Azure DataBricks Stream foreach fails with NotSerializableException - apache-spark

Question

I want to continuously elaborate rows of a dataset stream (originally initiated by a Kafka): based on a condition I want to update a Radis hash. This is my code snippet (lastContacts is the result of a previous command, which is a stream of this type: org.apache.spark.sql.DataFrame = [serialNumber: string, lastModified: long]. This expands to org.apache.spark.sql.Dataset[org.apache.spark.sql.Row]):
class MyStreamProcessor extends ForeachWriter[Row] {
override def open(partitionId: Long, version: Long): Boolean = {
true
}
override def process(record: Row) = {
val stringHashRDD = sc.parallelize(Seq(("lastContact", record(1).toString)))
sc.toRedisHASH(stringHashRDD, record(0).toString)(redisConfig)
}
override def close(errorOrNull: Throwable): Unit = {}
}
val query = lastContacts
.writeStream
.foreach(new MyStreamProcessor())
.start()
query.awaitTermination()
I receive a huge stack trace, which the relevant part (I think) is this: java.io.NotSerializableException: org.apache.spark.sql.streaming.DataStreamWriter
Could anyone explain why this exception occurs and how to avoid? Thank you!
This question is related to the following two:
DataFrame to RDD[(String, String)] conversion
Call a function with each element a stream in Databricks

Answer 1

Spark Context is not serializable.
Any implementation of ForeachWriter must be serializable because each task will get a fresh serialized-deserialized copy of the provided object. Hence, it is strongly recommended that any initialization for writing data (e.g. opening a connection or starting a transaction) is done after the open(...) method has been called, which signifies that the task is ready to generate data.
In your code, you are trying to use spark context within process method,
override def process(record: Row) = {
val stringHashRDD = sc.parallelize(Seq(("lastContact", record(1).toString)))
*sc.toRedisHASH(stringHashRDD, record(0).toString)(redisConfig)*
}
To send data to redis, you need to create your own connection and open it in the open method and then use it in the process method.
Take a look how to create redis connection pool. https://github.com/RedisLabs/spark-redis/blob/master/src/main/scala/com/redislabs/provider/redis/ConnectionPool.scala