GCP Dataflow: Handling Schema Evolution in Apache Beam with Avro
Quick question that's been bugging me - I've encountered a strange issue with I just started working with I'm working on a personal project and This might be a silly question, but I'm working on a Google Cloud Dataflow pipeline using Apache Beam (version 2.36.0) with Avro as the data serialization format, and I've run into challenges around handling schema evolution... My data source has been updated recently, introducing new fields in Avro schemas, and I want to ensure that my pipeline can handle both old and new records seamlessly. Currently, I have the following code to read from Pub/Sub and apply a transformation: ```java PCollection<GenericRecord> records = pipeline.apply("Read from Pub/Sub", PubsubIO.readAvro() .fromTopic("projects/my-project/topics/my-topic")) .apply("Transform Records", ParDo.of(new DoFn<GenericRecord, MyOutputType>() { @ProcessElement public void processElement(ProcessContext c) { GenericRecord input = c.element(); // Process input record MyOutputType output = new MyOutputType(); output.setField1(input.get("field1")); output.setField2(input.get("field2", "default_value")); // new schema field c.output(output); } })); ``` After upgrading to the new schema, I'm encountering `java.lang.NullPointerException` when trying to access the newly added fields for old records that don’t contain them. I've tried using `GenericData.get().getField()` with default values, but it doesn’t seem to be working as intended. I also considered using a custom deserializer for Avro but I'm not sure how to implement it within the Beam pipeline context. Any advice on how to effectively manage schema evolution in this setup? Are there any best practices or specific approaches to avoid these null pointer exceptions while ensuring backward compatibility? Has anyone else encountered this? For context: I'm using Java on Windows. How would you solve this? Hoping someone can shed some light on this. I'm working in a CentOS environment.