Rafal Wojdyla created SPARK-35386:
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Summary: parquet read with schema should fail on non-existing
columns
Key: SPARK-35386
URL: https://issues.apache.org/jira/browse/SPARK-35386
Project: Spark
Issue Type: Bug
Components: Input/Output, PySpark
Affects Versions: 3.0.1
Reporter: Rafal Wojdyla
When read schema is specified as I user I would prefer/like if spark failed on
missing columns.
{code:python}
spark: SparkSession = ...
spark.read.parquet("/tmp/data.snappy.parquet")
# inferred schema, includes 3 columns: col1, col2, new_col
# DataFrame[col1: bigint, col2: bigint, new_col: bigint]
# let's specify a custom read_schema, with **non nullable** col3 (which is not
present):
read_schema = StructType(fields=[StructField("col3",DoubleType(),False)])
df = spark.read.schema(read_schema).parquet("/tmp/data.snappy.parquet")
df.schema
# we get a DataFrame with **nullable** col3:
# StructType(List(StructField(col3,DoubleType,true)))
df.count()
# 0
{code}
Is this a feature or a bug? In this case there's just a single parquet file, I
have also tried {{option("mergeSchema", "true")}}, which doesn't help.
Similar read pattern would fail on pandas (and likely dask).
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