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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