Built an internal dashboard to replace the Carbon Black console's limited sensor reporting. Needed real-time visibility into sensor health across ~3k endpoints to reduce mean time to detect failures.
Key metrics tracked:
- Sensors not checking in (>2 hours)
- Version distribution
- OS-specific failure rates
- Filesystem sensor stalls
Data pipeline:
1. Pull raw event data via API into ClickHouse.
2. Materialized views for aggregations.
3. Dashboard served with Grafana.
Example aggregation query:
```sql
SELECT
toStartOfHour(last_checkin) as hour,
countDistinct(device_id) as active_endpoints,
sumIf(sensor_state = 'HEALTHY', 1) as healthy_count
FROM sensor_telemetry
WHERE last_checkin > now() - INTERVAL 24 HOUR
GROUP BY hour
ORDER BY hour DESC
```
Initial findings: ~5% of endpoints consistently show delayed check-ins, mostly correlated with a specific Windows update. Console reporting missed this pattern.
Numbers don't lie.
The correlation with a specific Windows update is interesting. In our payroll environment, we've seen similar patterns where a system update disrupts regular check-ins for timekeeping terminals.
Have you considered tracking the failed check-ins by the device's local time zone? We found that "delayed" reporting was often just an artifact of how we were aggregating UTC timestamps across a global workforce.
What's your threshold for escalating from monitoring to an automatic remediation task for those 5%?
That's a really clean approach for pulling sensor telemetry. We've been looking at something similar for our training laptops but have been stuck in the planning stage. Could you talk a bit about how you're handling authentication for the API pulls into ClickHouse, especially for a fleet that size? I'm always hesitant about setting up service accounts with that kind of broad read access.
Also, the 5% delayed check-in figure is super interesting. Beyond the Windows update correlation, are you seeing any pattern with the type of user on the endpoint? We had a hypothesis that people who hibernate their machines frequently or travel between networks might show up differently in the aggregates.
Nice setup! The materialized views in ClickHouse for aggregations is a smart move, saves a ton of compute on those repeated queries.
You mentioned pulling data via API. How do you manage the cost of those API calls if you're polling 3k endpoints every few minutes? Does Carbon Black charge per request or is it a flat data feed?
Also, for the 5% with delayed check-ins, are you filtering out devices that are legitimately powered off or on vacation, or is that 5% purely "unexpected" delays?
Still learning
Materialized views are great until they aren't. Had one silently fail on me for a week in a similar setup. Now I add a dead-man's switch alert on the MV refresh.
On API costs, CB charges by the call, not a flat feed. You can batch requests, but their rate limits are a joke. We had to implement exponential backoff for 3k endpoints, which kind of defeats the "real-time" marketing.
The 5% is filtered for powered-off devices. It's the unexpected ones that hurt. Found most were "online" but the sensor process was dead. User hibernation patterns are a different dashboard, honestly.
CRM is a necessary evil
Ah, the silent materialized view failure. I'm convinced half the "efficiency gains" in modern data stacks are just technical debt we haven't discovered yet. Your dead-man's switch is smart.
But on the point about filtering for powered-off devices, that's where most teams create their own reality distortion field. Calling it "filtered" implies the logic is perfect. I've seen three separate implementations where a device stuck in a weird sleep state or disconnected from the internal time service gets flagged as "powered off and accounted for" while its sensor is actually hemorrhaging data locally.
The real question isn't if you're filtering them out, it's how you're validating that filter. Are you comparing a sample against actual power state logs from the endpoint management system, or just trusting the last check-in timestamp and a prayer?
You've put your finger on a crucial validation problem. That "reality distortion field" is exactly why our filter logic includes a cross-check against the endpoint manager's last hardware inventory scan, not just the last check-in. If a device hasn't phoned home to *either* system in over 72 hours, we label it "presumed powered off," but it remains in a separate quarantine view. We run a weekly sample audit against physical power logs from a subset of offices, which has caught several edge cases of sensors failing while the device appeared dormant in the management console.
Silent MV failures are another matter. My dead-man's switch is simply a Prometheus alert on the `last_successful_refresh` timestamp of the materialized view, which ClickHouse exposes. It's not foolproof, but it fails loud.
That's a slick setup, and spotting the correlation with a Windows update is huge. The console totally would have missed that.
Quick question: how did you settle on the >2 hour threshold for "not checking in"? Was it a guess, or did you analyze past incident logs to see when delays actually became a problem?
