Description
I have a Spark Structured Streaming job where I read Avro messages from Kafka, convert them to a Spark DataFrame using Abris, and then write the stream as a Delta table to S3. Below is the relevant configuration and code snippet.

Abris Configuration

val abrisConfig = AbrisConfig.fromConfluentAvro
  .downloadReaderSchemaByVersion(1)
  .andRecordNameStrategy(schemaName, schemaNamespace)
  .usingSchemaRegistry(schemaRegistryUrl)
  .withExceptionHandler(new PermissiveRecordExceptionHandler())

Code Example
Here’s an example where I write the stream to the console for demonstration purposes:

val df = reader
.readKafkaStream(topic, kafkaConfig, options)
.withColumnRenamed("headers", KAFKA_HEADERS)
.withColumn("kafka_meta", map_from_entries(col(KAFKA_HEADERS)))
.withColumn("kafka_meta_column", col("kafka_meta.meta"))
.withColumn(KAFKA_HEADERS_META, from_avro(col("kafka_meta_column"), abrisConfig))

df.select(KAFKA_HEADERS_META).writeStream
.outputMode("append")
.format("console")
.option("truncate", "true")
.start()

The kafka_meta.meta column contains the Avro message I want to deserialize using from_avro. The schema for the Avro message is as follows:

{
  "type": "record",
  "name": "Example",
  "fields": [
    { "name": "id", "type": "string" },
    { "name": "timestamp", "type": "long" }
  ]
}

Problem
I encounter the following error during execution:
Caused by: java.lang.NullPointerException

Upon investigation, I concluded that this occurs because the PermissiveRecordExceptionHandler returns a null record for malformed data as expected. However, downstream transformations attempt to access the nested field meta (the column containing the Avro message), resulting in the NullPointerException.

Note: None of the columns originally contain null values, so the issue is not caused by the source data.

Question
How can I handle this scenario to avoid the NullPointerException while keeping the permissive behavior of the exception handler? Any guidance would be appreciated.

Thank you!