How to handle exception while parsing JSON in Flink - apache-flink

Question

I am reading data from Kafka using flink 1.4.2 and parsing them to ObjectNode using JSONDeserializationSchema. If the incoming record is not a valid JSON then my Flink job fails. I would like to skip the broken record instead of failing the job.
FlinkKafkaConsumer010<ObjectNode> kafkaConsumer =
new FlinkKafkaConsumer010<>(TOPIC, new JSONDeserializationSchema(), consumerProperties);
DataStream<ObjectNode> messageStream = env.addSource(kafkaConsumer);
messageStream.print();
I am getting the following exception if the data in Kafka is not a valid JSON.
Job execution switched to status FAILING.
org.apache.flink.shaded.jackson2.com.fasterxml.jackson.core.JsonParseException: Unrecognized token 'This': was expecting ('true', 'false' or 'null')
at [Source: [B#4f522623; line: 1, column: 6]
Job execution switched to status FAILED.
Exception in thread "main" org.apache.flink.runtime.client.JobExecutionException: Job execution failed.

Answer 1

The easiest solution is to implement your own DeserializationSchema and wrap JSONDeserializationSchema. You can then catch the exception and either ignore it or perform custom action.

Answer 2

As suggested by #twalthr, I implemented my own DeserializationSchema by copying JSONDeserializationSchema and added exception handling.
import org.apache.flink.api.common.serialization.AbstractDeserializationSchema;
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.ObjectMapper;
import org.apache.flink.shaded.jackson2.com.fasterxml.jackson.databind.node.ObjectNode;
import java.io.IOException;
public class CustomJSONDeserializationSchema extends AbstractDeserializationSchema<ObjectNode> {
private ObjectMapper mapper;
#Override
public ObjectNode deserialize(byte[] message) throws IOException {
if (mapper == null) {
mapper = new ObjectMapper();
}
ObjectNode objectNode;
try {
objectNode = mapper.readValue(message, ObjectNode.class);
} catch (Exception e) {
ObjectMapper errorMapper = new ObjectMapper();
ObjectNode errorObjectNode = errorMapper.createObjectNode();
errorObjectNode.put("jsonParseError", new String(message));
objectNode = errorObjectNode;
}
return objectNode;
}
#Override
public boolean isEndOfStream(ObjectNode nextElement) {
return false;
}
}
In my streaming job.
messageStream
.filter((event) -> {
if(event.has("jsonParseError")) {
LOG.warn("JsonParseException was handled: " + event.get("jsonParseError").asText());
return false;
}
return true;
}).print();

Answer 3

Flink has improved null record handling for FlinkKafkaConsumer
There are two possible design choices when the DeserializationSchema encounters a corrupted message. It can either throw an IOException which causes the pipeline to be restarted, or it can return null where the Flink Kafka consumer will silently skip the corrupted message.
For more details, you can see this link.