Integrating Spark Structured Streaming with the Confluent Schema Registry
It took me a couple months of reading source code and testing things out. In a nutshell, Spark can only handle String and Binary serialization. You must manually deserialize the data. In spark, create the confluent rest service object to get the schema. Convert the schema string in the response object into an Avro schema using the Avro parser. Next, read the Kafka topic as normal. Then map over the binary typed "value" column with the Confluent KafkaAvroDeSerializer. I strongly suggest getting into the source code for these classes because there is a lot going on here, so for brevity I'll leave out many details.
//Used Confluent version 3.2.2 to write this.
import io.confluent.kafka.schemaregistry.client.rest.RestService
import io.confluent.kafka.serializers.KafkaAvroDeserializer
import org.apache.avro.Schema
case class DeserializedFromKafkaRecord(key: String, value: String)
val schemaRegistryURL = "http://127.0.0.1:8081"
val topicName = "Schema-Registry-Example-topic1"
val subjectValueName = topicName + "-value"
//create RestService object
val restService = new RestService(schemaRegistryURL)
//.getLatestVersion returns io.confluent.kafka.schemaregistry.client.rest.entities.Schema object.
val valueRestResponseSchema = restService.getLatestVersion(subjectValueName)
//Use Avro parsing classes to get Avro Schema
val parser = new Schema.Parser
val topicValueAvroSchema: Schema = parser.parse(valueRestResponseSchema.getSchema)
//key schema is typically just string but you can do the same process for the key as the value
val keySchemaString = "\"string\""
val keySchema = parser.parse(keySchemaString)
//Create a map with the Schema registry url.
//This is the only Required configuration for Confluent's KafkaAvroDeserializer.
val props = Map("schema.registry.url" -> schemaRegistryURL)
//Declare SerDe vars before using Spark structured streaming map. Avoids non serializable class exception.
var keyDeserializer: KafkaAvroDeserializer = null
var valueDeserializer: KafkaAvroDeserializer = null
//Create structured streaming DF to read from the topic.
val rawTopicMessageDF = sql.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "127.0.0.1:9092")
.option("subscribe", topicName)
.option("startingOffsets", "earliest")
.option("maxOffsetsPerTrigger", 20) //remove for prod
.load()
//instantiate the SerDe classes if not already, then deserialize!
val deserializedTopicMessageDS = rawTopicMessageDF.map{
row =>
if (keyDeserializer == null) {
keyDeserializer = new KafkaAvroDeserializer
keyDeserializer.configure(props.asJava, true) //isKey = true
}
if (valueDeserializer == null) {
valueDeserializer = new KafkaAvroDeserializer
valueDeserializer.configure(props.asJava, false) //isKey = false
}
//Pass the Avro schema.
val deserializedKeyString = keyDeserializer.deserialize(topicName, row.key, keySchema).toString //topic name is actually unused in the source code, just required by the signature. Weird right?
val deserializedValueString = valueDeserializer.deserialize(topicName, row.value, topicValueAvroSchema).toString
DeserializedFromKafkaRecord(deserializedKeyString, deserializedValueString)
}
val deserializedDSOutputStream = deserializedTopicMessageDS.writeStream
.outputMode("append")
.format("console")
.option("truncate", false)
.start()
Disclaimer
This code was only tested on a local master, and has been reported runs into serializer issues in a clustered environment. There's an alternative solution (step 7-9, with Scala code in step 10) that extracts out the schema ids to columns, looks up each unique ID, and then uses schema broadcast variables, which will work better, at scale.
Also, there is an external library AbsaOSS/ABRiS that also addresses using the Registry with Spark
Since the other answer that was mostly useful was removed, I wanted to re-add it with some refactoring and comments.