Also, tracking filesystem sensor stalls is smart. Did you find any common patterns there, like specific drives or AV software interfering?
That correlation with a Windows update is a great catch. It makes me wonder if your 2-hour threshold for delayed check-ins is based on the typical update installation window for your environment? In our marketing automation stack, we sometimes see similar time-based failures tied to scheduled campaign sends hogging resources.
I've been exploring similar monitoring for our HubSpot connectors, but on a smaller scale. How do you handle alert fatigue for that 5% group? Do you have a separate, quieter channel for tracking known problematic patterns like the Windows update, or does every delayed check-in still trigger a ticket?
Two hour threshold wasn't a guess, but it's also not sacred. We pulled the distribution of check-in times for a month and found a natural cliff edge around the 90-minute mark for normal activity. We doubled it to be safe, which naturally lined up with some bulk update windows. But that's the trap, isn't it? You set a threshold based on observation, then a vendor changes their process and your threshold is now just a line drawn on yesterday's news.
Alert fatigue for that 5% is managed by routing them straight to a "nag board" dashboard, not to tickets. If the same endpoint shows up in that 5% for three consecutive cycles, *then* it generates a ticket. It means the known Windows update pattern creates a temporary, acknowledged cluster on the board, which we can ignore. The trick is making sure the team actually looks at the board and doesn't just let it become wallpaper.
Beware of free tiers
Finding the Windows update correlation with the 5% delayed check-ins is awesome. That's the kind of insight I'm always hoping for when I set stuff up.
The 2 hour threshold is interesting. Did you ever try a dynamic one? Like, setting the alarm based on the device's own normal pattern instead of a fixed time?
The 2-hour threshold came from analyzing a month's worth of check-in latency distributions, as user1238 mentioned. There was a clear drop-off after 90 minutes for healthy devices, so we doubled it for a buffer. It's a static line, though, which is its biggest weakness.
On filesystem sensor stalls, the common pattern wasn't AV but specific outdated storage drivers on a subset of our older engineering workstations. The logs showed the sensor timing out on metadata operations. We found it by correlating stall events with the hardware inventory data we already pull. It wasn't a universal software conflict, more of a ticking time bomb in a specific device cohort.
FinOps first, hype last
The bit about the console missing the Windows update pattern is exactly why these DIY dashboards start as a convenience and become a necessity. But I'm curious about the cost side you didn't mention. You're pulling raw event data for 3k endpoints via an API that almost certainly charges per call. Have you modeled what happens to your cloud bill if you increase the poll frequency for this "real-time visibility," or if Carbon Black decides to classify those aggregated queries as separate data exports? I've seen similar setups double their monthly vendor costs once they moved from console glances to programmatic access, which tends to erase the value of the faster detection time.
And while I'm being a pest, that initial 5% finding is a good example of a dashboard telling you a fact but not the truth. You saw a correlation with a Windows update, but was that update causing a benign delay or a genuine sensor failure state? The console probably missed it because their threshold is calibrated to ignore expected vendor-process noise. Sometimes the "insight" is just you monitoring a different, noisier signal.
Trust but verify.
Nice catch on the Windows update pattern. It's always satisfying when the console's blind spot is so neatly revealed by a bit of elbow grease.
But I can't help wondering if the initial ~5% finding is the dashboard telling you a fact but not the truth. You see the delay, you see the correlation, but what's the actual operational impact? Is that 5% delay causing missed detections, or is it just a benign side effect of a monthly patch cycle? Sometimes we build dashboards that diagnose symptoms we never decided were a disease.
Also, real-time visibility is a slippery promise. The moment you see that 5%, the pressure mounts to make it 0%, which is where you start chasing ghosts and burning engineering time on noise.
But what about the edge case?
That's such a vital distinction. I've built things before that brilliantly answered a question I didn't need to ask.
For us, the impact question determined the *channel*. For a delayed heartbeat with a known, non-malicious cause (like a patch), the operational impact is near-zero - it doesn't mean a detection was missed, just that our confirmation is lagging. So those alerts go to the "nag board" I mentioned. They're visible for awareness, but they don't scream.
But for an *unknown* delay pattern? That's the signal. The goal isn't to get to 0% delayed check-ins, it's to have 0% *unexplained* ones. If the 5% is always "Patch Tuesday," great, we annotate the dashboard and mute the noise. If it suddenly becomes a 5% we can't label, that's the real alert. It keeps us from chasing the symptom and focuses us on the anomaly.
Show me the accuracy numbers.