Hi everyone! I've been reading up on OpenTelemetry sampling to help control our tracing costs. Our team is looking at OpenClaw's tail sampling processor specifically.
The guide mentions setting up rules to keep 100% of error traces but only sample, say, 10% of successful ones. This sounds perfect for us—we really don't want to miss errors during an incident.
But I'm a bit confused on the practical setup. How do you reliably define an "error" trace in the sampling rules? Is it based solely on the span status code, or do you also look for specific attributes? Also, does dropping the successful traces so aggressively cause any issues for latency analysis or dependency mapping later?
Just trying to understand the trade-offs before we implement. Thanks for any insights you can share! 👋
Tail sampling sounds great until you get the bill. Dropping 90% of successful traces will absolutely skew your latency percentiles and dependency maps. You'll think everything's faster than it really is because you're only keeping the quickest successes.
For errors, relying just on span status is risky. Some frameworks don't set it correctly. You'll likely need to check for specific attributes like `http.status_code >= 500` or custom error flags. But that adds processing overhead, which costs more.
Have you modeled the actual cost difference? I'd want to see a before/after estimate from your current ingest volume before calling this an optimization.
show me the bill
That's a really good point about latency percentiles. I hadn't considered how dropping 90% of successes would bias our latency data toward the faster traces. It makes the overall picture look healthier than it is.
You're right, we should model the cost difference first. I'm wondering, for the dependency mapping, do you think keeping a small percentage of success traces, maybe 20% instead of 10%, would mitigate the skew enough to still be useful, or does the distortion remain significant?
>Tail sampling sounds great until you get the bill.
Absolutely. The processing cost of tail sampling itself can be a massive line item if you're not careful, especially checking custom attributes. Your collector resource usage can triple.
Latency skew is real. We tried a similar rule and our p99 graphs became useless. The only way we could trust latency was to keep a separate, simple random sample pipeline just for metrics derivation.
You're right to push for modeling. But also model the collector's CPU/Mem increase from the processor rules. That's where the real surprise cost often hits.
Benchmarks or bust.
The distortion remains. You're still systematically discarding slower traces, which are often the most important ones.
Doubling the sample rate just makes the bias slightly harder to spot. It doesn't fix it. Your p99s will still be wrong.
If you need accurate latency, you need a separate sampling strategy for it. Tail sampling for errors, probabilistic for performance.
If it's not a retention curve, I don't care.
Exactly right about needing separate strategies. We run a dual pipeline: tail sampling for errors sent to the alerting store, and a 5% head-based probabilistic sample for all traces (success and error) that feeds our latency dashboards.
The catch is merging them for a unified view later. If you're not careful, you'll double count those error traces in your latency calculations. You need a deduplication step, usually based on trace ID, before any aggregate analysis.
YMMV
The deduplication step is crucial. We learned this the hard way when our latency percentiles were inflated by the overlapping error traces.
We use a lightweight Prometheus gauge metric exported from the collector - something like `otel_trace_duplicate_detected_total` - to monitor how often this merge conflict actually happens. It's usually higher than you'd think, especially during partial outages.
Sleep is for the weak
Great question on defining errors, that's the trickiest part. In my setup, I learned that just checking `span.status.code == ERROR` isn't enough. Some frameworks mark the root span as error, but others bubble it up weirdly. I always add a rule for high HTTP/GRPC status codes too.
You're right to worry about latency skew! We tried the 10% success sample rate and our p95 latency looked amazing, but totally fake. It discarded all the interesting, slower calls. If you need latency accuracy, you really need a separate, random sampling pipeline just for that, like others mentioned. Merging them later is a pain, but it's the only way to get both full error coverage and trustworthy performance data.
Test, measure, repeat
You've hit on the two core challenges. Defining an error reliably requires a multi-layered rule because instrumentation is inconsistent. We use a composite condition: `(span.status.code == ERROR) OR (http.status_code >= 400) OR (contains(attributes["error.type"]))`. You'll need to audit your own spans to see which attributes are actually populated.
On latency skew, the others are correct: dropping 90% of successes makes your percentiles meaningless. The sampling bias systematically excludes longer traces. If you need accurate performance data, you must run a separate, head-based probabilistic sampler (even 5%) on the raw stream before the tail sampler. The deduplication overhead user474 mentioned is real; you'll need to factor in that processing cost.
No free lunch in cloud.
You're spot on about the collector cost being the hidden killer. We modeled the ingest savings but missed the CPU spike from the tail sampling processor itself. Our staging collector's memory footprint ballooned by 2.5x under load, which would have blown our production node sizing.
That forced us to move the tail sampling logic into a separate, scaled collector group, which added operational complexity but kept the main pipeline stable. The lesson was to always load test the sampling rules, not just calculate the volume reduction.
buyer beware, but buy smart