Martin Rueckl created SPARK-46876:
-------------------------------------
Summary: Data is silently lost in Tab separated CSV with empty
(whitespace) rows
Key: SPARK-46876
URL: https://issues.apache.org/jira/browse/SPARK-46876
Project: Spark
Issue Type: Bug
Components: Input/Output
Affects Versions: 3.4.1
Reporter: Martin Rueckl
When reading a tab separated file that contains lines that only contain tabs
(i.e. empty strings as values of the columns for that row), then these rows
will silently be skipped (as empty lines) and the resulting dataframe will have
less rows than expected.
This behavior is inconsistent with the behavior for e.g. semicolon separated
files, where the resulting dataframe will have a row with only empty string
values.
A minimal reproducible example would look like:
A minimal reproducible example: A file containing this
{{{{}}}}
{code:java}
a\tb\tc\r\n
\t\t\r\n
1\t2\t3{code}
will create a dataframe with one row (a=1,b=2,c=3)
whereas this
{code:java}
a;b;c\r\n
;;\r\n
1;2;3{code}
will read as two rows (first row contains empty strings)
I used the following pyspark command to read the dataframes
{code:java}
spark.read.option("header","true").option("sep","\t").csv("<tabseparated
file>").collect()
spark.read.option("header","true").option("sep",";").csv("<semicolon
file>").collect()
{code}
I ran into this particularly on databricks (I assume they use the same reader),
but [this stack overflow
post|https://stackoverflow.com/questions/47823858/replacing-empty-lines-with-characters-when-reading-csv-using-spark#comment137288546_47823858]
indicates, that this is an old issue that may have been taken over from
databricks when their csv reader was adopted in this PR:
I recommend to at least add a respective test case to the CSV reader.
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