Github user koeninger commented on a diff in the pull request:
https://github.com/apache/spark/pull/10089#discussion_r46438227
--- Diff:
external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
---
@@ -89,23 +89,29 @@ class DirectKafkaInputDStream[
private val maxRateLimitPerPartition: Int =
context.sparkContext.getConf.getInt(
"spark.streaming.kafka.maxRatePerPartition", 0)
- protected def maxMessagesPerPartition: Option[Long] = {
+ protected def maxMessagesPerPartition(leaderOffsets:
Map[TopicAndPartition, LeaderOffset])
+ : Option[Map[TopicAndPartition, Long]] = {
val estimatedRateLimit = rateController.map(_.getLatestRate().toInt)
- val numPartitions = currentOffsets.keys.size
-
- val effectiveRateLimitPerPartition = estimatedRateLimit
- .filter(_ > 0)
- .map { limit =>
- if (maxRateLimitPerPartition > 0) {
- Math.min(maxRateLimitPerPartition, (limit / numPartitions))
- } else {
- limit / numPartitions
+
+ // calculate a per-partition rate limit based on current lag
+ val effectiveRateLimitPerPartition = estimatedRateLimit.filter(_ > 0)
match {
+ case Some(rate) =>
+ val lagPerPartition = leaderOffsets.map { case (tp, lo) =>
+ tp -> Math.max(lo.offset - currentOffsets(tp), 0)
+ }
+ val totalLag = lagPerPartition.values.sum.toFloat
+
+ lagPerPartition.map { case (tp, lag) =>
+ tp -> Math.round(lag / Math.max(totalLag, 1) * rate)
--- End diff --
I also think you should account for what happens when someone has set
spark.streaming.kafka.maxRatePerPartition
in addition to enabling backpressure
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