Github user viirya commented on the pull request: https://github.com/apache/spark/pull/4467#issuecomment-74010907 Let's analyze it clearly. The following is a simplified status transformation of the problem: tracker receivers t = 1 started registered:{A, B}; starting, not registered: {C} t = 2 stopping got stop msg:{A, B}; starting, not registered: {C} t = 3 stopping stopped:{A, B}; registered: {C} The above causes potential data loss. We want to avoid that. I agree. If we implement option 1, now the status transformation: tracker receivers t = 1 started registered:{A, B}; starting, not registered: {C} t = 2 stopping got stop msg:{A, B}; starting, not registered: {C} *we are going to wait for receivers that are started but not registered yet. *suppose we wait a fixed time period n. *however, we can't guarantee when the receiver C will be registered. *so, after waiting time n, the system status can be: t = n+2 stopping stopped:{A, B}; registered: {C} *or still t = n+2 stopping stopped:{A, B}; starting, not registered: {C} As you see, there will still be possible status that we have unregistered receiver C that processes data. This pr implements another approach. The receivers register first then do starting process: tracker receivers t = 1 started registered, started:{A, B}; registered, starting: {C} t = 2 stopping got stop msg:{A, B, C}; **D wants to register -> timeout t = 3 stopping stopped:{A, B, C}
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