org.apache.spark.sql.kafka010.KafkaOffsetRangeCalculator.scala Maven / Gradle / Ivy
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package org.apache.spark.sql.kafka010
import org.apache.kafka.common.TopicPartition
import org.apache.spark.sql.sources.v2.DataSourceOptions
/**
* Class to calculate offset ranges to process based on the the from and until offsets, and
* the configured `minPartitions`.
*/
private[kafka010] class KafkaOffsetRangeCalculator(val minPartitions: Option[Int]) {
require(minPartitions.isEmpty || minPartitions.get > 0)
/**
* Calculate the offset ranges that we are going to process this batch. If `minPartitions`
* is not set or is set less than or equal the number of `topicPartitions` that we're going to
* consume, then we fall back to a 1-1 mapping of Spark tasks to Kafka partitions. If
* `numPartitions` is set higher than the number of our `topicPartitions`, then we will split up
* the read tasks of the skewed partitions to multiple Spark tasks.
* The number of Spark tasks will be *approximately* `numPartitions`. It can be less or more
* depending on rounding errors or Kafka partitions that didn't receive any new data.
*/
def getRanges(
fromOffsets: PartitionOffsetMap,
untilOffsets: PartitionOffsetMap,
executorLocations: Seq[String] = Seq.empty): Seq[KafkaOffsetRange] = {
val partitionsToRead = untilOffsets.keySet.intersect(fromOffsets.keySet)
val offsetRanges = partitionsToRead.toSeq.map { tp =>
KafkaOffsetRange(tp, fromOffsets(tp), untilOffsets(tp), preferredLoc = None)
}.filter(_.size > 0)
// If minPartitions not set or there are enough partitions to satisfy minPartitions
if (minPartitions.isEmpty || offsetRanges.size > minPartitions.get) {
// Assign preferred executor locations to each range such that the same topic-partition is
// preferentially read from the same executor and the KafkaConsumer can be reused.
offsetRanges.map { range =>
range.copy(preferredLoc = getLocation(range.topicPartition, executorLocations))
}
} else {
// Splits offset ranges with relatively large amount of data to smaller ones.
val totalSize = offsetRanges.map(_.size).sum
val idealRangeSize = totalSize.toDouble / minPartitions.get
offsetRanges.flatMap { range =>
// Split the current range into subranges as close to the ideal range size
val numSplitsInRange = math.round(range.size.toDouble / idealRangeSize).toInt
(0 until numSplitsInRange).map { i =>
val splitStart = range.fromOffset + range.size * (i.toDouble / numSplitsInRange)
val splitEnd = range.fromOffset + range.size * ((i.toDouble + 1) / numSplitsInRange)
KafkaOffsetRange(
range.topicPartition, splitStart.toLong, splitEnd.toLong, preferredLoc = None)
}
}
}
}
private def getLocation(tp: TopicPartition, executorLocations: Seq[String]): Option[String] = {
def floorMod(a: Long, b: Int): Int = ((a % b).toInt + b) % b
val numExecutors = executorLocations.length
if (numExecutors > 0) {
// This allows cached KafkaConsumers in the executors to be re-used to read the same
// partition in every batch.
Some(executorLocations(floorMod(tp.hashCode, numExecutors)))
} else None
}
}
private[kafka010] object KafkaOffsetRangeCalculator {
def apply(options: DataSourceOptions): KafkaOffsetRangeCalculator = {
val optionalValue = Option(options.get("minPartitions").orElse(null)).map(_.toInt)
new KafkaOffsetRangeCalculator(optionalValue)
}
}
private[kafka010] case class KafkaOffsetRange(
topicPartition: TopicPartition,
fromOffset: Long,
untilOffset: Long,
preferredLoc: Option[String]) {
lazy val size: Long = untilOffset - fromOffset
}