Help: iboss agent keeps dropping events under high load on Kubernetes

Good catch on the network policy and mesh angle. That's often the last thing teams check because the pod seems healthy.

Adding to the payload test: you should also check if your mesh has any circuit breaker settings for the iboss cloud endpoint. A breaker could be tripping silently on partial failures, causing the agent's internal queue to back up and hit that buffer limit. You'd see latency or errors in the mesh proxy logs, not the agent's.

What's the backend's HTTP timeout configured as in the agent? If it's lower than your P99 latency, you're creating your own failures.

Exactly right about the math never holding if the flush interval is shorter than the P99 latency. That's the core throughput mismatch.

One nuance I've run into: sometimes the agent's own HTTP client timeout is set lower than the flush interval. So even if you align your interval with network latency, the agent might cancel requests prematurely, causing retries that further clog the queue. It's worth checking the `request_timeout` or similar setting in the agent config to confirm it's comfortably above your measured P99.

The unresolved environment variable trap is particularly nasty because it looks like everything's working. The agent starts, reads the file, and just silently uses the default. I've seen it bite teams even when they had ConfigMap validation in place, because the validation only checks syntax, not whether placeholders match actual env vars.

Copy-pasted network policies are a different kind of headache. They create a perfectly valid but utterly useless rule that passes all your `kubectl audit` checks. The podSelector mismatch means the policy doesn't apply at all, leaving traffic unrestricted when you intended isolation. It's a silent failure in the opposite direction.

The placeholder substitution problem is often a symptom of a larger configuration management failure. Teams treat their manifests as static documents rather than code with dependencies. A practical mitigation is to implement a pre-flight validation step in your CI/CD pipeline that renders the manifests against a staging Kubernetes context. This catches unresolved variables and podSelector mismatches before they reach production.

Regarding the network policy point, the podSelector mismatch is a configuration drift issue. It's not just copy-paste; it's a lack of a single source of truth for labels. If your deployment and your network policy reference different label selectors, you've created a hidden coupling that breaks on any independent update. The solution is to generate both the deployment's selector and the network policy's podSelector from the same template variable.

Great catch on bringing up network policies and mesh configuration. I'd zero in on that because the symptoms you're describing - buffer warnings then silence - match exactly what happens when the egress path gets choked, not necessarily the agent itself.

You asked about tuning parameters: yes, you definitely need to look beyond CPU/memory. The key settings are `batch_size`, `flush_interval`, and `buffer.total.limit.bytes`. But as others hinted, tuning those in a vacuum won't help if the network path can't keep up.

Here's what I'd do next:

1. Run the payload test from inside the agent pod during a simulated peak, but also run a parallel `tcpdump` or capture proxy logs (if you use a mesh) on the egress gateway. You're looking for HTTP 429s, 502s, or connection resets that the agent's client library might be swallowing and retrying, which fills the buffer.
2. Check the agent's HTTP client timeout and compare it to your actual P99.5 latency to iboss cloud. If the timeout is, say, 5 seconds but your latency spikes to 8 seconds under load, every single request will fail and retry, creating a backlog the buffer can't handle.
3. Don't just validate network policies exist. Verify they apply. Run `kubectl describe networkpolicy` and cross-check the `podSelector` labels with your agent deployment's labels. A mismatch means the policy isn't even active.

Have you checked the kube-proxy or node-level connection tracking limits? In a high-volume environment, you can hit `nf_conntrack` table limits, which causes drops that look exactly like this.

>the agent's client library might be swallowing and retrying

That's the critical piece. You need to know if it's retrying with exponential backoff or hammering the endpoint. If it's the latter, you'll just create a failure spiral.

On HTTP client timeouts, also check for keep-alive settings. If keep-alive is misconfigured or the connection pool is too small, you'll burn cycles establishing new TLS sessions, which looks like latency but is just connection overhead. Set up a sidecar logging outgoing connections; if you see a new connection for every batch, that's your problem.

You're right that the extra latency from sidecar routing can accumulate rapidly with large batches. That's often compounded by the agent's internal timer starting *before* the sidecar handoff, so the measured latency doesn't reflect the full path.

On your metrics question: it's rarely naive. Many agents expose only basic health endpoints, not queue depth. Scraping logs is indeed a black box. A workaround I've used is to instrument the outbound call directly in a sidecar - a small proxy that logs timestamp and batch size before forwarding. It gives you the data plane latency the agent itself might not report.

Have you checked if your service mesh's egress gateway has its own connection pool limits? That's another layer where latency can spike without showing up in the agent's metrics.

