Why HAProxy Outages Are Invisible Until It's Too Late

See our HAProxy in Production guide for architecture patterns and failure modes.

HAProxy Is Designed to Hide Failure

HAProxy masks problems mechanically. This isn't a bug. It's HAProxy doing exactly what it was designed to do. Partial backend degradation gets smoothed. Retry behavior delays visible errors. Connection reuse absorbs pain. Graceful degradation that isn't actually graceful. Understanding this design intent explains why failures surface late.

On a Tuesday afternoon, the dashboard showed everything green. HAProxy process: running. CPU: 12%. Requests per second: stable at 8,000. Error rate: 0.3%. p50 latency: 48ms. The on-call engineer checked the dashboard twice and saw nothing alarming. Meanwhile, users were reporting 30-second timeouts. Support tickets were flooding in. The p99 latency had silently climbed to 28 seconds, but the dashboard aggregated it away. Queue depth had grown to 15,000 requests, but no one was watching queue depth. When the alert finally fired at T+18 minutes, three backends had already failed completely. The remaining two were handling 400% of their normal load. The outage declaration came at T+22 minutes. The dashboard had been green the entire time.

Partial Backend Degradation Smoothing

When one backend slows, HAProxy routes around it. Traffic shifts to healthy backends. Aggregate metrics stay green. This is HAProxy doing exactly what it was designed to do: maintaining service availability by routing around failures.

In one incident, three of five backends started responding slowly. HAProxy detected the slowness and shifted traffic to the two healthy backends. Aggregate request rates stayed stable because HAProxy continued accepting requests. Aggregate error rates stayed low because most requests succeeded on the two healthy backends. CPU usage stayed normal because HAProxy itself wasn't the bottleneck. The dashboard looked healthy. But the two healthy backends were now handling 250% of their normal load. They started failing minutes later.

The failure wasn't visible until all backends were saturated. By then, queues had built up, tail latency had exploded, and recovery required more than restarting the slow backends. HAProxy had successfully masked the partial failure until it became a total failure.

Retry Behavior Delaying Visible Errors

HAProxy retries failed requests automatically. When a backend returns an error, HAProxy can retry on another backend. This delays visible errors. Users don't see failures immediately. They see retries happening behind the scenes.

We've seen incidents where backends were returning errors, but HAProxy retries masked them. Aggregate error rates stayed low because retries succeeded on other backends, diluting the failure signal. But each retry increased load on healthy backends, accelerating their failure. The errors became visible only when all backends started failing and retries had nowhere to go.

This retry behavior is by design. It improves user experience by hiding transient failures. But it also hides systemic failures until they're severe enough that retries can't mask them anymore. By that point, multiple backends are failing simultaneously.

Connection Reuse Absorbing Pain

HAProxy reuses connections to backends. This improves performance, but it also masks connection-level problems. When a backend connection degrades, HAProxy continues using it until it fails completely. New connections might be healthy, but existing connections carry degraded performance.

In one incident, backend connections started experiencing packet loss. HAProxy continued reusing these degraded connections because they hadn't failed completely. Some requests succeeded, some failed, some timed out. Aggregate error rates stayed below thresholds because enough requests succeeded to keep the average acceptable. Aggregate latency metrics stayed normal because fast requests on new connections balanced slow requests on degraded connections. The problem became visible only when connection reuse couldn't mask the degradation anymore, when degraded connections outnumbered healthy ones.

Graceful Degradation That Isn't Actually Graceful

HAProxy degrades gracefully by design. When backends fail, traffic continues on remaining backends. This feels like graceful degradation, but it's actually concentration of risk. The remaining backends handle more load, increasing their failure probability.

This graceful degradation masks the underlying problem. Teams see service continuing and assume everything is fine. But the system is operating closer to failure. When the next backend fails, the remaining backends fail faster. What looks like graceful degradation is actually cascading failure in slow motion.

The Green Dashboard Fallacy

Common signals stay green while failures build. Process up. CPU low. Requests per second stable. Error rates diluted. p50 latency flat. These metrics lie, not because they're wrong, but because they measure the wrong things at the wrong granularity.

Process Up

The HAProxy process can be running while the service is degraded. We've seen incidents where HAProxy was running, but all backends were down. We've seen incidents where HAProxy was running, but queues were saturated. Process monitoring tells you the binary is executing. It doesn't tell you if HAProxy is serving traffic. The process is up, but the system is failing.