Here are the dependencies needed. Code tested with Confluent 5.x and Spark 2.4
<dependency>
<groupId>io.confluent</groupId>
<artifactId>kafka-avro-serializer</artifactId>
<version>${confluent.version}</version>
<exclusions>
<!-- Conflicts with Spark's version -->
<exclusion>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-clients</artifactId>
</exclusion>
</exclusions>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql-kafka-0-10_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-avro_${scala.version}</artifactId>
<version>${spark.version}</version>
</dependency>
And here is the Scala implementation (only tested locally on master=local[*])
First section, define the imports, some fields, and a few helper methods to get schemas
import io.confluent.kafka.schemaregistry.client.{CachedSchemaRegistryClient, SchemaRegistryClient}
import io.confluent.kafka.serializers.AbstractKafkaAvroDeserializer
import org.apache.avro.Schema
import org.apache.avro.generic.GenericRecord
import org.apache.commons.cli.CommandLine
import org.apache.spark.sql._
import org.apache.spark.sql.avro.SchemaConverters
import org.apache.spark.sql.streaming.OutputMode
object App {
private var schemaRegistryClient: SchemaRegistryClient = _
private var kafkaAvroDeserializer: AvroDeserializer = _
def lookupTopicSchema(topic: String, isKey: Boolean = false) = {
schemaRegistryClient.getLatestSchemaMetadata(topic + (if (isKey) "-key" else "-value")).getSchema
}
def avroSchemaToSparkSchema(avroSchema: String) = {
SchemaConverters.toSqlType(new Schema.Parser().parse(avroSchema))
}
// ... continues below
Then define a simple main method that parses the CMD args to get Kafka details
def main(args: Array[String]): Unit = {
val cmd: CommandLine = parseArg(args)
val master = cmd.getOptionValue("master", "local[*]")
val spark = SparkSession.builder()
.appName(App.getClass.getName)
.master(master)
.getOrCreate()
val bootstrapServers = cmd.getOptionValue("bootstrap-server")
val topic = cmd.getOptionValue("topic")
val schemaRegistryUrl = cmd.getOptionValue("schema-registry")
consumeAvro(spark, bootstrapServers, topic, schemaRegistryUrl)
spark.stop()
}
// ... still continues
Then, the important method that consumes the Kafka topic and deserializes it
private def consumeAvro(spark: SparkSession, bootstrapServers: String, topic: String, schemaRegistryUrl: String): Unit = {
import spark.implicits._
// Setup the Avro deserialization UDF
schemaRegistryClient = new CachedSchemaRegistryClient(schemaRegistryUrl, 128)
kafkaAvroDeserializer = new AvroDeserializer(schemaRegistryClient)
spark.udf.register("deserialize", (bytes: Array[Byte]) =>
kafkaAvroDeserializer.deserialize(bytes)
)
// Load the raw Kafka topic (byte stream)
val rawDf = spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers", bootstrapServers)
.option("subscribe", topic)
.option("startingOffsets", "earliest")
.load()
// Deserialize byte stream into strings (Avro fields become JSON)
import org.apache.spark.sql.functions._
val jsonDf = rawDf.select(
// 'key.cast(DataTypes.StringType), // string keys are simplest to use
callUDF("deserialize", 'key).as("key"), // but sometimes they are avro
callUDF("deserialize", 'value).as("value")
// excluding topic, partition, offset, timestamp, etc
)
// Get the Avro schema for the topic from the Schema Registry and convert it into a Spark schema type
val dfValueSchema = {
val rawSchema = lookupTopicSchema(topic)
avroSchemaToSparkSchema(rawSchema)
}
// Apply structured schema to JSON stream
val parsedDf = jsonDf.select(
'key, // keys are usually plain strings
// values are JSONified Avro records
from_json('value, dfValueSchema.dataType).alias("value")
).select(
'key,
$"value.*" // flatten out the value
)
// parsedDf.printSchema()
// Sample schema output
// root
// |-- key: string (nullable = true)
// |-- header: struct (nullable = true) // Not a Kafka record "header". This is part of our value schema
// | |-- time: long (nullable = true)
// | ...
// TODO: Do something interesting with this stream
parsedDf.writeStream
.format("console")
.outputMode(OutputMode.Append())
.option("truncate", false)
.start()
.awaitTermination()
}
// still continues
The command line parser allows for passing in bootstrap servers, schema registry, topic name, and Spark master.
private def parseArg(args: Array[String]): CommandLine = {
import org.apache.commons.cli._
val options = new Options
val masterOption = new Option("m", "master", true, "Spark master")
masterOption.setRequired(false)
options.addOption(masterOption)
val bootstrapOption = new Option("b", "bootstrap-server", true, "Bootstrap servers")
bootstrapOption.setRequired(true)
options.addOption(bootstrapOption)
val topicOption = new Option("t", "topic", true, "Kafka topic")
topicOption.setRequired(true)
options.addOption(topicOption)
val schemaRegOption = new Option("s", "schema-registry", true, "Schema Registry URL")
schemaRegOption.setRequired(true)
options.addOption(schemaRegOption)
val parser = new BasicParser
parser.parse(options, args)
}
// still continues
In order for the UDF above to work, then there needed to be a deserializer to take the DataFrame of bytes to one containing deserialized Avro
// Simple wrapper around Confluent deserializer
class AvroDeserializer extends AbstractKafkaAvroDeserializer {
def this(client: SchemaRegistryClient) {
this()
// TODO: configure the deserializer for authentication
this.schemaRegistry = client
}
override def deserialize(bytes: Array[Byte]): String = {
val value = super.deserialize(bytes)
value match {
case str: String =>
str
case _ =>
val genericRecord = value.asInstanceOf[GenericRecord]
genericRecord.toString
}
}
}
} // end 'object App'
Put each of these blocks together, and it works in IntelliJ after adding -b localhost:9092 -s http://localhost:8081 -t myTopic to Run Configurations > Program Arguments