Circonus Blog

TSC stuff is always fun, the blogs [1] and presentations [2] from Circonus have been my favorite pieces on this. In fact Circonus and Theo Schlossnagle along with alumni Heinrich Hartmann and Riley Berton all have excellent presentations out there for anyone interested in motoring. [1] https://www.circonus.com/2016/09/time-but-faster/ [2] https://www.youtube.com/watch?v=xqocAHGoBAg (from around 19:20)

Circonus (https://www.circonus.com/) supports both recording histogram directly, and merging histograms for analysis. IIUC, it also supports first-class timeseries data similarly to Monarch, where each data point has a high precision timestamp that does not have to align with other timeseries in the data set.

PS: At Circonus we have built technology that is explicitly addressing many of the statistical pitfalls that are explained in the post. If you are looking for a monitoring vendor, who deeply cares about getting the the statistics right (especially around aggregating and analysing latency data), have a look at https://circonus.com / https://lps.circonus.com/statistics-for-engineers/ and/or reach out to me.

I did some work with eBPF measuring block i/o latencies on SSDs a few weeks ago. I wish I had a testbench the scale that Backblaze does to really get some data at scale. https://www.circonus.com/2019/01/which-block-i-o-scheduler-is-the-best…

If you care about correctness of data, solid data retention and good analytics (prediction, forecasting, etc.) then you should take a look at Circonus. http://www.circonus.com/ 500 metrics accounts are free for life. Built by SREs for SREs.

Amazon has nothing in the category of exception tracking. StackDriver error reporting is a lite product intended mostly for folks with small applications on AppEngine. There is a recent post by Corey Quinn entitled CloudWatch Is of the Devil, but I Must Use It discussing the extremely poor usability of Amazon CloudWatch [0]. Here’s another story about the general weirdness of the product [1] I would strongly recommend using a dedicated exception tracker like Sentry.io, they have an excellent full featured product at a extremely reasonable price. GCP has clearly documented Platform Launch stages for Alpha, Beta, and General Availability [2]. The Kubernetes project itself follows the same definition for these identifiers as GCP. Specifically Beta is considered to be acceptable for most production use-cases, but it is not guaranteed that the API will remain backwards compatible. So if you’re running the majority of cloud services, beta is fine. If you’re running a system that will cause permanent harm to humans, don’t use beta. One last note, GCP has just released a fully managed certificate product for their global load balancer. There is fully integrated Kubernetes support which has not yet been announced formally [3]. Certificates for Kubernetes on Amazon are less integrated than the above. There’s several options, it’s not clear without extensive research and testing what the tradeoffs are, and in the end it turns out that there is not an officially supported…

Circonus | Golang and Perl Software Engineers, Technical Writer | Remote | Full-time | https://www.circonus.com/ Circonus is a super-scalable data analytics platform for operational time series data. Think ops and engineering data enabled by a cost-effective and powerful data science platform; visualize any data, in any application, from any system, in real ­time. Our SaaS and On-­Premise solutions enable companies to combine monitoring, alerting, event processing, and predictive analytics into a unified solution. Listen to one of our CEO Theo's talks to get an idea of what Circonus does: https://www.youtube.com/watch?v=qh4hLIWqw6Q We are a 100% distributed and remote company, mostly across the US as well as Europe. You are also welcome to work from the Maryland office; most employees work from home. We build web software using technologies such as C, C++, Lua, Java, Ruby, Go, Perl, JavaScript, jQuery, HTML, Scss. 1) Go (Golang) Software Engineer Significant real-world experience in server-side development with Go and related frameworks https://jobs.lever.co/circonus/32e94960-c5e8-43c6-9746-9dc2e4ef1112 2) Perl Software Engineer Strong server-side development experience with Perl and working knowledge of mod_perl https://jobs.lever.co/circonus/f21d2a10-7f34-42b9-9b90-08bf55ed9a2c 3) Technical Writer Fluency producing documentation targeted at developers or operations teams, for our highly technical audience, creating eBooks, guides, primers and tutorials, user…

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This is uh... very beginner level, and doesn't feel super organized. https://www.circonus.com/resources/statistics-for-engineers/ seems like a better intro? For more advanced conversations, here's a few more topics of interest: - binomial distributions, and confidence intervals (application: https://www.youtube.com/watch?v=R4nCsgt1qEU) - forecasting timeseries data with triple exponential smoothing to highlight anomalies (https://www.usenix.org/legacy/publications/library/proceedings/lisa200… - methods of counting distinct values at scale. Hyperloglog (https://en.wikipedia.org/wiki/HyperLogLog) is used by systems like statd to estimate various statistics.

So it isn't reading the time from this memory page, it's using TSC. In the case of CLOCK_REALTIME, corrections that are applied to TSC are read from this memory page (comm_page). This summary only applies to Illumos. The Solaris implementation diverged significantly around build 167 (2011) long after the last OpenSolaris build Illumos was based on (build 147). It changed again significantly in 2015. I believe Circonus contributed an alternate implementation that does some of the same things as Solaris in 2016: https://www.circonus.com/2016/09/time-but-faster/ With that said, you are correct that whether or not it will read from a memory page instead depends on which interfaces you are using (e.g. get_hrusec()) and other subtle details.

"Is this the right room for an argument?" "I've told you once." http://www.circonus.com/pully-mcpushface/

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They store scalar values in that they are binary encoded data structures. But the data structures they actually encode at the cell levels they histogram bins in a datastructure optimized for histogram calculations (in the Circonus case log linear histograms encoded as 3 values[0]). These histogram structures are optimized for fast re-aggregation and quantization across time/dimension. By optimizing for histograms first and plugging things like counts and gauges into them (rather than the reverse for something like Prometheus) they get around a lot of the bad statistics and expensive query calculations that other metrics systems have. This is different than say how Prometheus stores histograms where it treats the bucket boundaries as a dimension in a fixed bucket histogram and the cell as the count of items in that bucket. [0] https://www.circonus.com/2018/05/effective-management-of-high-volume-n…