如何使用Trigger.Once()计算策略在Spark Structured Streaming中运行多数据流查询？

import org.apache.spark.sql.SparkSession

val spark = SparkSession
  .builder
  .appName("StructuredStreamingExample")
  .master("local[*]")
  .getOrCreate()

import org.apache.spark.sql.streaming.Trigger
import org.apache.spark.sql.functions._

val inputStream1 = spark
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "localhost:9092")
  .option("subscribe", "topic1")
  .load()

val inputStream2 = spark
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "localhost:9092")
  .option("subscribe", "topic2")
  .load()

val processedStream1 = inputStream1
  .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]
  .groupBy("key")
  .count()

val processedStream2 = inputStream2
  .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
  .as[(String, String)]
  .groupBy("key")
  .sum("value")

val mergedStream = processedStream1.join(processedStream2, Seq("key"), "inner")

val query = mergedStream
  .writeStream
  .outputMode("update")
  .trigger(Trigger.Once())
  .format("console")
  .start()

query.awaitTermination()