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The resource usage section at the end was really interesting, and surprising to me. 1% of jobs use 99% of resources! It would be interesting to try and understa
by remus 6y ago
The resource usage section at the end was really interesting, and surprising to me. 1% of jobs use 99% of resources! It would be interesting to try and understand how this pattern came about and if there's particular engineering decisions that tend to lead to this situation where you have a handful of incredibly resource intensive jobs and loads of very lightweight jobs.
- jeffbee 6y agoThat seems more related to weird choice of denominator than to any underlying facts of large scale cluster management. Large services have stable names and run forever. Search is just "search" and bigtable is just "bigtable" (some irrelevant details have been elided for clarity). If I run a batch logs analysis job though it is transient and has a unique name every time I run it. So there's a huge long tail of transient job _names_ and a few dominant services with permanent names, which makes the number-of-jobs denominator quirky.
- yeshengm 6y agoYeah. Likely a small amount of analytics jobs taking up most memory/CPU footprint, while critical prod OLTP jobs take very little resource comparably.