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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Frequent deployments can quietly become a service’s unofficial restart policy: replacing instances clears accumulated symptoms, but leaves the defect untouched. Sergey Shinder describes how that pattern concealed a memory leak until an internal reconciliation service went months without a deploy. His account offers a practical warning for teams monitoring long-running services: track memory growth in relation to instance age, and test what happens when an instance is allowed to live.
How routine deployments hid the failure
In Shinder’s account, an internal reconciliation service went down at about four on a Sunday morning. All four pods were killed for memory within roughly twenty minutes of one another. They restarted, then failed again a few hours later. The service had last been deployed in February and had accumulated about seven months of uptime.
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The failure was traced to SDK client lifecycle. The service created a client for each request; each client registered a listener on a static registry, and those listeners were not removed. As requests accumulated, so did listener objects and memory use.
Shinder estimated the leak at about 40 MB per pod per day and compared heaps containing about 1.2 million listener objects. He also observed memory rising with pod age across services using the same library. Those are incident observations from his essay, not independently verified measurements or general industry statistics. The essay’s publication year is not established.
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The wider fleet rarely showed the long-running pattern clearly: Shinder says median pod age was under 30 hours because services were deployed frequently. In effect, deployments repeatedly cleared accumulated state before the age-dependent failure became obvious. As he puts it, “Deploying often had become a reliability control, and we had never decided to have it.”
Why a restart can make a defect look fixed
It resets symptoms, not the cause
A restarted process begins with fresh in-memory state. If a defect accumulates state gradually, a restart can temporarily restore memory headroom without correcting the code or lifecycle behavior responsible for the growth. The leak resumes as the new process handles work.
That distinction matters operationally: a service can appear healthy after a restart while remaining vulnerable to the same failure later. In this incident, removing the accumulated listeners by replacing the process did not address why each request created another client and registered another listener.
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Fleet averages can conceal age-dependent behavior
Memory use viewed without instance age can be hard to interpret. A fleet dominated by recently started pods may look stable even if memory rises steadily in each long-lived process. Tracking pod age alongside memory helps distinguish a broadly high baseline from growth that accumulates over time.
Shinder’s caution is that “a quiet fortnight is a risk and not a rest” when short replacement cycles may be keeping an age-related defect out of view. A long-lived instance is not automatically unhealthy; it is evidence about behavior that younger instances cannot provide.
What to monitor instead of memory alone
Pair memory with pod age and growth rate
An absolute memory threshold is useful for detecting proximity to a limit, but it can be a late warning: the process may be killed shortly after crossing the limit. Shinder recommends measuring memory growth as a rate normalized by pod age, so a team can notice a rising trend before it becomes an urgent threshold event.
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- Graph memory over time for individual instances or clearly grouped age cohorts.
- Show pod age alongside memory so gradual accumulation is visible rather than lost in fleet-wide summaries.
- Alert on sustained growth trends as well as high-water thresholds; choose thresholds based on the service’s workload and memory limits.
- Include pod-age distribution on the platform dashboard to make the fleet’s exposure to long-lived behavior visible.
These signals answer different questions. A high-water alert asks whether memory is near a limit now. An age-normalized trend asks whether memory is accumulating as an instance runs. Neither replaces the other.
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Run a long-lived soak instance
Shinder proposes running one instance of each service for 30 days in a soak environment, exercising it with synthetic traffic and graphing its memory. The point is to observe behavior “on day thirty,” rather than infer long-term stability from a fleet whose instances are routinely replaced.
- Select a representative service instance and a soak environment that can safely sustain synthetic traffic.
- Keep the instance running for the planned observation period, avoiding routine replacement that would reset its accumulated state.
- Exercise it with traffic that reflects the relevant request paths, including paths that create or use the suspected clients.
- Graph memory against elapsed instance age, and investigate sustained growth or step changes rather than relying only on a final snapshot.
- Compare the findings with heap profiles and object-retention evidence when growth appears; use the evidence to identify and correct the underlying lifecycle defect.
A 30-day soak is the author’s proposed practice, not a guarantee that every defect will manifest within that period. A useful duration and traffic profile depend on the service, workload, and the suspected failure mechanism.
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Separate Kubernetes restart, readiness, and rollout behavior
“Restart” can refer to different actions in Kubernetes, and they do not all mean the same thing. A container can restart within its pod under the pod’s restart policy; repeated failures are subject to restart backoff. A workload controller can replace a pod. Updating a Deployment replaces pods according to its rollout strategy. Which behavior occurs depends on the workload and configuration.
Kubernetes also distinguishes liveness from readiness. A liveness probe can cause a container restart when it fails; a readiness probe controls whether a pod is considered ready to receive service traffic. The official Kubernetes probe documentation warns that poorly designed liveness checks can contribute to cascading failures under load.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsFor lifecycle and restart-policy details, consult the version-appropriate Pod Lifecycle documentation. Deployment updates follow a separate rolling-update model. None of these mechanisms diagnoses or repairs a memory leak: adding a liveness probe or rolling out a new version may reset symptoms, but the defect still requires investigation and correction.
Turn the hidden dependency into an explicit reliability practice
Shinder’s broader lesson is to notice when routine replacement is carrying reliability work that no one intended it to carry. “Anything that hides a defect is load bearing,” he writes. If a service’s apparent stability depends on frequent restarts, teams need visibility into that dependency rather than assuming a quiet period proves the system is healthy.
Quick Recap
- Review code and library usage for objects created per request but retained through static registries, global references, or listeners.
- Use heap comparisons and profiling to identify what accumulates, then correct the ownership or cleanup lifecycle.
- Make age and memory growth visible on dashboards, and retain at least some safely managed long-lived test coverage.
- Treat restarts as recovery behavior, not as proof that the underlying failure has been fixed.
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