How to use foreachPartition in Spark 2.2 to avoid Task Serialization error - scala

Question

I have the following working code that uses Structured Streaming (Spark 2.2) in order to read data from Kafka (0.10).
The only issue that I cannot solve is related to Task serialization problem when using kafkaProducer inside ForeachWriter.
In my old version of this code developed for Spark 1.6 I was using foreachPartition and I was defining kafkaProducer for each partition to avoid Task Serialization problem.
How can I do it in Spark 2.2?
val df: Dataset[String] = spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("subscribe", "test")
.option("startingOffsets", "latest")
.option("failOnDataLoss", "true")
.load()
.selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)").as[(String, String)]
.map(_._2)
var mySet = spark.sparkContext.broadcast(Map(
"metadataBrokerList"->metadataBrokerList,
"outputKafkaTopic"->outputKafkaTopic,
"batchSize"->batchSize,
"lingerMS"->lingerMS))
val kafkaProducer = Utils.createProducer(mySet.value("metadataBrokerList"),
mySet.value("batchSize"),
mySet.value("lingerMS"))
val writer = new ForeachWriter[String] {
override def process(row: String): Unit = {
// val result = ...
val record = new ProducerRecord[String, String](mySet.value("outputKafkaTopic"), "1", result);
kafkaProducer.send(record)
}
override def close(errorOrNull: Throwable): Unit = {}
override def open(partitionId: Long, version: Long): Boolean = {
true
}
}
val query = df
.writeStream
.foreach(writer)
.start
query.awaitTermination()
spark.stop()

Answer 1

Write implementation of ForeachWriter and than use it. (Avoid anonymous classes with not serializable objects - in your case its ProducerRecord)
Example: val writer = new YourForeachWriter[String]
Also here is helpful article about Spark Serialization problems: https://www.cakesolutions.net/teamblogs/demystifying-spark-serialisation-error