes
- Defines pipeline and distributed-store availability, then contrasts valid responses with guarantees of freshness.
- Examples
- Uses data that must be ready before a system runs and a network partition that forces a consistency-versus-availability choice.
- What you can do after
- Evaluate the tradeoff between returning a potentially stale response and canceling an operation to favor consistency.
Generative AI in Action
The property of a system remaining operational and accessible for a high proportion of time.
- How it is used
- Frames high availability as a production deployment goal for generative AI applications.
- How deep it goes
- Links high availability to redundancy, failover, disaster recovery, load balancing, monitoring, and automated testing.
- Examples
- Uses cloud load balancing and redundancy, alongside failover and disaster-recovery planning, as availability measures.
- What you can do after
- Operate/configure deployments by planning redundancy, failover, disaster recovery, monitoring, and automated testing.
Architecting an Apache Iceberg Lakehouse
High availability is the capability of a system to remain operational with minimal interruption despite component or site failures.
- How it is used
- Keeps lakehouse storage and catalog services accessible during component, network, or localized site failures.
- How deep it goes
- Explains replication, failover, load balancing, recovery coordination, and uptime targets, but stops short of vendor-specific configuration.
- Examples
- Uses active-active availability-zone replication, redundant network paths, load-balanced endpoints, geographic distribution, clustering, and multiregion catalog deployment.
- What you can do after
- Evaluate storage and catalog architectures for failure tolerance, uptime, recovery behavior, scalability, and operational cost.
Read how Architecting an Apache Iceberg Lakehouse explains uptime →
Think Like a CTO
Uptime is the proportion or duration of time during which a system or service is available and operating normally.
- How it is used
- Uses availability to set credible uptime promises for customers and assess the reliability expected from third-party services.
- How deep it goes
- Develops contractual, staffing and monitoring decisions, but does not descend into availability calculations or system implementation.
- Examples
- Uses an EC2 uptime commitment, out-of-hours support staffing, and external checks of login, authentication and database access.
- What you can do after
- Operate/configure external availability checks and make uptime commitments that match support capacity and business need.
Terraform in Depth: Infrastructure as Code with Terraform and OpenTofu
High availability is a system design property that minimizes service interruption by avoiding dependence on a single failure domain.
- How it is used
- Guides state-storage selection and network topology decisions where inaccessible state blocks deployments or a single-location outage could interrupt an application.
- How deep it goes
- Defines uptime, gives percentage conventions and evaluation criteria, then illustrates a configurable network module without deriving an uptime calculation.
- Examples
- Uses four- and five-nines targets, an inaccessible state store, and a Terraform network module configured for three availability zones.
- What you can do after
- Apply it via a Terraform network module by choosing a zone count appropriate to each environment’s availability and cost needs.
Read how Terraform in Depth: Infrastructure as Code with Terraform and OpenTofu explains uptime →
Effective Platform Engineering
High availability is the design of a system to maintain service access despite component failures or maintenance.
- How it is used
- Tests high-availability and resilience configurations outside production to build confidence in platform-service releases.
- How deep it goes
- Distinguishes availability from self-correcting resilience and recommends nonproduction testing, without detailing HA configurations.
- Examples
- Uses demo configurations for metrics-server, kube-state-metrics, and event-exporter to contrast nonproduction and production HA.
- What you can do after
- Evaluate the cost of omitting nonproduction HA against developer blockage and reduced release confidence.
Kafka for Architects
High availability is the property of a system remaining operational despite failures of individual components.
- How it is used
- Frames availability as a nonfunctional requirement and an operational trade-off against data safety.
- How deep it goes
- Explains why partitions go offline when in-sync replicas are insufficient and summarizes settings for favoring durability or availability.
- Examples
- Contrasts financial transactions and payment processing with log collection and real-time analytics dashboards.
- What you can do after
- Configure producer and consumer settings to favor fewer losses or faster recovery, accepting the corresponding availability or durability trade-off.
Data Without Labels
Availability is the degree to which required data can be obtained and accessed for a particular use case without operational or permission barriers.