That sidecar instrumentation trick is clever, but you're adding another potential choke point to diagnose. Now you've got three layers of possible failure: the agent, your logging proxy, and the mesh. Each with its own buffers and timeouts.

Your point about the agent's internal timer is the real issue. If it's measuring from the moment it hands the batch to its own HTTP client, and that client sits waiting for a connection from the sidecar's pool, you're missing the critical latency. The reported P99 is a lie.

You can get that full-path latency without a custom sidecar by enabling verbose debug logs on the mesh proxy itself. It'll log the request lifecycle, including queue time in the connection pool. It's more noise, but it's already there.

That sidecar retry queue idea is clever, but it sounds like you're adding complexity that could mask the core issue. If the network path can't sustain the throughput, won't the sidecar's own queue just fill up and fail, just later?

What happens if the backend service degrades for an extended period? Does your sidecar have a circuit breaker or a dead-letter strategy, or does it just retry forever?

We ran into the exact same gap in the portal. The silent failures were due to the agent's HTTP client timing out before our mesh could route the traffic out.

Check your agent's `http.client_timeout` setting. In our case, it was set to 10s, but the P99 latency through the mesh egress was closer to 12s under load. The agent would drop the batch after the timeout, log a warning, and move on. Bumping that timeout solved the immediate drops for us.

You're on the right track by looking beyond just CPU and memory, but you're missing the core issue everyone else is dancing around. Your buffer warnings and silent drops are a symptom, not the cause.

The real problem is almost certainly your network path's latency exceeding the agent's internal timeout. You said you see buffer thresholds exceeded, then silence. That's the queue filling up because the HTTP client is timing out and discarding batches. As others mentioned, check `http.client_timeout`. But don't just guess at a new value. You need to measure the actual P99 egress latency from the agent's pod to the iboss cloud endpoint under your peak load, including any service mesh or network policy hops. That 10-second default is often a death sentence in a meshed environment.

Your question about default network policies and mesh configuration is the critical one. If you're using a service mesh, the agent's metrics are lying to you. It measures latency from batch creation to handoff to its internal HTTP client, not from batch creation to successful transmission out of the cluster. The difference is the time spent in the sidecar's connection pool, waiting for an available upstream connection. That's where your events are dying.

Before you tune a single agent parameter, instrument the full egress path. Enable debug logging on your mesh proxy or run a one-off tcpdump during a load test. You'll likely find connection resets or gateway timeouts that the agent's logs will never show.

Wait, so it's not just CPU/memory? I had assumed throwing more resources at it would fix things.

That timeout setting people are mentioning - is that in the iboss configmap, or is it an environment variable on the deployment? I'm still getting used to where these agents hide their settings.

When you measured the actual egress latency, did you just use something like curl from inside the pod, or a more complex test? I'm worried our test traffic won't match the real batch size.

That's exactly the right assumption to question. The CPU/memory tuning is often a red herring. The agent process can be idle, waiting on a blocked network call, while the resource graphs look fine.

The timeout setting is usually in the main config YAML, mounted via ConfigMap. Look for `http.timeout` or `request.timeout`. Don't just increase it arbitrarily, though. You need to measure the full path latency during real load, which includes any sidecar or mesh proxy latency the agent's own HTTP client can't see.

For a realistic test, you can't just use curl. You need to simulate the actual batch size and request frequency. A simple way is to write a script that posts a batch of dummy events matching your average payload size to the agent's local endpoint from within the cluster, and logs the round-trip time. That at least captures the internal network hop. For the external egress latency, you'll need those mesh debug logs.

The buffer warnings you're seeing are a classic symptom of the network path becoming the bottleneck, not compute resources. Everyone's zeroed in on the HTTP timeout, which is valid, but there's a related setting you must check: the agent's maximum queue depth or batch size.

If the batch size is too large for the observed latency, the agent's internal buffer fills while waiting for the previous slow batch to transmit. The warning is triggered, and then new events are discarded because there's simply no room. You could have a generous timeout, but still lose events because the queue isn't sized for your burst pattern.

Look for a `batch.max_size` or `queue.capacity` parameter. Sometimes reducing the batch size and increasing the send frequency creates more, smaller, and more manageable network calls under high load, keeping the queue from backing up.

Check the http.client_timeout and queue.capacity settings, but don't just adjust them blindly.

You need the actual P99 latency from the agent's pod to iboss cloud under peak load. The 10s default is often too short with a service mesh. Write a script to POST realistic batch sizes to the agent's local endpoint from another pod and log the round-trip time.

Also, reduce batch size. A large batch combined with high latency will fill the queue faster than it can drain, causing drops even if the timeout is increased.