Uptime Monitoring

Your users know when your site is down. The question is whether you find out from your monitoring system or from an angry tweet.

Uptime monitoring answers the most fundamental question in operations: Is it working? Through continuous checks from external vantage points, it detects when websites, APIs, and services become unreachable—providing the first alert when problems affect real people.

Why Uptime Monitoring Comes First

Before worrying about performance metrics, error rates, or resource utilization, you need to know whether your service is accessible at all. A website that loads slowly frustrates users. A website that doesn't load at all loses them forever.

The simplicity of uptime monitoring is its strength. While comprehensive monitoring encompasses hundreds of metrics across multiple systems, a single uptime check detects many failure modes at once: server crashes, network connectivity loss, DNS resolution failures, load balancer problems, expired SSL certificates, and application-level failures that prevent responses.

This is high-value detection with minimal configuration. That's why uptime monitoring is the natural starting point for any monitoring strategy.

How It Works

Uptime monitors periodically send requests to your endpoints and evaluate what comes back.

Request generation sends HTTP/HTTPS requests to websites and APIs, establishes TCP connections to network services, or pings servers using ICMP. The monitoring system initiates these requests at configured intervals—every minute, every 30 seconds, whatever frequency balances detection speed with resource usage.

Response evaluation examines what the target returns. For HTTP monitoring, the system checks status codes (200 OK versus 500 Internal Server Error), validates that response time stays within acceptable limits, and optionally verifies that response content contains expected text.

State determination translates responses into discrete states: UP, DOWN, or DEGRADED. Most systems require multiple consecutive failures before declaring a service DOWN, preventing transient network blips from triggering false alerts.

Notification triggers when state changes. A service transitioning from UP to DOWN generates alerts. Returning from DOWN to UP sends recovery notifications. This change-based alerting focuses attention on transitions rather than constantly reporting status.

Check Frequency: The Tradeoffs

How often to check involves real tradeoffs.

Frequent checks (every 30-60 seconds) detect problems quickly, minimizing the window where issues go unnoticed. Faster detection enables faster response, reducing overall downtime impact. But frequent checks consume more monitoring resources and generate more traffic to monitored services.

Moderate intervals (every 2-5 minutes) balance detection speed with efficiency. Most services can tolerate a few minutes between checks without risking prolonged undetected outages.

Infrequent checks (every 10-15 minutes) work for non-critical services where slightly delayed detection is acceptable.

The right interval depends on business impact. Critical revenue-generating services warrant frequent checks. Internal tools might check every 5-10 minutes without consequence.

Why Location Matters

Where monitoring checks originate determines what they can detect.

Multiple locations distinguish between localized network problems and actual service failures. If checks from North America fail while European checks succeed, the problem affects North American connectivity specifically—not the service itself.

User-representative locations reveal the experience your actual users encounter. If most users access your service from Europe and Asia, monitoring from those regions shows what those users see.

Global coverage catches regional routing problems, DNS propagation issues, and geographically-specific failures that single-location monitoring would miss entirely.

Internal versus external monitoring serves different purposes. External monitoring from Internet locations shows what users experience. Internal monitoring from within your data center helps distinguish internal problems from network connectivity issues.

Beyond Up and Down

Uptime monitoring extends beyond binary availability to include performance.

Response time tracking measures how long requests take. A service might technically be "up" while taking 30 seconds to respond—that's degraded service, not acceptable operation.

Performance thresholds define what's acceptable. Checks might warn when responses exceed 2 seconds and fail when exceeding 10 seconds, providing gradual degradation detection.

Percentile analysis reveals the full picture. If 95% of checks complete under 1 second but 5% take over 5 seconds, you have intermittent performance problems even if the average looks fine.

Baselines detect gradual degradation. Response times slowly increasing from 200ms to 800ms over weeks might not trigger threshold alerts, but that's meaningful regression.

Content Verification

Receiving HTTP 200 doesn't guarantee correct operation.

Keyword checking verifies responses contain expected text. A page that loads but displays "Sorry, something went wrong" returns 200 OK—content verification catches this.

Absence checking ensures responses don't contain error indicators. Verifying that "exception" or "error" doesn't appear catches many application failures.

JSON validation for APIs ensures responses contain well-formed data. An API returning HTML error pages instead of JSON has problems despite the 200 status code.

SSL/TLS Certificate Monitoring

HTTPS services depend on valid certificates, and certificates expire.

Expiration tracking warns before certificates become invalid. Warnings typically trigger 30, 14, and 7 days before expiration—enough time to renew without panic.

Chain validation ensures complete certificate chains are served. Missing intermediate certificates cause validation failures in some browsers while working in others.

Protocol monitoring verifies servers support modern TLS versions and avoid deprecated protocols like TLS 1.0.

Multi-Step Monitoring

Some failures only appear in realistic workflows.

Login flows test that users can authenticate—catching session management failures that simple page loads wouldn't detect.

Transaction workflows verify critical business functions work end-to-end. For e-commerce: search, add to cart, proceed to checkout.

API sequences test realistic usage patterns involving multiple calls, detecting problems that appear only when APIs are used in specific combinations.

Alert Configuration

The difference between useful monitoring and overwhelming noise lives in alert configuration.

Confirmation periods wait for multiple consecutive failures before alerting. Requiring 3 failures with 1-minute intervals means problems must persist for 2-3 minutes before triggering alerts—long enough to filter transient issues, short enough to catch real problems.

Severity levels distinguish failure types. Complete unavailability is critical. Slow responses are warnings.

Notification routing sends alerts to appropriate people. Critical production services alert on-call engineers immediately. Development environment failures might just log to a dashboard.

Maintenance windows suppress notifications during planned work. Avoiding 3 AM alerts for scheduled maintenance improves on-call sustainability.

Uptime Calculations and SLAs

Uptime monitoring provides the data for availability commitments.

Uptime percentage divides successful checks by total checks. 1,440 daily checks with 1 failure yields 99.93% daily uptime.

SLA compliance tracks actual performance against commitments. A 99.9% uptime SLA allows roughly 43 minutes of monthly downtime. Monitoring reveals whether you're meeting that target.

Maintenance exclusion removes planned downtime from calculations, ensuring SLA metrics reflect unplanned outages only.

What Uptime Monitoring Catches

Server crashes make services completely unreachable. Uptime checks detect these immediately.

Application errors often appear as 500 status codes or missing expected content.

Database failures manifest as timeouts or degraded response times as connection pools exhaust.

Network issues—DNS failures, routing problems, connectivity loss—prevent requests from reaching servers.

Resource exhaustion causes timeouts as servers struggle to respond.

Configuration errors after deployments break services in ways uptime checks catch immediately.

The Limitations

Uptime monitoring has real constraints.

Surface-level checking might miss deeper problems. A homepage loading correctly doesn't mean all features work.

Single-endpoint focus means checking one page doesn't detect problems elsewhere in the application.

External perspective shows symptoms, not causes. Uptime monitoring tells you something is wrong, not why.

Sample-based detection might miss brief outages. A 30-second outage could escape entirely between 1-minute checks.

These limitations don't diminish uptime monitoring's value—they define its role. Uptime monitoring is the foundation, not the entire building. It answers the first question ("Is it working?") so you can move on to deeper questions about why and how.