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Spark Custom Kafka Partitioner

 

Custom Partitoner can be implemented by extending org.apache.kafka.clients.producer.Partitioner. 

This can be used with Spark-SQL Kafka Data Source by setting property "kafka.partitioner.class"


For example 

df.write.format("kafka").option("kafka.partitioner.class", "com.mycustom.ipartitioner")


We implemented one such custom partitioner extending org.apache.kafka.clients.producer.RoundRobinPartitioner. 

Complete Source code is available @ https://github.com/dinesh028/engineering/blob/master/Kafka/com/aquaifer/producer/KeyPartitioner.scala


This paritioner  - 

  • Reads a configuration file which has Kafka Key and PrimaryKey Name mapping. Value in Kafka is a JSON Message which has a Primary Key with unique Value. 
  • Idea is to partition messages based on this unique value, such that messages with same value for primarykey go into same partition.
  • Once, configurations are loaded. For each byte array message- 
    • convert it to String JSON
    • Parse JSON
    • Get unique value for  PrimaryKey.
    • Do Modulus of  Postive Hash Code of Value by available partition in Kafka Topic.
    • The output of Modulus determines the partition for given message.

Also, we noticed that custom properties are not passed to Partitioner  configurations, w.r.t same - we did raise a defect with Spark. Refer - https://issues.apache.org/jira/browse/SPARK-45666

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