Performance Monitoring

Herein we explore approaching service level objectives a bit differently and explore how one can measure time with less overhead to enable accounting of low-latency operations.

Transcript

1. PERFORMANCE MONITORING AND NOW FOR SOMETHING
 ENTIRELY DIFFERENT @postwait
3. PERFORMANCE IMPACTS PEOPLE REMEMBER WHY YOU DO THIS
4. CONSIDER A GOAL 99TH PERCENTILE AT 1500MS
5. 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
6. OCCUPY! PERFORMANCE OF THE 99%
8. NOW CONSIDER THE PEOPLE YOU’RE BLIND 1266ms 860
9. PERCENTAGES
 ARE NOT PEOPLE
11. COMPARE YOUR SLA TWO MOMENTARY VIOLATIONS VS. AN EPIC OUTAGE
12. WITH THE ACTUAL TRAGEDY
13. WHAT IF I TOLD YOU IT WAS OKAY TO CARE I KNOW IT SOUNDS CRAZY, BUT
14. THEY’RE FASTER
 THAN
 USERS SYSTEMS
15. IT’S REAL PROBE EFFECT important_op() st := hrtime() important_op() fn := hrtime() log(fn-st)
16. OF PROBING RULES • fixed O(1) operations • no latency bubbles • no allocations
17. TIME IS AN ILLUSION, LUNCHTIME DOUBLY SO - Douglas Adams
18. TIME BUT FASTER MTEV_TIME https://github.com/circonus-labs/libmtev
19. FAST
 &
 CORRECT LIBCIRCMETRICS https://github.com/circonus-labs/libcircmetrics
20. OCTOPUS THE TECHNOLOGY
21. FIGHT
 THE OCTOPUS GET OUT THERE - @postwait