With microservices, one request can go through hundreds of nodes. Not one engineer can know all the possible paths of the request, How can engineers infer how the system behaves? Metrics? Logging? These tools have their place, but neither of these inherently constructs a journey of the entire request. What if we want to optimize the overall request latency? Figure out how many additional hops the system will make by adding a new API call? I am here to talk about how distributed tracing tells a story about your system. I will go over how you can see the entire picture of what your system looks like, and with this data, make investigate and triage systematic issues, and make impactful, data-driven, performance optimizations to your system. I will go over what tracing does well and what it isn’t meant for. I will also go over how we went about tracing at Lyft and lessons learned from our adoption process.
|Starts On||9/7/18, 3:30 PM|
|Room||Multi-Purpose Room 1|
|Session Duration||Regular Session (60min)|
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