Structured Streaming: Reading from multiple Kafka topics at once - apache-spark

Question

I have a Spark Structured Streaming Application which has to read from 12 Kafka topics (Different Schemas, Avro format) at once, deserialize the data and store in HDFS. When I read from a single topic using my code, it works fine and without errors but on running multiple queries together, I'm getting the following error
java.lang.IllegalStateException: Race while writing batch 0
My code is as follows:
def main(args: Array[String]): Unit = {
val kafkaProps = Util.loadProperties(kafkaConfigFile).asScala
val topic_list = ("topic1", "topic2", "topic3", "topic4")
topic_list.foreach(x => {
kafkaProps.update("subscribe", x)
val source= Source.fromInputStream(Util.getInputStream("/schema/topics/" + x)).getLines.mkString
val schemaParser = new Schema.Parser
val schema = schemaParser.parse(source)
val sqlTypeSchema = SchemaConverters.toSqlType(schema).dataType.asInstanceOf[StructType]
val kafkaStreamData = spark
.readStream
.format("kafka")
.options(kafkaProps)
.load()
val udfDeserialize = udf(deserialize(source), DataTypes.createStructType(sqlTypeSchema.fields))
val transformedDeserializedData = kafkaStreamData.select("value").as(Encoders.BINARY)
.withColumn("rows", udfDeserialize(col("value")))
.select("rows.*")
val query = transformedDeserializedData
.writeStream
.trigger(Trigger.ProcessingTime("5 seconds"))
.outputMode("append")
.format("parquet")
.option("path", "/output/topics/" + x)
.option("checkpointLocation", checkpointLocation + "//" + x)
.start()
})
spark.streams.awaitAnyTermination()
}

Answer 1

Alternative. You can use KAFKA Connect (from Confluent), NIFI, StreamSets etc. as your use case seems to fit "dump/persist to HDFS". That said, you need to have these tools (installed). The small files problem you state is not an issue, so be it.
From Apache Kafka 0.9 or later version you can Kafka Connect API for KAFKA --> HDFS Sink (various supported HDFS formats). You need a KAFKA Connect Cluster though, but that is based on your existing Cluster in any event, so not a big deal. But someone needs to maintain.
Some links to get you on your way:
https://data-flair.training/blogs/kafka-connect/
https://github.com/confluentinc/kafka-connect-hdfs