如何在以kafka为来源的Spark结构化流中识别消息的来源？

Question

I have a use case in which I have to subscribe to multiple topics in kafka in spark structured streaming . Then I have to parse each message and form a delta lake table out of it. I have made the parser and the messages(in form of xml) correctly parsing and forming delta-lake table. However, I am only subscribing to only one topic as of now. I want to subscribe to multiple topics and based on the topic, it should go to the parser dedicatedly made for this particular topic. So basically I want to identify the topic name for all the messages as they process so that I can send them to the desired parser and process further.

This is how I am accessing the messages from different topics. However, I have no idea how to identify the source of the incoming messages while processing them.

 val stream_dataframe = spark.readStream
  .format(ConfigSetting.getString("source"))
  .option("kafka.bootstrap.servers", ConfigSetting.getString("bootstrap_servers"))
  .option("kafka.ssl.truststore.location", ConfigSetting.getString("trustfile_location"))
  .option("kafka.ssl.truststore.password", ConfigSetting.getString("truststore_password"))
  .option("kafka.sasl.mechanism", ConfigSetting.getString("sasl_mechanism"))
  .option("kafka.security.protocol", ConfigSetting.getString("kafka_security_protocol"))
  .option("kafka.sasl.jaas.config",ConfigSetting.getString("jass_config"))
  .option("encoding",ConfigSetting.getString("encoding"))
  .option("startingOffsets",ConfigSetting.getString("starting_offset_duration"))
  .option("subscribe",ConfigSetting.getString("topics_name"))
  .option("failOnDataLoss",ConfigSetting.getString("fail_on_dataloss")) 
  .load()


 var cast_dataframe = stream_dataframe.select(col("value").cast(StringType))

 cast_dataframe =  cast_dataframe.withColumn("parsed_column",parser(col("value"))) // Parser is the udf, made to parse the xml from the topic. 

How can I identify the topic name of the messages as they process in spark structured streaming ?

Answer 1

As per the official documentation (emphasis mine)

Each row in the source has the following schema:

Column Type

---

key binary

---

value binary

---

topic string

---

partition int

...

As you see input topic is part of the output schema, and can be accessed without any special actions.