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emfree
searching PlanetScale…
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5 ms
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1.
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by
emfree
8y ago
Goroutine stacks are in fact allocated on the heap. All the details are in here: https://github.com/golang/go/blob/master/src/runtime/stack.g...
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by
emfree
9y ago
A nice writeup, thanks. There are a few variations on this workflow that I've found useful in practice; perhaps they'll be helpful to some folks: - Linux perf can profile unmodified Go programs. This is handy when your application
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by
emfree
9y ago
Use cases for ProxySQL: many. - failover - query routing (e.g., for sharded deployments) - caching - workload stats/metrics - query rewriting etc.
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by
emfree
9y ago
Curious: What's your strategy for measuring application performance? Would love to hear more details on how you're tracking the effect of your efforts.
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by
emfree
9y ago
You piqued my curiosity :) A comment in the source for the release notes ( https://github.com/golang/go/blob/master/doc/go1.9.html#L922 ) points to the relevant change: https://go-review.go
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by
emfree
9y ago
I think the author is specifically evaluating low-concurrency in-memory workloads here. The previous post describes why regressions for those workloads might be "not a surprise": https://smalldatum.blogspot.com/201
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by
emfree
10y ago
One of the older posts eloquently discusses why "accidentally quadratic" behavior is both so recurring and so insidious: http://accidentallyquadratic.tumblr.com/post/113840433022/wh...
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Event-Driven Instrumentation in Go
(honeycomb.io)
1 points
by
emfree
10y ago
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0 comments
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by
emfree
10y ago
I'll second the post above -- if you miss Scuba, honeycomb.io is for you. https://honeycomb.io/blog/2016/11/honeycomb-faq-in-140-chars...
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emfree
10y ago
These are such great comments, thanks for sharing your insights. For folks looking for other options, I'd also mention https://honeycomb.io , perhaps the most promising newcomer in this space. It's essentially Facebook&
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by
emfree
10y ago
Looks cool. Instrumenting at the network layer is certainly a promising approach. Are you recording latency distributions, and not just averages? The screenshots only show mean and median latency, which isn't enough to spot many anomal
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by
emfree
10y ago
> But in web services you often care more about the tail-end latency, the p90, p99 etc. For sure. I think Theorem 2 in the paper implicitly addresses the latency distribution in this scheme. They're saying that in the limit of a lar
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by
emfree
10y ago
Great question. I wondered the same thing a while ago, and tried to build one using SystemTap ( https://github.com/emfree/pystap ). Couple reasons why this isn't too easy: * "Python" in general might mean
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by
emfree
10y ago
Yep! In this case, I think you end up constructing, slightly more specifically, the isogeny whose kernel is exactly the cyclic subgroup generated by the point R (i.e., phi(S) is 0 iff S is a power of R). There are explicit formulas ("V
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by
emfree
10y ago
Thanks for the insightful comment! > The alternative if you are at a company with the resources to do so (mine is), is to build something that fits your use case better than Kafka I'd love to hear more about this :) What did you end
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by
emfree
11y ago
Here's a reference I found for one way to do it: http://www.math.nus.edu.sg/~matsr/ProbII/Lec6.pdf (Theorem 2.1). You define the Green's function G(x, y) = \sum_n Pr_x(S_n=y), where x and y are 3-vectors
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by
emfree
11y ago
Author of the post here. That's a good question. I don't know if this approach is objectively better, but it has a few nice features. * We generally favor free/open source solutions where practical. * It is quite a bit cheape
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by
emfree
11y ago
Ansible works well for us, although we use it in a somewhat different way than most folks. We previously wrote about our approach here, if you're curious: https://nylas.com/blog/graduating-past-playbooks
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by
emfree
12y ago
Hi, Inbox engineer here. Beyond the contextIO feature set, we support creating drafts, sending mail, and client sync, so you can use the API to really build full-fledged mail clients. The Inbox sync engine indexes all the data, so the API&#