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
`createDataFrame`
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(),
True)])
In [8]: df.collect()
Out[8]:
[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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