How to perform Unit testing on Spark Structured Streaming? - scala

Question

I would like to know about the unit testing side of Spark Structured Streaming. My scenario is, I am getting data from Kafka and I am consuming it using Spark Structured Streaming and applying some transformations on top of the data.
I am not sure about how can I test this using Scala and Spark. Can someone tell me how to do unit testing in Structured Streaming using Scala. I am new to streaming.

Answer 1

tl;dr Use MemoryStream to add events and memory sink for the output.
The following code should help to get started:
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)

Answer 2

So, I tried to implement the answer from #Jacek and I couldn't find how to create the eventGen object and also test a small streaming application for write data on the console. I am also using MemoryStream and here I show a small example working.
The class that I testing is:
import org.apache.spark.sql.functions.col
import org.apache.spark.sql.streaming.StreamingQuery
import org.apache.spark.sql.{DataFrame, SparkSession, functions}
object StreamingDataFrames {
def main(args: Array[String]): Unit = {
val spark: SparkSession = SparkSession.builder()
.appName(StreamingDataFrames.getClass.getSimpleName)
.master("local[2]")
.getOrCreate()
val lines = readData(spark, "socket")
val streamingQuery = writeData(lines)
streamingQuery.awaitTermination()
}
def readData(spark: SparkSession, source: String = "socket"): DataFrame = {
val lines: DataFrame = spark.readStream
.format(source)
.option("host", "localhost")
.option("port", 12345)
.load()
lines
}
def writeData(df: DataFrame, sink: String = "console", queryName: String = "calleventaggs", outputMode: String = "append"): StreamingQuery = {
println(s"Is this a streaming data frame: ${df.isStreaming}")
val shortLines: DataFrame = df.filter(functions.length(col("value")) >= 3)
val query = shortLines.writeStream
.format(sink)
.queryName(queryName)
.outputMode(outputMode)
.start()
query
}
}
I test only the writeData method. This is way I split the query into 2 methods.
Then here is the Spec to test the class. I use a SharedSparkSession class to facilitate the open and close of spark context. Like it is shown here.
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.execution.streaming.{LongOffset, MemoryStream}
import org.github.explore.spark.SharedSparkSession
import org.scalatest.funsuite.AnyFunSuite
class StreamingDataFramesSpec extends AnyFunSuite with SharedSparkSession {
test("spark structured streaming can read from memory socket") {
// We can import sql implicits
implicit val sqlCtx = sparkSession.sqlContext
import sqlImplicits._
val events = MemoryStream[String]
val queryName: String = "calleventaggs"
// Add events to MemoryStream as if they came from Kafka
val batch = Seq(
"this is a value to read",
"and this is another value"
)
val currentOffset = events.addData(batch)
val streamingQuery = StreamingDataFrames.writeData(events.toDF(), "memory", queryName)
streamingQuery.processAllAvailable()
events.commit(currentOffset.asInstanceOf[LongOffset])
val result: DataFrame = sparkSession.table(queryName)
result.show
streamingQuery.awaitTermination(1000L)
assertResult(batch.size)(result.count)
val values = result.take(2)
assertResult(batch(0))(values(0).getString(0))
assertResult(batch(1))(values(1).getString(0))
}
}