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Data minimization is a
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Classroom Contents
Practicalities of Productionizing Distributed Systems
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- 1 Intro
- 2 Why you should listen to me
- 3 Quick foundation
- 4 What makes distributed systems different
- 5 A subset of failures
- 6 Clients stuck to an overloaded process
- 7 Partial failure
- 8 "It's slow" is the hardest problem you'll ever debug
- 9 Create partial availability
- 10 "Who to Follow" in the monorail
- 11 Knowing what the system has done
- 12 Percentiles, not averages
- 13 Tracing
- 14 On profiling
- 15 Releases should change a metric
- 16 Free-form logs are liars
- 17 Common "problems" are overlogged
- 18 Uncommon problems
- 19 Avoid coordination
- 20 Backpressure
- 21 Dropping new messages on the floor
- 22 Returning "overload" error responses
- 23 Timeouts and exponential back-offs
- 24 Roll out infrastructure with feature flags
- 25 if (Decider.available..)
- 26 Multiple versions are the norm
- 27 Datacenter schedulers are worth it
- 28 Collaboration is politics
- 29 No time-traveling stalkers
- 30 moral necessity
- 31 Data minimization is a
