📖Designing Data-Intensive Applications

Martin Kleppmann
  • p.28 In fault-tolerant systems, it makes sense to increase fault rate (deliberately introduce more faults, kill servers, etc.), so that fault-handling code is executed and tested. Netflix’s chaos monkey is an example
  • p.38 latency vs. response time. response time = time between user issuing a request and receiving results (network delays, processing time (service time), queuing delay). latency is the duration that a request is waiting to be handled (during which it is latent)

    • response time = network latency + queuing latency + processing time
  • p.187 language-specific de/serialization frameworks often require ability to instantiate arbitrary classes which often leads to security vulnerabilities
  • p.212 data outlives code → Data outlives code


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