petastorm: Petastrom fails with big datasets

Using the code from the repo Github main page as reference, my code looks like follows:

spark = SparkSession.builder.config('spark.driver.memory', '10g').master('local[4]').getOrCreate()
sc = spark.sparkContext

with materialize_dataset(spark=spark, dataset_url='file:///opt/data/hello_world_dataset',
                         schema=MySchema, row_group_size_mb=256):

    logging.info('Building RDD...')
    rows_rdd = sc.parallelize(ids)\
        .flatMap(row_generator)\  # Generator that yields lists of examples
        .map(lambda x: dict_to_spark_row(MySchema, x))

    logging.info('Creating DataFrame...')
    spark.createDataFrame(rows_rdd, MySchema.as_spark_schema()) \
        .coalesce(10) \
        .write \
        .mode('overwrite') \
        .parquet('file:///opt/data/hello_world_dataset') 

Now the RDD code executes successfully but fails only the .createDataFrame call with the following error:

_pickle.PicklingError: Could not serialize broadcast: OverflowError: cannot serialize a string larger than 4GiB

This is my first experience with Spark, so I can’t really tell if this error originates in Spark or Petastorm.

Looking through other solutions to this error (in respect to Spark, not Petastorm) I saw that it might have to do with the pickling protocol, but I can’t confirm that, neither did I find a way of altering the pickling protocol.

How could I avoid this error?