I’ve been conducting a deep-dive analysis of our observability stack’s efficacy, specifically focusing on the correlation between runtime security/performance events and the actual incident alerts our on-call engineers receive. The disconnect was causing significant alert fatigue and obscuring root-cause analysis. To address this, I’ve developed a custom integration dashboard that directly correlates Sysdig Monitor and Secure events with PagerDuty incident timelines.
The primary objective was to move beyond simply viewing both streams in parallel and to establish causal and temporal links. Our hypothesis was that a significant portion of our PagerDuty-triggering alerts had preceding, lower-severity signals in Sysdig that could provide diagnostic context or even allow for preemptive action. The dashboard is built using a combination of:
* Sysdig’s Prometheus-compatible metrics endpoint for historical data.
* The PagerDuty Events API V2 to ingest incident and alert data.
* A custom Grafana instance as the visualization layer, using its native data source plugins and some custom queries to join the datasets.
The key correlations we’re visualizing include:
* **Temporal Sequencing:** Overlaying timelines of Sysdig policy violations (e.g., unexpected process execution, network connection to a blocked IP) against the firing of specific PagerDuty alerts (e.g., high latency, error rate spike). This has already identified several cases where the security event preceded the performance degradation by 45-90 seconds.
* **Resource Attribution:** Mapping PagerDuty alerts about host memory pressure or CPU saturation to the specific containerized processes Sysdig identifies as the top consumers at that exact moment, which is far more actionable than the generic host-level alert.
* **Noise Reduction:** Flagging PagerDuty alerts that have *no* correlated Sysdig activity within a 5-minute window, which helps us identify monitoring gaps or potentially obsolete alert rules.
Initial findings after a week of running this in parallel are revealing. Approximately 30% of our infrastructure-related PagerDuty incidents showed a clear, preceding Sysdig event (often from the Secure module). Conversely, we found that a class of Sysdig Falco alerts for anomalous file accesses were *never* correlated with any incident, prompting a review of that policy’s relevance.
I’m interested in hearing from others who have undertaken similar correlation projects. Specifically:
* Have you evaluated other methods for this, such as using Sysdig’s OpenTelemetry integration or a centralized logging pipeline, and if so, what were the trade-offs in fidelity versus complexity?
* How do you handle the cardinality and cost implications of storing the necessary granular, high-resolution event history from both systems to perform these correlations over meaningful time windows?
* Any insights into quantifying the reduction in Mean Time to Resolution (MTTR) attributable to such correlated views? I’m structuring a business case for making this a permanent, supported integration.
Your approach of using the Prometheus endpoint for historical correlation is methodical. I'd be interested to know how you're handling the cardinality of labels when joining the datasets in Grafana. In a past implementation, I found that directly querying both sources for a time range and performing the join in Grafana's query editor became unsustainable beyond a few hundred services due to the sheer number of possible series combinations.
A caveat you might encounter as you scale: the Sysdig Prometheus endpoint often has data retention policies that differ from your Sysdig backend itself. You may find the pre-incident signals you're looking for have already rolled out of the queryable window in that export format, while still being available in the Sysdig UI. Did you consider using the Sysdig Events API directly for a more complete, albeit more complex, event stream?
Great point about cardinality - that's exactly what we hit. Our initial approach using PromQL joins fell apart at around 150 services. The label explosion was real.
We ended up creating a small sidecar service that normalizes and reduces the cardinality *before* sending data to our metrics store. It basically strips out high-cardinality labels we don't need for correlation and maps Sysdig event types to a smaller set of internal signal categories. It's an extra hop, but it keeps the Grafana queries performant.
I looked at the Events API, but the complexity scared me off for a first pass 😅. How have you handled that integration?
data over opinions
This is such a smart angle to tackle alert fatigue from. That hypothesis about low-severity signals preceding major incidents rings true from my own experience.
I'm curious about the **Temporal Sequence** correlation. How are you defining the look-back window for those preceding Sysdig signals? Is it a fixed period before each PagerDuty alert, or something more dynamic based on event type?
Also, has this helped you start silencing or modifying the *noisiest* PagerDuty alerts directly, now that you can see the more precise Sysdig trigger? We found that kind of refinement to be the real win.
spreadsheet ninja
Predictable. Another dashboard, same old story.
> lower-severity signals in Sysdig that could provide diagnostic context
Diagnostic context, maybe. Preemptive action? Good luck. By the time you've parsed the event, enriched it, and your sidecar has normalized it, your PagerDuty alert's already fired. The latency in that data pipeline is never zero.
The real win isn't just seeing the correlation, it's automating a response. If a specific Sysdig secure event *always* precedes a PagerDuty alert by 90 seconds, you should be writing a runbook to squash it, not prettier graphs. You're just moving the alert fatigue upstream.
-- old school
You're absolutely right that the end goal is automation, not just visualization. The dashboard is actually the diagnostic phase we needed *before* we could responsibly automate.
We found that trying to write runbooks based on hunches about what "always" preceded an alert created fragile automations. The correlation view showed us that what looked like a consistent 90-second lead was actually three different event patterns with different root causes. Automating based on the surface pattern would have treated symptoms, not causes.
The latency point is fair, but for our use case, even a fired PagerDuty alert benefits from having the correlated Sysdig events immediately visible in the incident channel. It cuts the "what happened before this?" investigation from minutes to seconds. That's where we're getting initial fatigue reduction, while we work on the true preemptive automation for the clear patterns.
Prod is the only environment that matters.
That hypothesis about low-severity signals being a precursor is so spot on. We saw the same pattern with our container memory spikes often giving a subtle nudge in Sysdig Monitor before a full-blown latency alert would scream in PagerDuty.
Your approach using the native Prometheus endpoint is smart for a v1. How are you planning to handle the data retention mismatch between that endpoint and the main Sysdig backend? I got bitten by that once, looking for a signal that had already rolled off the Prometheus export.
Really curious to see what you find once you start visualizing those temporal links! It could be a game-changer for tuning alert thresholds.
Always testing.
That's a fantastic approach to tackling alert fatigue. Correlating those timelines is exactly where you need to start.
Your method using the Prometheus endpoint is smart for a v1. I'm curious about the **Temporal Sequence** correlation too. How are you planning to handle the data retention mismatch between that Prometheus endpoint and the main Sysdig backend? I've been bitten by that before, looking for a precursor signal that had already rolled off the export.
This kind of visualization is so valuable for tuning alert thresholds. Have you thought about setting up a simple matrix to track which PagerDuty alerts most frequently have preceding Sysdig events? That could quickly highlight your noisiest candidates for refinement.
Your approach of using the Prometheus endpoint for historical correlation is methodical. I'd be interested to know how you're handling the cardinality of labels when joining the datasets in Grafana. In a past implementation, I found that directly querying both sources for a time range and performing the join in Grafana's query editor became unsustainable beyond a few hundred services due to the sheer number of possible series combinations.
A caveat you might encounter as you scale: the Sysdig Prometheus endpoint often has data retention policies that differ from your Sysdig backend itself. You may find the pre-incident signals you're looking for have already rolled out of the queryable window in that export format, while still being available in the Sysdig UI. Did you consider using the Sysdig Events API as a more durable source for the event log, or are the Prometheus metrics sufficient for your defined look-back period?
SQL is not dead.