Thursday, July 4, 2019

Serializing Spark dataframes to Avro using KafkaAvroSerializer

I recently worked on a project that used Spark Structured Streaming using Apache Spark, Confluent Schema Registry and Apache Kafka. Due to some versioning constraints between the various components, I had to write a custom implementation of the KafkaAvroSerializer class for serializing Spark Dataframes into Avro format. The serialized data was then published to Kafka. This post is based on the examples specified in the Confluent documentation here.

In newer versions of Confluent Schema Registry, lot of the implementations detailed below have been simplified and much easier to use. The standard recommended usage of the Confluent KafkaAvroSerializer is fairly simple in that it requires you to set it as one of the Kafka properties that is used when initializing a KafkaProducer:

val kafkaProperties = new Properties();
props.put(...)
...
...
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, io.confluent.kafka.serializers.KafkaAvroSerializer.class
val producer = new KafkaProducer(props);

This abstracts out many of the implementation specifics and details. The way this works is that when the object to be published to Kafka is sent using the KafkaProducer, internally the KafkaAvroSerializer does the following:


  1. Use the provided schema and initialise an internal cache that is used to store the schema for future use.
  2. Register the schema at the Schema Registry URL provided at a path that corresponds to KAFKA_KEY_NAME-value.
  3. If the publishing in (2) is successful and the serialisation process succeeds, the serialized message is returned as type Array[Byte]
Implementation using Spark dataframes

Each row in our dataframe corresponds to a single Kafka message. The message key is of type String while the message value would be an array of bytes. A custom implementation requires a few things to be pulled out from under the hood. In this case, we perform three specific tasks:

  1. Get an existing schema from a known source
  2. Initialise the KafkaAvroSerializer object
  3. Serialize the data using the object defined in (2) and publish it to Kafka using the standard KafkaProducer

The rest of this post covers three areas of the process which are manually implemented.

Get an existing schema from a SchemaRegistry URL:

In the implementation detailed below, we first define the required CachedSchemaRegistry client and use it to fetched the schema from an endpoint by providing a Subject name.

df.foreachPartition(currentPartition => {
val client = new CachedSchemaRegistryClient("http://localhost:80/", Integer.MAX_VALUE)
val schema = client.getLatestSchemaMetadata("YOUR_SUBJECT_NAME-value").getSchema
val schemaParser = new Schema.Parser
val parsedSchema = schemaParser.parse(schema)
})
Initialise the serializer

Using the client defined above and kafka properties defined earlier, we then initialise the KafkaAvroSerializer. The serializer registers the schema at the URL specified when it serializes an object. We also initialise the Kafka producer at this stage for each partition so that it can be reused by each row in the partition. By defining these objects within the foreachPartition, we can ensure that they can be serialized correctly thereby avoiding any SerializationException errors.:

val serializer = new KafkaAvroSerializer(client)
serializer.configure(kafkaProperties, false)
val producer = new KafkaProducer[String, Array[Byte]](kafkaProperties)
Serialize the data and publish to Kafka

One of the side effects of using the KafkaAvroSerializer is that it attempts to register the schema at the specified SchemaRegistry URL at the path KAFKA-KEY-value. The newer versions of SchemaRegistry include the option to disable this, however, the older version I worked with did not. Lastly, as mentioned earlier, each row of our dataframe corresponds to a single Kafka message and needs to be serialized individually. We begin doing this by creating the GenericData.Record object and passing the schema to it. We then use it to convert each Row object to it. Specific implementation for this is included in the supplementary section below:

currentPartition.foreach(row > {
val avroRecord = new GenericData.Record(parsedSchema)
val serializedMessage = serializer.serialize(key, avroRecord)
producer.send(new ProducerRecord[String, Array[Byte]](KAFKA_TOPIC_NAME, key, serializedMessage))
})
producer.flush()
producer.close()


Supplementary: Code snippet


2 comments :

  1. Hi,
    can u please share full code as i was facing some issues. Github link will be helpful.

    ReplyDelete
    Replies
    1. The CustomKafkaAvroSerializer.scala is all there is to it. All the operations on the dataframe are contained within it.

      If you could post your specific issue, perhaps, it would be useful.

      Delete