带有 Spark 3.0.1 结构化流的 Kafka：ClassException：org.apache.kafka.common.TopicPartition； class 对反序列化无效

Question

I am trying to read the kafka messages in google dataproc using pyspark - structured streaming.

Version details are:

1. dataproc image verison is 2.0.0-RC22-debian10 (to get pyspark 3.0.1 verison with delta lake 0.7.0 as I have to finally write this data to delta hosted on google storage)
2. pyspark version 3.0.1 and python version used by pyspark is 3.7.3
3. The packages I am using is org.apache.spark:spark-sql-kafka-0-10_2.12:3.0.1
   io.delta:delta-core_2.12:0.7.0
   org.apache.spark:spark-avro_2.12:3.0.1

Snippet of the code is:

__my_dir = os.path.dirname("<directory_path>") 
jsonFormatSchema = open(os.path.join(__my_dir, 'avro_schema_file.avsc'), "r").read() 

df = spark \    
    .readStream \    
    .format("kafka") \   
    .option("kafka.bootstrap.servers", "<kafka_broker>") \   
    .option("subscribe", "<kafka_topic>") \    
    .option("startingOffsets", "latest") \    
    .load()\    
    .select(from_avro("value", jsonFormatSchema)
    .alias("element"))

df.printSchema()
 
df_output =     df.select("element.after.id","element.after.name","element.after.attribute","element.after.quantity")

StreamQuery = ( df_output.writeStream \ 
               .format("delta") \   
               .outputMode("append") \   
               .option("checkpointLocation","<check_point_location>") \   
               .trigger(once=True) \    
               .start("<target_delta_table>") \    )

Error I am getting is:

java.io.InvalidClassException: org.apache.kafka.common.TopicPartition;
class invalid for deserialization

Why spark fails to deserialize TopicPartition and how can I solve it?

Answer 1

The following post helped to resolve this issue: How to use Confluent Schema Registry with from_avro standard function?

In addition, we started pointing to the following jars for kafka-client

kafka-clients-2.4.1.jar

Answer 2

The error disappears when you set the master to local[*] . Anyhow, the problem seems to be related with a jar conflict between the driver and the executor: they use different versions of the kafka-clients library.

To solve the problem you may want to launch jobs with

gcloud dataproc jobs submit spark \
 --class <YOUR_CLASS> \
 --jars target/scala-2.12/<COMPILED_JAR_FILE>.jar,kafka-clients-2.4.1.jar  \
 --cluster <CLUSTER_NAME> \
 --region= <YOUR_REGION> \
 --properties spark.jars.packages=org.apache.spark:spark-sql-kafka-0-10_2.12:3.0.1,spark.executor.extraClassPath=org.apache.kafka_kafka-clients-2.4.1.jar,spark.driver.extraClassPath=org.apache.kafka_kafka-clients-2.4.1.jar

This works in my case. Please check https://mvnrepository.com/artifact/org.apache.spark/spark-sql-kafka-0-10_2.12/3.0.1 for further details on versions.