- How it is used
- Checks whether data needed for a business use case can be obtained and accessed.
- How deep it goes
- Defines access barriers and describes using surrogate variables, without detailing a technical retrieval process.
- Examples
- Uses missing monthly store-visit counts and revenue as a surrogate for demand forecasting.
- What you can do after
- Evaluate whether to proceed with available data or use surrogate variables when required data is unavailable.
Microsoft Azure in Action
High availability is the property of a system remaining accessible and operational for a high proportion of required service time.
- How it is used
- Uses redundancy choices to reduce virtual-machine outages and keep globally distributed Cosmos DB applications operational.
- How deep it goes
- Explains VM fault and update domains, then details Cosmos DB regional failover priorities and zone redundancy.
- Examples
- Uses VM placement options and an e-commerce database configured across australiacentral and eastus.
- What you can do after
- Configure VM redundancy choices and Cosmos DB regions, failover priority, and zone redundancy.
Platform Engineering on Kubernetes
High availability is a system property that minimizes service interruption through redundancy and failure tolerance.
- How it is used
- Uses Kubernetes Helm Charts to provision databases and messaging infrastructure for application services.
- How deep it goes
- Describes Helm-chart selection for provisioning, without detailing redundancy or failure-tolerance mechanisms.
- Examples
- Uses a walking skeleton with Redis for Agenda, PostgreSQL for C4P, and Kafka as its message broker.
- What you can do after
- Operate/configure infrastructure by selecting a suitable Helm Chart for a component to provision in Kubernetes.
Read how Platform Engineering on Kubernetes explains uptime →
Starting Data Analytics with Generative AI and Python
Availability is the likelihood that a system is operational and accessible when needed.
- How it is used
- Frames availability as a performance metric for data-analysis systems that need continuous access for decision-making.
- How deep it goes
- Defines availability as the likelihood of being operational and available when needed, without detailing how to measure it.
- What you can do after
- Evaluate whether a data-analysis system is operational and available when needed.
Read how Starting Data Analytics with Generative AI and Python explains uptime →
Build AI-Enhanced Web Apps
High availability is the capability of a system to remain operational with minimal interruption despite component failures.
- How it is used
- Guides deployment reliability choices by expected request volume, including monitoring uptime and response times.
- How deep it goes
- Lists monitoring, scaling, security, backups, and error tracking for small deployments, then begins expanding requirements for medium traffic.
- Examples
- Uses applications below 10,000 daily requests and names Vercel for monitoring and alerting.
- What you can do after
- Operate/configure basic uptime and response-time monitoring with provider tools such as Vercel.
MongoDB 8.0 in Action, Third Edition
High availability is an operational property in which a system remains accessible despite component failures or maintenance.
- How it is used
- Positions availability as a reliability property of Atlas and as an element of information security.
- How deep it goes
- Defines availability briefly and links it to replica-set redundancy, without detailing failover procedures.
- Examples
- Uses replica sets of mongod processes holding identical data as the concrete availability example.
- What you can do after
- Understand how replica-set redundancy supports high availability in MongoDB.
Read how MongoDB 8.0 in Action, Third Edition explains uptime →
Related concepts
- CIA triadpart of · The CIA triad is an information-security model comprising confidentiality, integrity, and availability as core protection objectives.
- resiliencecontrasts with · Resilience is the capacity of a system or organization to withstand disruption, adapt, recover, or continue functioning acceptably.
- meanhas part · A mean is a measure of central tendency, calculated as the arithmetic average of values or, for a random variable, as the probability-weighted average of its possible values.
- CAP theoremused by · The CAP theorem is a distributed-systems impossibility result stating that consistency, availability, and partition tolerance cannot all be guaranteed during a network partition.
- durabilitycontrasts with · Durability is the capacity of data, systems, or other artifacts to persist or remain usable over time despite failures or change.
- consistencycontrasts with · Consistency is the property or degree to which related data, behavior, outputs, or claims conform to requirements and remain coherent across instances or operations.
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