@jmacd regarding my earlier call-out, It was less to do with sampling decisions, and more about the ability to addLink after span creation for me, which I understood was removed due to sampling related concerns.
Having said that, I do indeed have some views on sampling so happy to chip-in some of my thoughts:
Some characteristics that I have found useful of samplers (in-house based) are:
- Ability to influence sampling from application code (i.e. the application code can force sample)
- Late evaluation of sampling decision (for the addLink case)
- Very much pro Liz's proposal on sample_rate, although I was thinking that this is more of a semantic convention that vendors can adhere to, rather than a defined property on the data model
I'm very much not in the loop here, but I was reading on OTel sampling somewhere mentioning the troubles of coordination, and I was reminded of this WeChat overload control paper that I figured could be of interest as an approach: https://www.cs.columbia.edu/~ruigu/papers/socc18-final100.pdf
In there, they only need to align the hashing algorithm they pick for overload control to give a priority to queries on a kind of user basis to ensure that a given user's transactions work end-to-end across all of their microservices (3000 services over 20,000 machines). There's a huge parallel with distributed tracing sampling to be compared there -- you want all traces everywhere to line up and let a full lineage of an operation be visible, and they want user transactions across thousands of services to succeed under heavy load without heavy coordination.
In a nutshell, their trick was to define 128 levels of user priorities (which are assigned by time-bound hashing algorithms so that over time slices, the priorities of various users are changed, ensuring that eventually during the day a user gets service), couple them to business priority rules (admin > sales > etc.), and then they check the current overall load availability of the local service to do a quick lookup and know whether to keep or shed a thing. That lets them quickly, based on locally observed load and predefined hash schedules, make decisions that without coordination tend to shed load similarly for all related flows of a given user or session and give successful end-to-end transactions. They also added a feedback system where a responding service that had to shed load feeds that decision to the parent, which can then abort some further downstream load.
@awssandra It's Matt (MSFT), good to meet you today. Some questions about the X-Ray Spec (by Section):
Workflow
1) Do you consider X-Ray "head-based", "tail-based" sampling, or something in between?
2) Does X-Ray impact throughput of the "entry service" (assuming no memory constraints)?
3) Is there a scenario where sampling at the Client would be useful (before data is sent)?
Sampling Rule
4) Do you have any requests to cap volume by default? Seems like "reservoir" + 5% rate could be expensive with high-volume.
5) Does "Service type" only refer to the entry service? What if two difference service types are in a dependency chain?
6) Do you have any usage data on these filters? For instance, is "URL Path" used enough to justify it?
Work Modes
7) How long does it take for sampling rules changes to take effect? Any customer feedback here?
8) Without the X-Ray SDK, is sampling unavailable? Do customers have other sampling options?
9) Can customers using 3rd-party monitoring venders take advantage of X-Ray?
I been looking at the sampler for .NET and wanted to propose building the concept of an Aggregate Sampler (AndSmapler, OrSampler). Here is a draft of a OTEP describing it. At a high level, it describes building a sampler that evaluates the results of multiple inner samplers to make a decision. I feel that it may make things easier in scenarios where multiple considerations/approaches go into making a sampling decision.
https://github.com/jifeingo/oteps/blob/AggregateSamplers/0000-sampler-and.md
Is this the right place to discuss something like this?
sampling.rate or sampling.rule would make sense to fill that gap. Or maybe there's a reason to not include such information?
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Hi all, I'm new here, and I'm not sure if this is the right place for my concern and if it hasn't already been addressed elsewhere.With trace ID ratio based sampling, a trace can break into several parts, if there is a sampler with a smaller ratio in the middle. You then know that all spans belong to the same trace, but it is not possible to determine which part is a child of another.
For example, consider a trace consisting of 3 spans, A -> B -> C. The sampling ratio is 1 for A and C, while it is 0.01 for B. This means that only A and C are collected while B is discarded with 99% probability. Since both will have the same trace ID, we just know they are part of the same trace. However, the parent span ID of C will be the span ID of B which is not collected and therefore not known finally. Hence, it is not possible in the general case to conclude that span C is a (grand-)child of A. (In this particular case, we know that C must be a child of A, because A is the root span. In the general case, this reasoning is not possible though.)
A possible solution would be forwarding the parent span ID in case a sampler decides not to sample a span. For that example, this means that the parent ID of A instead of B would be received for C, and the span data of C would finally report span A as its parent. A further improvement would be to also count the number of ancestor spans that have not been sampled. If this counter is carried by the span context, incremented after every hop, and finally added to the span data, it would be easy to find out, how many generations are between the parent span. In other words, we would know, for example, if a span is a child, grand-child, or a great-grandchild.
trace_id (and it's used to compute the hash), they will be consistently sampled (unless there are some collectors with different hash seed configured at the same tier)
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