make_spark_converter RuntimeError: Vector columns are only supported in pyspark>=3.0

[Alxe1]

I convert pyspark dataframe to two columns: one for feature column, it's a dense vector, and another is a label column. When I transform to tensorflow dataset using make_spark_converter, it raised an error:

/mnt/softwares/hvd_env/lib/python3.7/site-packages/petastorm/spark/spark_dataset_converter.py:28: FutureWarning: pyarrow.LocalFileSystem is deprecated as of 2.0.0, please use pyarrow.fs.LocalFileSystem instead.
  from pyarrow import LocalFileSystem
/mnt/softwares/hvd_env/lib/python3.7/site-packages/petastorm/hdfs/namenode.py:270: FutureWarning: pyarrow.hdfs.connect is deprecated as of 2.0.0, please use pyarrow.fs.HadoopFileSystem instead.
  return pyarrow.hdfs.connect(hostname, url.port or 8020, **kwargs)
Traceback (most recent call last):
  File "/mytest/tf_with_spark.py", line 381, in <module>
    train_test()
  File "/mytest/tf_with_spark.py", line 345, in train_test
    converter = make_spark_converter(train_transformed_sdf)
  File "/mnt/softwares/hvd_env/lib/python3.7/site-packages/petastorm/spark/spark_dataset_converter.py", line 696, in make_spark_converter
    df, parent_cache_dir_url, parquet_row_group_size_bytes, compression_codec, dtype)
  File "/mnt/softwares/hvd_env/lib/python3.7/site-packages/petastorm/spark/spark_dataset_converter.py", line 512, in _cache_df_or_retrieve_cache_data_url
    compression_codec, dtype)
  File "/mnt/softwares/hvd_env/lib/python3.7/site-packages/petastorm/spark/spark_dataset_converter.py", line 436, in create_cached_dataframe_meta
    dtype=dtype)
  File "/mnt/softwares/hvd_env/lib/python3.7/site-packages/petastorm/spark/spark_dataset_converter.py", line 579, in _materialize_df
    df = _convert_vector(df, dtype)
  File "/mnt/softwares/hvd_env/lib/python3.7/site-packages/petastorm/spark/spark_dataset_converter.py", line 558, in _convert_vector
    vector_to_array(df[col_name], dtype))
  File "/mnt/softwares/hvd_env/lib/python3.7/site-packages/petastorm/spark/spark_dataset_converter.py", line 40, in vector_to_array
    raise RuntimeError("Vector columns are only supported in pyspark>=3.0")
RuntimeError: Vector columns are only supported in pyspark>=3.0

Does it not support pyspark < 3.0? But in the setup.py file I see it required 'pyspark>=2.1.0'. How to salve this problem?

[selitvin]

Would be easier to address if you could post a runnable code snippet. Would you be able to post such snippet?

[Alxe1]

Would be easier to address if you could post a runnable code snippet. Would you be able to post such snippet?

conf = SparkConf().setAppName("test")
spark = SparkSession.builder.config(conf=conf).enableHiveSupport().getOrCreate()
spark.conf.set(SparkDatasetConverter.PARENT_CACHE_DIR_URL_CONF, 'file://')

df = pd.DataFrame({'x': [0, 1, 2, 3], "y": [6, 2, 5, 7], "z": [0, 0, 1, 1]})
sdf = spark.createDataFrame(df)

vector_assembler = VectorAssembler(inputCols=["x", "y"], outputCol="features")
sdf = vector_assembler.transform(sdf)
sdf = sdf.select("features", "z")
sdf.show()

converter = make_spark_converter(sdf)

[selitvin]

Don't have enough spark knowledge to give an accurate answer. Perhaps @WeichenXu123 can weigh in?

[OscarDPan]

https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.ml.functions.vector_to_array.html
I think in the documentation it's clear that you need to have Spark 3.0