zhengruifeng commented on PR #40402:
URL: https://github.com/apache/spark/pull/40402#issuecomment-1477209035

   I save a df with UDT in pyspark, and then read it in python client, and it 
works fine. So I guess something is wrong in 
   vanilla PySpark:
   In [1]: from pyspark.ml.linalg import Vectors
   In [2]: df = spark.createDataFrame([(1.0, 1.0, Vectors.dense(0.0, 5.0)), 
(0.0, 2.0, Vectors.dense(1.0, 2.0)), (1.0, 3.0, Vectors.dense(2.0,
      ...:    ...: 1.0)), (0.0, 4.0, Vectors.dense(3.0, 3.0)),], ["label", 
"weight", "features"],)
   In [3]: df.write.parquet("/tmp/tmp.pq")
   Python Client:
   In [6]: df = spark.read.parquet("/tmp/tmp.pq")
   In [7]: df.schema
   Out[7]: StructType([StructField('label', DoubleType(), True), 
StructField('weight', DoubleType(), True), StructField('features', VectorUDT(), 
   In [8]: df.collect()
   [Row(label=0.0, weight=4.0, features=DenseVector([3.0, 3.0])),
    Row(label=0.0, weight=2.0, features=DenseVector([1.0, 2.0])),
    Row(label=1.0, weight=3.0, features=DenseVector([2.0, 1.0])),
    Row(label=1.0, weight=1.0, features=DenseVector([0.0, 5.0]))]

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