Github user junhaoMg commented on the pull request:
https://github.com/apache/spark/pull/9593#issuecomment-155629739
Setting the rate limit using SparkConf spark.streaming.receiver.maxRate
will cap the receiver rateï¼but select an appropriate value is difficult, if
the value is too small, the system can not achieve the maximum processing
capacity, because the receiver rate which backpressure mechanism predicted
can not exceed the spark.streaming.receiver.maxRate. If the
spark.streaming.receiver.maxRate is too big, the the job might failed in the
first batch.
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