How to read records in JSON format from Kafka using Structured Streaming?

I am trying to use structured streaming approach using Spark-Streaming based on DataFrame/Dataset API to load a stream of data from Kafka.

I use:

  • Spark 2.10
  • Kafka 0.10
  • spark-sql-kafka-0-10

Spark Kafka DataSource has defined underlying schema:


My data come in json format and they are stored in the value column. I am looking for a way how to extract underlying schema from value column and update received dataframe to columns stored in value? I tried the approach below but it does not work:

 val columns = Array("column1", "column2") // column names
 val rawKafkaDF = sparkSession.sqlContext.readStream
  val columnsToSelect = x => new Column("value." + x))
  val kafkaDF =*)

  // some analytics using stream dataframe kafkaDF

  val query = kafkaDF.writeStream.format("console").start()

Here I am getting Exception org.apache.spark.sql.AnalysisException: Can't extract value from value#337; because in time of creation of the stream, values inside are not known...

Do you have any suggestions?

Solution 1:

From the Spark perspective value is just a byte sequence. It has no knowledge about the serialization format or content. To be able to extract the filed you have to parse it first.

If data is serialized as a JSON string you have two options. You can cast value to StringType and use from_json and provide a schema:

import org.apache.spark.sql.types._
import org.apache.spark.sql.functions.from_json

val schema: StructType = StructType(Seq(
  StructField("column1", ???),
  StructField("column2", ???)
))$"value".cast(StringType), schema))

or cast to StringType, extract fields by path using get_json_object:

import org.apache.spark.sql.functions.get_json_object

val columns: Seq[String] = ???

val exprs = => get_json_object($"value", s"$$.$c")) _*)

and cast later to the desired types.