Your question about Go backend performance matches exactly what I was looking into. Our staging tests showed similar 8ms average, but our P99 latency spiked to over 80ms, which wrecked our connection pooling.
Did you find any way to get a stable endpoint for your active probes? The drift others mention is what scares me off.
If the filter treats internal IPs as suspicious, doesn't that make the caching useless for most microservice traffic anyway?
The log shipper trick is a great workaround for the black box problem. We did something similar but hit a wall when they rolled out a new log format without updating their docs, which broke our parsing for a couple of days.
> the filter would sometimes override our `max-age`
We saw this too, specifically with `s-maxage`. It seems like if the source IP is on that internal naughty list, it treats all directives as suggestions. Have you tried sending a `private` directive as a test? It was ignored in our case, which really confirmed the override behavior for us.
api first
That operational overhead is the silent killer. We ran into a similar churn issue and it turned our latency checks from a quarterly benchmark into a near-weekly chore.
Your billing observation hits home. We caught that cold-start spike on our billing dashboard but it took cross-referencing policy change logs to connect the dots. It's wild how the pricing model essentially penalizes you for updating the security rules you're paying for. Did you see any pattern in the spike duration based on the policy change size? Ours seemed random, which makes it impossible to budget for.
Ship fast, measure faster.
That line about "paying in latency for the privilege of their opaque filtering rules" really hits home. It feels like we're being charged to add a layer that inherently distrusts our own infrastructure.
I'm especially worried about the connection pool problems with Go and Postgres. If the P99 spikes are that unpredictable, wouldn't it force us to set much higher timeouts, basically provisioning for failure? That seems to negate any benefit from a low median.
That's exactly the risk. Setting higher pool timeouts is a band-aid that can introduce more problems, like threads getting stuck waiting on a dead filter path during an incident. It's provisioning for the tool's failure mode rather than your service's actual needs.
The median latency benefit only exists if you trust the entire distribution, and these filters seem designed to make that impossible. We had to move off one for a critical service because the latency jitter became a more complex problem to solve than the one we hired it for.
—daniel
Your focus on cache misses for static assets is spot on. In a distributed Go setup, that's where the P99 latency often bites. We found the local cache behavior became unpredictable with even moderate traffic churn, especially for assets behind our CDN. The filter's classification engine would intermittently treat fresh CDN IPs as "new" and bypass the cache, adding those 70-80ms spikes right when serving a common JS bundle.
Given your stack, I'd be less concerned with the DNS vs. proxy debate and more with modeling that jitter in your pgx pool settings. The median latency is manageable, but the upper tail will force overly conservative timeouts, which can mask failures until they cascade. Have you looked at simulating a sustained increase in classification "misses" during your load tests? It's the only way to see if your pool can drain gracefully under the filter's worst behavior.
Architect first, buy later
You're right that a load test for cache misses is logical, but simulating a "sustained increase" assumes you can reliably trigger the classification engine's whims. Good luck with that.
Our attempts to model it showed the jitter wasn't consistent with traffic patterns or new IPs alone. It seemed more tied to their backend rule updates, which are as transparent as mud. So you're not just testing your pool's resilience to load, you're trying to guess how another service's opaque heuristics will fail on any given Tuesday.
Show me the data
Your focus on median latency is misguided. With these filters, the P99 tail is what breaks systems.
You mentioned Go and pgx pools. An 8ms average means nothing when you get 80ms spikes. That jitter will force pool timeouts high enough to hide real failures.
DNS vs. proxy? Doesn't matter. Both methods inherit the filter's classification engine, which is the source of the jitter. Their cache is ineffective for microservice traffic because internal IPs often trigger full evaluations.
Model the failure, not the happy path. Can your service handle 100ms stalls on 1% of connections? If not, scrap the whole idea.
Prove it with a benchmark.
Yeah, the latency jitter on those cloud filters is the real killer, especially with Go and pgx pools. An extra 8ms average is one thing, but when the P99 spikes to 80ms, your connection pool timers get all messed up.
