package pl.japila.spark.examples
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.streaming.{OutputMode, Trigger}
object GroupByKeyStreamingApp extends App {
val inputTopic = "GroupByKeyApp-input"
val appName = this.getClass.getSimpleName.replace("$", "")
val spark = SparkSession.builder
.master("local[*]")
.appName(appName)
.getOrCreate
import spark.implicits._
case class Order(id: Long, zipCode: String)
// Input (source node)
val orders = spark
.readStream
.format("kafka")
.option("startingOffsets", "latest")
.option("subscribe", inputTopic)
.option("kafka.bootstrap.servers", ":9092")
.load
.select($"offset" as "id", $"value" as "zipCode") // FIXME Use csv, json, avro
.as[Order]
// Processing logic
// groupByKey + count
val byZipCode = (o: Order) => o.zipCode
val ordersByZipCode = orders.groupByKey(byZipCode)
import org.apache.spark.sql.functions.count
val typedCountCol = (count("zipCode") as "count").as[String]
val counts = ordersByZipCode
.agg(typedCountCol)
.select($"value" as "zip_code", $"count")
// Output (sink node)
import scala.concurrent.duration._
counts
.writeStream
.format("console")
.outputMode(OutputMode.Update) // FIXME Use Complete
.queryName(appName)
.trigger(Trigger.ProcessingTime(5.seconds))
.start
.awaitTermination()
}
Demo: groupByKey Streaming Aggregation in Update Mode
The example shows Dataset.groupByKey streaming operator to count rows in Update output mode.
In other words, it is an example of using Dataset.groupByKey
with count
aggregation function to count customer orders (T
) per zip code (K
).
Complete Spark Structured Streaming Application
Credits
-
The example with customer orders and postal codes is borrowed from Apache Beam’s Using GroupByKey Programming Guide.