PERFORMANCE MONITORING AND NOW FOR SOMETHING
 ENTIRELY DIFFERENT @postwait 

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PERFORMANCE IMPACTS PEOPLE REMEMBER WHY YOU DO THIS 

CONSIDER A GOAL AVERAGE PERFORMANCE 

CONSIDER A GOAL 99TH PERCENTILE AT 1500MS 

THEY AREN’T HARD TO UNDERSTAND, JUST DECEPTIVE AT TIMES. QUICK TL;DR ON PERCENTILES • 99th percentile: q(0.99) • 99% of the samples are lower • 1% of the samples are higher q(0.99) = 149μs q(1) = 63ms 

OCCUPY! PERFORMANCE OF THE 99% 

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NOW CONSIDER THE PEOPLE YOU’RE BLIND 1266ms 860 

PERCENTAGES
 ARE NOT PEOPLE 

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COMPARE YOUR SLA TWO MOMENTARY VIOLATIONS VS. AN EPIC OUTAGE 

WITH THE ACTUAL TRAGEDY 

WHAT IF I TOLD YOU IT WAS OKAY TO CARE I KNOW IT SOUNDS CRAZY, BUT 

THEY’RE FASTER
 THAN
 USERS SYSTEMS 

IT’S REAL PROBE EFFECT important_op() st := hrtime() important_op() fn := hrtime() log(fn-st) 

OF PROBING RULES • fixed O(1) operations • no latency bubbles • no allocations 

TIME IS AN ILLUSION, LUNCHTIME DOUBLY SO - Douglas Adams 

TIME BUT FASTER MTEV_TIME https://github.com/circonus-labs/libmtev 

FAST
 &
 CORRECT LIBCIRCMETRICS https://github.com/circonus-labs/libcircmetrics 

RAYS OF HOPE LIBCIRCMETRICS HIGHLIGHTS • Inspired by stuff we saw in go • ability to observe memory • run prep-functions • gauges, counters, strings,
 and log-linear histograms • performance focused: • CPU-fanout counters & histograms • 10ns fixed histogram logging • JSON output, simple API 

OCTOPUS THE TECHNOLOGY 

FIGHT
 THE OCTOPUS GET OUT THERE - @postwait 

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