[SPARK-23541][SS] Allow Kafka source to read data with greater parallelism than the number of topic-partitions #20698
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.sql.kafka010 | ||
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| import org.apache.kafka.common.TopicPartition | ||
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| import org.apache.spark.sql.sources.v2.DataSourceOptions | ||
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| /** | ||
| * 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: Int) { | ||
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| require(minPartitions >= 0) | ||
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| import KafkaOffsetRangeCalculator._ | ||
| /** | ||
| * Calculate the offset ranges that we are going to process this batch. If `numPartitions` | ||
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| * 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) | ||
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Contributor
There was a problem hiding this comment. was this check here before? What if there are new topic partitions? Are we missing those, because they may not exist in fromOffsets?
Contributor
Author
There was a problem hiding this comment.
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| val offsetRanges = partitionsToRead.toSeq.map { tp => | ||
| KafkaOffsetRange(tp, fromOffsets(tp), untilOffsets(tp), preferredLoc = None) | ||
| } | ||
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| // If minPartitions not set or there are enough partitions to satisfy minPartitions | ||
| if (minPartitions == DEFAULT_MIN_PARTITIONS || offsetRanges.size > minPartitions) { | ||
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| // Assign preferred executor locations to each range such that the same topic-partition is | ||
| // always read from the same executor and the KafkaConsumer can be reused | ||
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| offsetRanges.map { range => | ||
| range.copy(preferredLoc = getLocation(range.topicPartition, executorLocations)) | ||
| } | ||
| } else { | ||
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| // Splits offset ranges with relatively large amount of data to smaller ones. | ||
| val totalSize = offsetRanges.map(o => o.untilOffset - o.fromOffset).sum | ||
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| offsetRanges.flatMap { offsetRange => | ||
| val tp = offsetRange.topicPartition | ||
| val size = offsetRange.untilOffset - offsetRange.fromOffset | ||
| // number of partitions to divvy up this topic partition to | ||
| val parts = math.max(math.round(size * 1.0 / totalSize * minPartitions), 1).toInt | ||
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| var remaining = size | ||
| var startOffset = offsetRange.fromOffset | ||
| (0 until parts).map { part => | ||
| // Fine to do integer division. Last partition will consume all the round off errors | ||
| val thisPartition = remaining / (parts - part) | ||
| remaining -= thisPartition | ||
| val endOffset = startOffset + thisPartition | ||
| val offsetRange = KafkaOffsetRange(tp, startOffset, endOffset, preferredLoc = None) | ||
| startOffset = endOffset | ||
| offsetRange | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| private def getLocation(tp: TopicPartition, executorLocations: Seq[String]): Option[String] = { | ||
| def floorMod(a: Long, b: Int): Int = ((a % b).toInt + b) % b | ||
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| 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 | ||
| } | ||
| } | ||
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| private[kafka010] object KafkaOffsetRangeCalculator { | ||
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| private val DEFAULT_MIN_PARTITIONS = 0 | ||
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| def apply(options: DataSourceOptions): KafkaOffsetRangeCalculator = { | ||
| new KafkaOffsetRangeCalculator(options.getInt("minPartitions", DEFAULT_MIN_PARTITIONS)) | ||
| } | ||
| } | ||
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| private[kafka010] case class KafkaOffsetRange( | ||
| topicPartition: TopicPartition, fromOffset: Long, untilOffset: Long, preferredLoc: Option[String]) | ||
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| /* | ||
| * 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 | ||
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| import org.apache.kafka.common.TopicPartition | ||
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| package object kafka010 { // scalastyle:ignore | ||
| // ^^ scalastyle:ignore is for ignoring warnings about digits in package name | ||
| type PartitionOffsetMap = Map[TopicPartition, Long] | ||
| } |
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What's the difference between assert and require here?
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not much really. assert throws Assertions and require throws IllegalArgumentException. Just a matter of preference. I can revert this change.
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Assertions can be turned off