tl; dr Используйте MemoryStream
для добавления событий и памяти для вывода.
Следующий код должен помочь начать:
import org.apache.spark.sql.execution.streaming.MemoryStream
implicit val sqlCtx = spark.sqlContext
import spark.implicits._
val events = MemoryStream[Event]
val sessions = events.toDS
assert(sessions.isStreaming, "sessions must be a streaming Dataset")
// use sessions event stream to apply required transformations
val transformedSessions = ...
val streamingQuery = transformedSessions
.writeStream
.format("memory")
.queryName(queryName)
.option("checkpointLocation", checkpointLocation)
.outputMode(queryOutputMode)
.start
// Add events to MemoryStream as if they came from Kafka
val batch = Seq(
eventGen.generate(userId = 1, offset = 1.second),
eventGen.generate(userId = 2, offset = 2.seconds))
val currentOffset = events.addData(batch)
streamingQuery.processAllAvailable()
events.commit(currentOffset.asInstanceOf[LongOffset])
// check the output
// The output is in queryName table
// The following code simply shows the result
spark
.table(queryName)
.show(truncate = false)