CPU Low

HAProxy is efficient. CPU usage stays low even under load. Low CPU doesn't mean everything is fine. It means HAProxy isn't the bottleneck. The bottleneck is often backends, queues, or network. Low CPU masks problems by making the load balancer look healthy while backends fail. HAProxy's efficiency becomes a blindness mechanism.

Requests Per Second Stable

Request rates can stay stable while failures build. When backends slow down, HAProxy continues accepting requests at the same rate. Request rates don't drop. They queue. Stable request rates feel reassuring, but they mask queue buildup. Requests are arriving, but they're not completing. The metric looks healthy because it measures arrival rate, not completion rate.

Error Rates Diluted

Aggregate error rates get diluted by averaging across backends. When some backends fail, HAProxy routes around them. Total error rates stay low because most requests succeed on healthy backends, averaging out failures. But the failing backends are returning 100% errors. Aggregate metrics hide per-backend failures. In one incident, two of five backends were returning errors. Aggregate error rate was 2%, below alert thresholds, because three healthy backends averaged out two failing backends. By the time aggregate error rates spiked, all backends were failing and aggregation could no longer hide it.

p50 Latency Flat

Median latency can stay flat while tail latency explodes. When some requests are fast and some are slow, p50 stays normal because it aggregates fast requests on healthy backends with slow requests on degraded backends. But p95 and p99 latency can spike. Teams monitoring p50 see green dashboards while users experience timeouts. We've seen incidents where p50 latency was 50ms while p99 latency was 5 seconds. The dashboard showed green because p50 was normal. But 1% of users experienced 5-second delays. By the time p50 started increasing, queues were saturated and recovery was harder. The aggregation hid tail latency until it was severe enough to affect the median.

The Blindness Progression We See Repeatedly

One Backend Slows

One backend starts responding slowly. Response times increase from 50ms to 500ms. HAProxy detects the slowness and routes new requests to other backends. Aggregate metrics stay green because aggregate request rates include healthy backends, aggregate error rates stay low because most requests succeed on healthy backends, and aggregate latency stays normal because fast requests on healthy backends average out slow requests. The slow backend continues receiving some traffic, but most traffic shifts away.

Retries Mask It

HAProxy retries failed requests on other backends. When the slow backend times out, retries succeed on healthy backends. Aggregate error rates stay low because retries dilute failures across multiple backends. Users don't see failures immediately because retries delay visible errors. The slow backend continues degrading, but retries mask the problem until healthy backends start failing under increased load.

Queues Grow Quietly

Requests queue in HAProxy while waiting for backends. Queue depth increases from 10 to 100 to 1000. But aggregate request rates stay stable because HAProxy continues accepting requests at the same rate. Aggregate error rates stay low because requests haven't failed yet. They're just queued. The dashboard looks healthy. Queue depth isn't monitored, so the problem isn't visible until queues saturate.

Tail Latency Explodes

p95 latency increases from 100ms to 2 seconds. p99 latency increases from 200ms to 5 seconds. But p50 latency stays flat at 50ms because median latency aggregates fast requests on healthy backends with slow requests on degraded backends. Teams monitoring p50 see green dashboards. Users experiencing p99 latency see timeouts. The aggregation hides tail latency until it's severe enough to affect the median.

Health Checks Still Pass

Health checks continue passing. The slow backend responds to health checks, even though it's slow for real traffic. Health check intervals are longer than request timeouts, so health checks don't detect the slowness. Health checks use simpler requests than real traffic, so they succeed even when real requests fail. Backends stay marked as healthy because health checks measure availability, not performance.

Users Complain Before Alerts Fire

Users report timeouts. Support tickets increase. But alerts haven't fired yet. Aggregate error rates are still below thresholds because aggregation dilutes failures. p50 latency is still normal because aggregation smooths out tail latency. Alerts fire minutes after users start complaining, when aggregation can no longer hide the problem. By then, queues are saturated and recovery is harder.

Why Load Balancers Fail Differently Than Services

Load balancers are in the traffic path. They absorb and smooth failure. They delay visible collapse. This is conceptually different from application failures. Understanding this difference explains why load balancer outages feel different.

