Having extensively analyzed Sumo Logic's pricing model for a client's multi-cloud FinOps deployment, I've come to appreciate the criticality of collector stability. The collector is the fundamental data ingestion pipeline, and any instability directly translates to gaps in observability, which in a cost-optimization context, can mask resource waste and budgetary anomalies.
My team is currently troubleshooting an issue where several installed collectors—a mix of hosted and local, across AWS and Azure environments—are experiencing seemingly random disconnections. The symptom is a cessation of log flow in the Sumo interface, followed by the collector status flipping to "Offline" or "Disconnected" in the management console, often without a corresponding critical alert from the infrastructure. The collectors typically resume after a manual restart of the collector service, but the root cause remains elusive.
Our investigation thus far has ruled out the obvious culprits:
* **Network egress/ingress costs and throttling:** We've verified no ISP throttling or hitting of data transfer caps that would trigger a kill. Firewall rules (port 443/TCP outbound) remain unchanged.
* **Resource contention:** The host VMs (mostly t3.medium and D2s_v3) show stable CPU/memory profiles with no sustained pressure that would cause the JVM to fail.
* **Credential expiration:** The access keys and IDs are confirmed valid and not rotating on a schedule that matches the disconnect pattern.
The randomness suggests a potential handshake or keep-alive issue with Sumo's ingestion endpoints. We are now correlating disconnect timestamps against:
* Sumo Logic's own service health history (though major incidents don't align).
* Changes in the volume and structure of the log data being forwarded, to see if a specific message pattern or a burst could be triggering a fault.
* The specific `collector.config` and `source.json` configurations, looking for any subtle misconfigurations in buffer or retry logic.
Has anyone else performed a similar forensic breakdown on random collector disconnects? I'm particularly interested in whether you've identified patterns related to:
* Specific source types (e.g., local file vs. script source vs. HTTP Source) being more prone to instability.
* The `collector.version` in use—we are standardizing on the latest, but have seen it across multiple versions.
* Any undocumented constraints in Sumo's ingestion API that might cause a silent drop when certain thresholds (beyond documented limits) are approached.
A stable collector is as crucial as a well-reserved instance for cost predictability; without it, you're flying blind. Any shared insights or log snippets from your own `collector.log` diagnostics would be invaluable for building a comprehensive failure model.
-- Liam
Always check the data transfer costs.
Your point about the financial impact of data gaps is spot on; it's not just a tech issue, it's a direct line-item risk. In a FinOps context, an unstable collector means your cost anomaly detection is blind, which defeats the entire purpose.
Given your mixed-environment setup, I'd suggest looking beyond the infrastructure layer to the collector's own state management. We observed similar behavior in a Salesforce event monitoring pipeline where the agent would silently fail when its internal queue reached a certain threshold, often due to a memory allocation issue that wasn't logged externally. The service remained running, but ingestion halted. A key indicator was a gradual increase in resident memory before each stall.
Have you examined the collector's own diagnostic logs for patterns around thread pool exhaustion or JVM garbage collection pauses? That's often where the silent culprit lives, especially if the process doesn't fully crash to trigger a standard infrastructure alert.
You've ruled out network throttling and resource constraints, which is the logical first step, but I'd caution against considering that investigation fully closed. In cloud environments, especially with a mix of hosted and local collectors, resource contention can be transient and not always reflected in average CPU/memory graphs.
Have you examined the specific timing of these disconnections against scheduled infrastructure events? In one of our Azure-based SOC 2 audit scopes, we traced similar "random" collector drops to the underlying VM's host patching cycle, which would cause a brief but critical TCP stack interruption that the collector service couldn't recover from gracefully. The service stayed running, but the socket died, creating exactly the symptom you describe: no logs, status flipping to disconnected, requiring a service restart. The key was correlating the disconnect timestamps with the cloud provider's maintenance logs, which weren't part of our default alerting.
—at
That's a solid checklist of first-tier exclusions. Since you've ruled those out, I'd shift focus to the collector's internal state and its "heartbeat" mechanism with Sumo's control plane.
In a similar scenario, we found the root cause was a mismatch between the collector's configured HTTP timeout and the actual latency of its status API calls. The collector would internally mark itself as unhealthy and stop ingesting if a single heartbeat took too long, even though the underlying network was fine. It was a silent failure because the service process itself never crashed.
Have you pulled the collector's own debug logs? Look for entries around the disconnection time that mention health checks, API timeouts, or internal queue status. That's usually where the real story is, not in the infrastructure metrics.
"Ruling out" resource constraints without concrete proof is premature. Charts lie. You need to look at process limits, not host averages.
> collector status flipping to "Offline" or "Disconnected"
That's a control plane reporting delay, not the root cause. The ingestion stopped well before the UI updated. Stop looking at the console and start tailing the local collector logs at the moment of failure. I've seen this exact thing happen when the JVM hits a GC wall and the heartbeat thread stalls.
Your manual restart is a clue. It's not a network blip; it's a process state corruption. Probably a slow memory leak. Add a cron watchdog that kills and restarts the service if local log output stops for 60 seconds. Ugly, but it'll keep the data flowing while you find the real bug.
-- old school
You're right about the queue and silent failure being a likely culprit. The memory leak pattern is classic.
But the thread pool exhaustion angle is more common than people think, especially with sudden bursts from auto-scaling events. The collector's default worker threads can get saturated, new log lines get queued, and the heartbeat manages to stay alive while actual ingestion is dead. Check the `collector.log` for "worker" or "pool" right before the disconnect time.
Adding a metric for queue depth and active threads to your monitoring is the only real way to spot this pre-failure.
Your cloud bill is 30% too high
You've ruled out the obvious suspects, but you're missing the most common one in my experience: scheduled OS-level maintenance that's killing the network stack. Especially on Azure VMs.
Check the Windows Update logs (or the equivalent for Linux) for the exact minute the collector drops. In one deployment, we found "random" disconnects were happening at precise 4 AM intervals, which matched the VM's default patch window. The service restart script didn't trigger because the process was still running, just zombified.
Your manual restart working points to this. The collector process survives a network stack reset, but its sockets are dead. You need to either disable automated maintenance during critical hours or wrap your collector service in a script that forces a full restart, not just a service stop/start, after any detected network interface flap.