Spark Structure streaming read data twice per every micro-batch. How to avoid

I have a very strange issue with spark structure streaming. Spark structure streaming creates two spark jobs for every micro-batch. As a result, read data from Kafka twice. Here is a simple code snippet.

import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.streaming.Trigger

object CheckHowSparkReadFromKafka {
  def main(args: Array[String]): Unit = {
    val session = SparkSession.builder()
      .config(new SparkConf()
        .setAppName(s"simple read from kafka with repartition")
        .setMaster("local[*]")
        .set("spark.driver.host", "localhost"))
      .getOrCreate()
    val testPath = "/tmp/spark-test"
    FileSystem.get(session.sparkContext.hadoopConfiguration).delete(new Path(testPath), true)
    import session.implicits._
    val stream = session
      .readStream
      .format("kafka")
      .option("kafka.bootstrap.servers",        "kafka-20002-prod:9092")
      .option("subscribe", "topic")
      .option("maxOffsetsPerTrigger", 1000)
      .option("failOnDataLoss", false)
      .option("startingOffsets", "latest")
      .load()
      .repartitionByRange( $"offset")
      .writeStream
      .option("path", testPath + "/data")
      .option("checkpointLocation", testPath + "/checkpoint")
      .format("parquet")
      .trigger(Trigger.ProcessingTime(10.seconds))
      .start()
    stream.processAllAvailable()

This happens because if .repartitionByRange( $"offset"), if I remove this line, all good. But with spark create two jobs, one with 1 stage just read from Kafka, the second with 3 stage read -> shuffle -> write. So the result of the first job never used.

This has a significant impact on performance. Some of my Kafka topics have 1550 partitions, so read them twice is a big deal. In case I add cache, things going better, but this is not a way for me. In local mode, the first job in batch takes less than 0.1 ms, except batch with index 0. But in YARN cluster and Messos both jobs fully expected and on my topics take near 1.2 min.

Why it's happen? How can I avoid this? Look like Bug?

P.S. I use spark 2.4.3.

There is no bug in the spark in this case. The root cause of reading this data from Kafka twice is very simple. repartitionByRange function generates two spark jobs.

One for actual repartition.

One for sampling to find borders for partitions.

Please find more details in spark jira