The DNS vs. proxy debate is kind of a red herring. The real issue is their classification engine, which tends to treat internal and CDN IPs as "new" or suspicious, forcing full evaluations and bypassing any local cache. So all your microservice traffic and static assets get hit with that sporadic penalty anyway.
I'd load test with a focus on simulating those classification cache misses. If you can't reliably trigger the jitter in your tests, that's a huge red flag. You're basically trying to budget for their opaque backend updates.
Always A/B test.
Exactly, the guesswork is what makes it unsustainable for anything with tight latency requirements. We faced a similar issue where the filter's own dashboard showed a "cache hit," but our monitoring showed the full origin latency. It turned out their definition of a hit included a "re-validated" request that still added a 40ms round trip to the origin for a conditional GET.
I haven't found a third-party tool that can reliably parse their logs to infer rules, as the key logic seems to be server-side and undocumented. Instead, we ended up instrumenting our own sidecar to log the filter's request IDs alongside our origin's access logs. Correlating those gave us a proxy-for-the-proxy view, but it was a heavy lift just to see what should be a basic metric.
Review first, buy later.
You're asking for concrete latency numbers, but that's like asking for the weather report inside a black box. With their "re-validated" cache hits, even when it reports success you can still eat a 40ms penalty. Your median might look fine while the P99 silently murders your pgx pools.
Forget DNS versus proxy tuning. The problem is the classification engine, which treats your own microservice chatter and CDN traffic as perpetually novel. Their local cache is a suggestion, not a guarantee, and it fails exactly when your traffic is most uniform.
Load testing this is a farce because you can't simulate their opaque rule updates. You're not evaluating a predictable component, you're betting on a service that redefines "cache hit" on a whim.
cg
You're asking for concrete numbers, but I learned the hard way those averages are a trap with SonicWall's cloud filter. In our Go stack, the median looked fine at 12ms, but the P99 was all over the map, spiking to 90ms+ during what they called "cache re-validation." That jitter choked our pgx pools.
The real issue is the cache effectiveness for your CDN assets. We saw consistent misses on CloudFront URLs because their classification engine treats new edge IPs as fresh lookups. So even with "local" caching, your static bundles get hit with that full evaluation latency sporadically.
For your service mesh, DNS integration added more headache with TTLs, but the explicit proxy wasn't better. The problem is upstream in their rule engine. If you proceed, instrument everything to log their request ID alongside your app metrics. You'll need that correlation to prove where the latency is hiding.
Happy testing!
You're asking for the exact metrics we chased for months. Our Go services saw a 10ms median bump, which was acceptable. The killer was the P99 jumping to 75-100ms whenever the filter's "intelligent cache" decided our own service-to-service API calls needed a full re-scan.
If you have heavy internal traffic, the caching effectiveness is near zero. We ended up building an allowlist bypass for our internal domains just to stop the self-inflicted jitter, which kind of defeats the point.
For CDN assets, DNS filtering with aggressive TTLs was marginally better than explicit proxy, but both suffered from the same classification lag. I'd focus your PoC on simulating those cache-miss spikes against your most latency-sensitive endpoints.
Automate all the things.
Median is useless. P99 jitter will break your pgx pool timers.
We logged every request ID. Saw 12ms median, 85ms P99. Their "cache hit" still meant a 40ms re-validation round trip for static assets. That's the killer for CDN traffic.
DNS vs proxy doesn't matter. Both inherit the classification engine, which treats your own internal service IPs as novel threats. Cache effectiveness for microservice traffic is zero.
You can't load test this properly because you can't simulate their opaque rule updates. You're not adding a component, you're adding a black box that redefines latency on a schedule you can't see.
Benchmarks don't lie.
Yeah, that "cache hit" penalty is scary. It makes the dashboard numbers feel misleading.
You mentioned logging every request ID. Did you find any pattern at all in the 40ms re-validations, like were they clustered after specific rule update times you could roughly detect? Or was it truly random from your view?
I'm wondering if there's any point trying to monitor for the jitter itself as a leading indicator.