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#monitorama Percentiles, lots of ways to aggregate those monoidally. Stick with the histograms, folks. Sketches are sketchy.
Lots of ways to apply math to time series data that is nonsensical. Preach it.
Great talk by @kscaldef
i'm feeling inspired seeing all these tweets and comments flooding out of #monitorama.
so i'm going to tweet some factoids and common misunderstandings throughout the day as they come to me (or as i notice people committing said sins) #o11yfacts incoming!
so i'm going to tweet some factoids and common misunderstandings throughout the day as they come to me (or as i notice people committing said sins) #o11yfacts incoming!
#o11yfacts 1: there are two different meanings of the word 'metrics'.
There are small-m "metrics", a generic collective noun for statistical data. And there are statsd "Metrics", which are a specific data type consisting of a single number with n tags appended to it.
There are small-m "metrics", a generic collective noun for statistical data. And there are statsd "Metrics", which are a specific data type consisting of a single number with n tags appended to it.
Tags are a way of adding dimensions to the number so they can be sorted, grouped and described more usefully.
Statsd Metrics can be one of a small number of types, e.g. counter, gauge, histogram, summary, timing, etc, which inform the aggregator how to process or bucket them.
Statsd Metrics can be one of a small number of types, e.g. counter, gauge, histogram, summary, timing, etc, which inform the aggregator how to process or bucket them.
OH this morning: "drink every time someone quotes charity" (#monitorama channel, honeycomb user slack)
... i miss you all! wish i was in portland with you! *blows kisses*
... i miss you all! wish i was in portland with you! *blows kisses*
so i have a treat for y'all! i'm going to live tweet @akvanh/@alainadev's talk on "Creating a Culture of Observability".
I first saw the talk three months ago, and it made my knees tremble. I learned *so much*, and been meaning to share ever since.
I first saw the talk three months ago, and it made my knees tremble. I learned *so much*, and been meaning to share ever since.
Alaina and Alyson are both software engineers at @honeycombio. But they aren't ex-googlers, and they weren't super senior when they joined. They went to school for other careers, then retrained at a hacker school -- I think this is their second programming job?
in honor of #monitorama and all the awesome talks and tweets, here is a mini-rant on why the common mantra "three pillars of observability: metrics, logs, and traces" is wrong, wrong, and wrong (or at least woefully outdated). with love. 📈❤️📉
first, some definitions.
📈 "metrics" can either mean a generic synonym for telemetry data, or "statsd-style integers with tags appended for grouping". the first is uselessly broad in this context.
📈 "logs" usually means unstructured strings written out to disk. (cont'd)
📈 "metrics" can either mean a generic synonym for telemetry data, or "statsd-style integers with tags appended for grouping". the first is uselessly broad in this context.
📈 "logs" usually means unstructured strings written out to disk. (cont'd)
"logs" can be interpreted so broadly as to mean "any data emitted by any piece of software", but this is also uselessly broad. to most people it means strings => disk.
📈 and "tracing" generally means distributed tracing.
ok. definitions in hand now.
📈 and "tracing" generally means distributed tracing.
ok. definitions in hand now.