In the Traffic Path

Load balancers sit between users and services. All traffic flows through them. When they mask failures, they mask them for all users. When they fail, all users are affected. This central position amplifies both masking and failure.

Absorbing and Smoothing Failure

Load balancers absorb failure by routing around it. They smooth failure by distributing load across remaining backends. This absorption and smoothing delays visible collapse because aggregate metrics stay green: aggregate request rates stay stable, aggregate error rates stay low, aggregate latency stays normal. Services might be failing, but the load balancer makes it look like they're not by aggregating failures away.

Delaying Visible Collapse

Load balancers delay visible collapse by maintaining service availability through aggregation. They route around failures until they can't, and aggregation hides the routing until it fails. This delay makes failures feel sudden when they finally become visible. But the failure was building the whole time. It was just invisible because aggregation masked it.

Why "More Monitoring" Usually Makes This Worse

Adding Dashboards Increases Confidence, Not Safety

More dashboards create the illusion of visibility. Teams see more metrics and assume they have better visibility. But more metrics don't mean better signals. They mean more noise. Teams become more confident in their monitoring while missing the same failures. We've seen teams add 20 new metrics after outages, feeling more prepared, but monitoring the same aggregates that missed the previous outage. The next outage still feels sudden because the new dashboards show the same green signals: the same aggregates that smooth out problems.

Teams Trust the Wrong Aggregates

Aggregate metrics smooth out problems by averaging across backends, routes, and time windows. Teams trust aggregates because they look stable. But aggregates hide per-backend failures by averaging healthy and failing backends, hide per-route failures by averaging healthy and failing routes, and hide tail latency spikes by averaging fast and slow requests. Teams see green aggregates and assume everything is fine. In one incident, aggregate error rate was 0.5% because three healthy backends averaged out two backends returning 50% errors. Teams trusted the aggregate and missed the failing backends. By the time aggregate error rates spiked, all backends were failing and aggregation could no longer hide it.

Alert Fatigue Sets In Before Meaningful Signals Fire

More alerts mean more noise. Teams tune alerts to reduce noise: increasing thresholds, adding delays, requiring multiple conditions. Meaningful signals get tuned out with the noise. We've seen teams with 50 alerts firing constantly. They tune them down. They increase thresholds. They add delays. By the time meaningful signals fire, teams ignore them because they're used to alerts being noise. The meaningful signal gets lost in the noise, and the outage declaration comes from users, not alerts.

How Teams Actually Reduce Blindness

Teams reduce blindness by focusing on fewer, sharper signals. Queue-centric thinking. Leading indicators over aggregates. Explicit failure budgets. This section focuses on approaches, not tools.

Fewer, Sharper Signals

Teams reduce blindness by monitoring fewer signals, not more. They choose signals that reveal problems early. Queue depth. Per-backend health. Tail latency. These signals catch failures before aggregates mask them.

We've seen teams reduce dashboards from 20 to 3. They monitor queue depth, per-backend error rates, and p99 latency. These three signals catch failures that 20 metrics missed. Fewer signals mean less noise, more focus, better visibility.

Queue-Centric Thinking

Queue depth is the earliest failure signal. When backends slow, queues build. Queue depth increases before error rates spike. Teams that monitor queue depth catch failures before they become outages.

Queue-centric thinking means understanding that requests queue before they fail. If queues are building, backends are slowing. If queues are saturated, backends are failing. Monitoring queue depth reveals problems that other metrics hide.

Leading Indicators Over Aggregates

Leading indicators reveal problems before aggregates degrade. Per-backend metrics reveal problems before aggregate metrics. Tail latency reveals problems before average latency. Leading indicators catch failures early.

Teams that monitor leading indicators see problems coming. They see one backend slowing before aggregate metrics degrade. They see tail latency spiking before average latency increases. Leading indicators give teams time to respond before outages occur.

Explicit Failure Budgets

Failure budgets make blindness explicit. Teams define acceptable failure rates. They monitor against these budgets. When budgets are exceeded, teams know they have a problem, even if dashboards look green.

Explicit failure budgets force teams to look beyond green dashboards. If p99 latency exceeds budget, there's a problem, even if p50 latency is normal. If per-backend error rates exceed budget, there's a problem, even if aggregate error rates are low. Failure budgets reveal problems that green dashboards hide.

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