Spark structured streaming app reading from multiple Kafka topics

From a resource(Memory and Cores) point of view, there will be a difference If you are running it as multiple streams(multiple drives-executors) on the cluster.

For the first case, you mentioned-

df = spark.readStream.format("kafka").option("subscribe", "t1,t2,t3")... t1df = df.select(...).where("topic = 't1'")... t2df = df.select(...).where("topic = 't2'")...

Considering there will be a driver and 2 executers you have provided to above.

In the second case-

t1df = spark.readStream.format("kafka").option("subscribe", "t1") t2df = spark.readStream.format("kafka").option("subscribe", "t2")

You can run these as different streams- 2 drivers and 2 executors(1 executor each). In second case there will require more memory and cores for extra driver required.

Each action requires a full lineage execution. Youre better off separating this into three separate kafka reads. Otherwise you'll read each topic N times, where N is the number of writes.

I'd really recommend against this but if you wanted to put all the topics into the same read then do this:

streamingDF.writeStream.foreachBatch { (batchDF: DataFrame, batchId: Long) =>
  batchDF.persist()
  batchDF.filter().write.format(...).save(...)  // location 1
  batchDF.filter().write.format(...).save(...)  // location 2
  batchDF.unpersist()
}