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Rafal Wojdyla commented on SPARK-35386: --------------------------------------- [~hyukjin.kwon] Thanks for the prompt reply. But in the case when the user explicitly specifies the read schema column as **required** wouldn't it make more sense to fail (or at least control the behaviour with a flag)? > 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). -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org