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https://issues.apache.org/jira/browse/SPARK-35386?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17345606#comment-17345606
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Hyukjin Kwon commented on SPARK-35386:
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This is an intended behaviour to consider the case when schema is evolved
> 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
> Priority: Major
>
> When read schema is specified as I user I would prefer/like if spark failed
> on missing columns.
> {code:python}
> from pyspark.sql.dataframe import DoubleType, StructType
> 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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