Anyone else having issues with Aqua's agent causing high memory usage on nodes?

Yes, we observed the same pattern across several EKS clusters. The baseline memory footprint is consistently high due to the agent's eBPF programs and event processing pipeline. While you can't eliminate it entirely, we've had success tuning the scan scope.

You can adjust the `resources.limits` in the DaemonSet, but the real fix is in the agent config. Focus the runtime profile on your specific threat model. For instance, we disabled file integrity monitoring for certain ephemeral mount paths that were generating a torrent of events. We also increased the `scanner.memoryScanInterval` to reduce frequency on known-stable images. This cut our steady-state usage by nearly 40% without a noticeable drop in meaningful alerts.

The key is to treat it like any other observability tool, you have to tune the sampling and scope. Their default config is designed for maximum visibility, which isn't always necessary. I can share a snippet of our tailored `aqua-agent.yaml` if you're interested.

It's not just you. High memory usage is the norm for runtime security tools, and Aqua is a known offender. While you can tweak resource limits, that's treating a symptom. You need to challenge the vendor's default threat model.

Their default configuration is designed for the most paranoid security posture possible, which means scanning everything, all the time. For most shops, that's overkill and leads to bloated agents. Work with your security team to define what actually needs protecting in those data processing workloads, then dial back everything else. Start with their "tuning guide" but be prepared to push them for specific configuration options they don't advertise.

You're definitely not alone in noticing that memory footprint. While the other suggestions about tuning the scan scope are spot on, don't overlook the basics first.

When you mentioned it's "throwing off our node sizing," that's a good indicator. It might be worth checking if your cluster's nodes are under-provisioned for the combined workload + security overhead. Sometimes a small bump in node resources is a simpler fix than a complex agent reconfiguration, especially if you value the default visibility.

Have you reached out to Aqua support with specific memory profiles? They can sometimes provide version-specific tweaks.

Yeah, that baseline memory hit is real. We saw similar numbers across our Azure AKS clusters.

While tuning the scope helps, I'd check if your spikes correlate with the agent's container image scanner kicking off. That component can be a memory hog, especially if it's trying to scan large layers. You can often move that workload to a separate, scheduled job and keep the runtime agent leaner. That dropped our steady-state usage down closer to 150-200 MiB per node.

Their default config really assumes you have memory to burn.

That's a common range we see in our environments too. The visibility does come at a cost, especially for data-heavy workloads where you're already memory-constrained.

One thing that helped us was a pragmatic review of the "monitored processes" list. The default includes a lot of shells and interpreters that, in our case, simply aren't a risk vector in our pipeline stages. Trimming that list down provided a steady reduction in footprint and CPU cycles spent on event evaluation, not just memory. It's a balance between ideal coverage and practical resource impact on your specific workloads.

Have you mapped out where those spikes align in your processing timeline yet? Sometimes they're tied to specific batch operations, which can help you target the tuning.

Oh yeah, that 300-400 MiB baseline hits close to home. We see the exact same range across our marketing automation platform's EKS clusters.

I agree with the others on tuning the scope, but from a martech perspective, we found a huge win by focusing on the process list. The default monitored processes include things like `python` and `node`, which are in near-constant use by our campaign engines. Whitelisting the specific, approved binaries for our data transformation jobs drastically cut down on event noise and memory churn.

One caveat though - if you do heavy container image scanning, that can be a separate culprit. Have you considered offloading those deeper scans to a dedicated, scheduled job instead of the runtime agent? That's what finally got our nodes back to predictable sizing for our A/B test pipelines.

Great point about the process list. We found something similar with our CI/CD runners - the default monitoring of common interpreters like `bash` created a ton of noise and memory overhead for entirely legitimate pipeline steps.

Your idea to offload deep image scans to a scheduled job is gold. We did that for our dev clusters and saw an immediate memory drop. For production, we kept the runtime agent lean and only schedule the heavy scanner during off-peak hours. The agent's footprint is much more predictable now.

I'm with you on checking node sizing first, but in my experience that's a short-term band-aid that can bite you later. If you simply bump up node resources to accommodate a bloated agent, you're just paying more for inefficiency every month. The real cost isn't just the extra memory, it's the cumulative compute waste across all nodes.

You're right that complex reconfigs are a pain, but taking the time to scope the agent to your actual risk profile pays off long-term. That said, reaching out to support is a solid step. In my case, they pointed me to some undocumented settings for pruning the event buffer, which helped a lot more than the generic tuning guide.

Yeah, that 300-400 MiB baseline is what we see too. The suggestion about adjusting the monitored process list is good, but don't just whitelist approved binaries, you can also prune the default list. Their default includes every shell and interpreter under the sun. If your data processing workloads are using something like Spark or Flink on the JVM, you can probably strip out python, perl, bash, sh, and all those right off the bat.

Also, check your agent config for the memoryScanInterval and eventBufferSize settings. We found the defaults were way too aggressive for our clusters. Dialing those back cut our steady state usage by about 30 percent. The docs are a bit light, you might have to dig in the configmap spec.

One more thing - are you using the image scanning component in the same agent? If so, that's probably where your spikes are coming from. Schedule that as a separate job, even if it's on the same node, and your runtime agent memory graph will flatline.

Yep, welcome to the club - that's the standard tax for Aqua's visibility. Since you're on GKE, a specific tweak that helped us was adjusting the `eventBufferSize` in the configmap. The default is greedy for workloads with high process churn, like data processing. Dialing it back from the default significantly smoothed out our memory graph without missing critical runtime alerts.

Also, take a close look at your node's memory requests/limits for the agent DaemonSet itself. We found setting a realistic memory limit (not just a request) helped the kernel's OOM killer manage the spikes more predictably, preventing it from impacting neighboring pods. It's a bit of a blunt instrument, but it gives you a ceiling while you work on the root-cause tuning others have mentioned.

Have you isolated whether the spikes happen during specific pipeline stages? Sometimes correlating it with, say, a heavy Spark shuffle can point you to a noisy process you can safely exclude.

Yeah, that's the exact range we saw when we first deployed on our marketing clusters too. It really throws a wrench in the predictable resource planning you need for data jobs.

Following the other suggestions to trim the monitored process list helped us a lot, especially for our marketing automation workloads. We also set explicit memory limits on the agent DaemonSet to cap those spikes. It doesn't solve the root cause, but it gives you breathing room to tune without breaking your node sizing.

Has anyone found that the memoryScanInterval setting in the configmap has a bigger impact than the eventBufferSize for data-heavy workloads? I'm curious which dial to turn first.

Oh, that's a really good tip about the memory limits on the DaemonSet itself. I hadn't thought to treat the agent like any other pod to control its impact.

I'm still figuring out the configmap settings. For the eventBufferSize, did you just lower it incrementally from the default, or is there a known safe starting point for GKE?

>safe starting point for GKE

There isn't one. Your workload is unique. Start by halving the default, see if your critical alerts still fire. If they do, halve it again.

Limiting the DaemonSet is just containment, not a fix. You're putting a lid on a leaky bucket. Better than flooding the node, but you still have a leak.

Exactly. The container is still the problem. Setting a limit just means the OOM killer steps in more, which can cause the agent to restart and potentially miss events during its recovery cycle. It's not a stable solution.

Halving the default eventBufferSize is a reasonable starting tactic. But the real answer is profiling your specific workload's process activity to determine a minimal viable setting. Generic advice will only get you so far before you start dropping actual security signals.