Github user brkyvz commented on a diff in the pull request:
https://github.com/apache/spark/pull/20698#discussion_r171988112
--- Diff:
external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/KafkaOffsetRangeCalculator.scala
---
@@ -0,0 +1,106 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+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)
+
+ import KafkaOffsetRangeCalculator._
+ /**
+ * 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(o => o.untilOffset -
o.fromOffset).sum
+ val idealRangeSize = totalSize.toDouble / minPartitions.get
+
+ offsetRanges.flatMap { range =>
+ // Split the current range into subranges as close to the ideal
range size
+ val rangeSize = range.untilOffset - range.fromOffset
+ val numSplitsInRange = math.round(rangeSize.toDouble /
idealRangeSize).toInt
+
+ (0 until numSplitsInRange).map { i =>
+ val splitStart = range.fromOffset + rangeSize * (i.toDouble /
numSplitsInRange)
+ val splitEnd = range.fromOffset + rangeSize * ((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]) {
+ def size: Long = untilOffset - fromOffset
--- End diff --
nite: maybe make this a `lazy val` so that it'll be calculated once
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