Availability is the ability of a system to be working and ready to use whenever it is needed, without unexpected problems or shutdowns.
Availability means your system is up and working when users need it. If users try to access your application and it is down, unavailable, or broken, that is poor availability.
Think of availability like a store is opening hours. A store that is reliably open from 9 AM to 9 PM has good availability during those hours. A store that randomly closes mid-day or has unpredictable hours has poor availability.
In tech, we measure availability as a percentage: How much time is the system working versus total time?
User Trust: Users abandon unreliable services. If your app is frequently down, users will find alternatives.
Revenue Impact: Downtime directly costs money. Amazon loses an estimated $220,000 per minute of downtime. E-commerce, banking, and payment systems lose sales with every second of unavailability.
Reputation Damage: One major outage makes headlines. Facebook is 6-hour outage in 2021 was global news and significantly damaged trust.
Legal Consequences: Some industries have regulatory requirements for availability. Financial systems, healthcare applications, emergency services must meet strict uptime guarantees.
Availability is calculated as:
Availability = (Uptime / Total Time) × 100
Common targets are expressed in "nines":
99% Availability ("two nines"): 3.65 days downtime per year 99.9% Availability ("three nines"): 8.76 hours downtime per year ("four nines"): 52.56 minutes downtime per year ("five nines"): 5.26 minutes downtime per year
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Ensure each component can scale, survive failures, and stay available independently.
Lets put it all together with a practical checklist for evaluating any system design.
The difference between 99% and 99.99% seems small but represents a 42x improvement in allowed downtime.
Google Search: Targets 99.99% availability. They can afford approximately 50 minutes of downtime per year. Any more and they miss their SLA.
AWS S3: Promises 99.99% availability. This is why companies trust S3 with critical data - it is almost always accessible.
Banking Apps: Most target 99.95% availability. They can afford roughly 4 hours of planned maintenance per year plus minimal unexpected downtime.
Startups: Often start with 99% or 99.5% availability. As they grow and users depend on them, they invest in improving to 99.9% or higher.
Infrastructure Failures: Servers crash, hard drives fail, network cables get cut, data centers lose power.
Software Bugs: A deployment introduces a bug that crashes the application. This is why testing and gradual rollouts matter.
Human Error: Engineer runs the wrong command and deletes the production database. Configuration mistakes cause outages.
Traffic Spikes: Sudden traffic surge (viral event, sale launch) overwhelms servers that cannot scale fast enough.
Third-Party Dependencies: Your app depends on a payment gateway or authentication service. When it goes down, so does your functionality.
Security Attacks: DDoS attacks flood systems with traffic, making them unavailable to legitimate users.
Redundancy: Run multiple copies of everything. If one server fails, others take over. No single point of failure.
Example: Netflix runs thousands of servers across multiple cloud providers. If some fail, others handle the traffic seamlessly.
Load Balancing: Distribute traffic across multiple servers. When one becomes overloaded or fails, others continue serving requests.
Automatic Failover: When a component fails, the system automatically switches to a backup without human intervention.
Example: Databases have primary and replica servers. If the primary fails, a replica is promoted to primary instantly.
Geographic Distribution: Place servers in multiple regions. If one data center goes down (natural disaster, power outage), others continue serving users.
Health Checks and Monitoring: Constantly monitor system health. Detect failures within seconds and respond automatically.
Graceful Degradation: When parts of the system fail, keep critical functionality working. Maybe image uploads are down, but users can still browse and read.
Example: Twitter during outages sometimes disables video playback but keeps text tweets working.
Higher availability is expensive:
99% → 99.9%: Requires redundant servers, load balancing, monitoring. Roughly doubles infrastructure cost.
99.9% → 99.99%: Requires multi-region deployment, sophisticated failover, 24/7 on-call team. Can triple costs.
99.99% → 99.999%: Requires fully automated operations, extensive testing, chaos engineering. Some of the most expensive infrastructure in tech.
Companies balance availability needs against costs. A side project does not need five nines. A banking system absolutely does.
These terms are related but different:
Availability: Is the system accessible right now?
Reliability: Does the system work correctly when it is available?
A system can be highly available but unreliable (always up but returns wrong data). Or reliable but not available (works perfectly but frequently down).
Both matter. You want systems that are available AND reliable.
SLAs are contracts that guarantee availability:
Provider Commits: "We will maintain 99.95% availability" Customer Expectation: System will be available except for 4.38 hours per year Penalty: If provider misses target, they refund money or provide credits
Cloud providers offer SLAs:
If they miss these targets, customers get service credits. This incentivizes providers to maintain high availability.
Tools to track uptime:
Uptime Monitoring Services: Pingdom, UptimeRobot ping your service every few minutes from around the world. Alert when it is down.
Application Performance Monitoring (APM): Datadog, New Relic monitor internal metrics, catch issues before users notice.
Synthetic Monitoring: Automated tests continuously check critical user flows work.
Real User Monitoring (RUM): Track actual user experience, detect regional issues or slow performance.
Planned Downtime: Scheduled maintenance windows. Communicate with users beforehand, schedule during low-traffic hours.
Example: "System maintenance Sunday 2 AM - 4 AM EST."
Unplanned Downtime: Unexpected outages from failures, bugs, attacks. These damage availability metrics and user trust more than planned maintenance.
High availability systems often achieve zero planned downtime through rolling updates and blue-green deployments.
Technical solutions only go so far. High availability requires:
On-Call Rotations: Engineers available 24/7 to respond to incidents.
Incident Response Procedures: Clear playbooks for common failures. When an outage happens, teams execute practiced responses.
Post-Mortems: After outages, teams analyze what went wrong and how to prevent recurrence. Blameless post-mortems foster learning.
Testing Chaos: Netflix is Chaos Monkey randomly kills servers in production to ensure systems handle failures gracefully.
DevOps/SRE Roles: Site Reliability Engineers specifically focus on availability. They design systems, implement monitoring, respond to incidents.
On-Call Responsibilities: Senior engineers often take on-call shifts. This comes with extra pay but also stress of being available nights/weekends.
Building Resilient Systems: Understanding availability makes you a better engineer. You design systems that handle failures instead of assuming perfect conditions.
Availability is not just uptime - it is user trust, revenue protection, and competitive advantage. Systems that are reliably available win users. Systems that are frequently down lose them.
As a developer, you must consider availability when designing systems. Redundancy, monitoring, graceful degradation, and testing are not optional for production systems. They are the difference between a hobby project and a professional service.
