scala - Spark Dataframe write to kafka topic in avro format?

Question

I have a Dataframe in Spark that looks like

eventDF

   Sno|UserID|TypeExp
    1|JAS123|MOVIE
    2|ASP123|GAMES
    3|JAS123|CLOTHING
    4|DPS123|MOVIE
    5|DPS123|CLOTHING
    6|ASP123|MEDICAL
    7|JAS123|OTH
    8|POQ133|MEDICAL
    .......
    10000|DPS123|OTH

I need to write it to Kafka topic in Avro format currently i am able to write in Kafka as JSON using following code

val kafkaUserDF: DataFrame = eventDF.select(to_json(struct(eventDF.columns.map(column):_*)).alias("value"))
  kafkaUserDF.selectExpr("CAST(value AS STRING)").write.format("kafka")
    .option("kafka.bootstrap.servers", "Host:port")
    .option("topic", "eventdf")
    .save()

Now I want to write this in Avro format to Kafka topic

Answer 1

Spark >= 2.4:

You can use to_avro function from spark-avro library.

import org.apache.spark.sql.avro._

eventDF.select(
  to_avro(struct(eventDF.columns.map(column):_*)).alias("value")
)

Spark < 2.4

You have to do it the same way:

• Create a function which writes serialized Avro record to ByteArrayOutputStream and return the result. A naive implementation (this supports only flat objects) could be similar to (adopted from Kafka Avro Scala Example by Sushil Kumar Singh)

  import org.apache.spark.sql.Row
  
  def encode(schema: org.apache.avro.Schema)(row: Row): Array[Byte] = {
    val gr: GenericRecord = new GenericData.Record(schema)
    row.schema.fieldNames.foreach(name => gr.put(name, row.getAs(name)))
  
    val writer = new SpecificDatumWriter[GenericRecord](schema)
    val out = new ByteArrayOutputStream()
    val encoder: BinaryEncoder = EncoderFactory.get().binaryEncoder(out, null)
    writer.write(gr, encoder)
    encoder.flush()
    out.close()
  
    out.toByteArray()
  }
  

• Convert it to udf:

  import org.apache.spark.sql.functions.udf
  
  val schema: org.apache.avro.Schema
  val encodeUDF = udf(encode(schema) _)
  

• Use it as drop in replacement for to_json

  eventDF.select(
    encodeUDF(struct(eventDF.columns.map(column):_*)).alias("value")
  )