[
https://issues.apache.org/jira/browse/SPARK-40808?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
ohad updated SPARK-40808:
-------------------------
Description:
Hello.
I am writing unit-tests to some functionality in my application that reading
data from CSV files using Spark.
I am reading the data using:
```
header=True
mergeSchema=True
inferSchema=True
```
When I am reading this single file:
```
Fi
"int_col","string_col","decimal_col","date_col"
1,"hello",1.43,2022-02-23
2,"world",5.534,2021-05-05
3,"my name",86.455,2011-08-15
4,"is ohad",6.234,2002-03-22
```
I am getting this schema:
```
int_col=int
string_col=string
decimal_col=double
date_col=string
```
When I am duplicating this file, I am getting the same schema.
The strange part is when I am adding new int column, it looks like spark is
getting confused and think that the column that already identified as int are
now string:
```
File1:
"int_col","string_col","decimal_col","date_col"
1,"hello",1.43,2022-02-23
2,"world",5.534,2021-05-05
3,"my name",86.455,2011-08-15
4,"is ohad",6.234,2002-03-22
File2:
"int_col","string_col","decimal_col","date_col","int2_col"
1,"hello",1.43,2022-02-23,234
2,"world",5.534,2021-05-05,5
3,"my name",86.455,2011-08-15,32
4,"is ohad",6.234,2002-03-22,2
```
result:
```
int_col=string
string_col=string
decimal_col=string
date_col=string
int2_col=int
```
When I am reading only the second file, it looks fine:
```
File2:
"int_col","string_col","decimal_col","date_col","int2_col"
1,"hello",1.43,2022-02-23,234
2,"world",5.534,2021-05-05,5
3,"my name",86.455,2011-08-15,32
4,"is ohad",6.234,2002-03-22,2
```
result:
```
int_col=int
string_col=string
decimal_col=double
date_col=string
int2_col=int
```
For conclusion, it looks like there is a bug mixing the two features: header
recognition and merge schema.
was:
Hello.
I am writing some unit-tests to some functionality in my application that
reading data from CSV files using Spark.
I am reading the data using:
```
header=True
mergeSchema=True
inferSchema=True
```
When I am reading this single file:
```
Fi
"int_col","string_col","decimal_col","date_col"
1,"hello",1.43,2022-02-23
2,"world",5.534,2021-05-05
3,"my name",86.455,2011-08-15
4,"is ohad",6.234,2002-03-22
```
I am getting this schema:
```
int_col=int
string_col=string
decimal_col=double
date_col=string
```
When I am duplicating this file, I am getting the same schema.
The strange part is when I am adding new int column, it looks like spark is
getting confused and think that the column that already identified as int are
now string:
```
File1:
"int_col","string_col","decimal_col","date_col"
1,"hello",1.43,2022-02-23
2,"world",5.534,2021-05-05
3,"my name",86.455,2011-08-15
4,"is ohad",6.234,2002-03-22
File2:
"int_col","string_col","decimal_col","date_col","int2_col"
1,"hello",1.43,2022-02-23,234
2,"world",5.534,2021-05-05,5
3,"my name",86.455,2011-08-15,32
4,"is ohad",6.234,2002-03-22,2
```
result:
```
int_col=string
string_col=string
decimal_col=string
date_col=string
int2_col=int
```
When I am reading only the second file, it looks fine:
```
File2:
"int_col","string_col","decimal_col","date_col","int2_col"
1,"hello",1.43,2022-02-23,234
2,"world",5.534,2021-05-05,5
3,"my name",86.455,2011-08-15,32
4,"is ohad",6.234,2002-03-22,2
```
result:
```
int_col=int
string_col=string
decimal_col=double
date_col=string
int2_col=int
```
For conclusion, it looks like there is a bug mixing the two features: header
recognition and merge schema.
> Infer schema for CSV files - wrong behavior using header + merge schema
> -----------------------------------------------------------------------
>
> Key: SPARK-40808
> URL: https://issues.apache.org/jira/browse/SPARK-40808
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 3.2.2
> Reporter: ohad
> Priority: Major
> Labels: CSVReader, csv, csvparser
>
> Hello.
> I am writing unit-tests to some functionality in my application that reading
> data from CSV files using Spark.
> I am reading the data using:
> ```
> header=True
> mergeSchema=True
> inferSchema=True
> ```
> When I am reading this single file:
> ```
> Fi
> "int_col","string_col","decimal_col","date_col"
> 1,"hello",1.43,2022-02-23
> 2,"world",5.534,2021-05-05
> 3,"my name",86.455,2011-08-15
> 4,"is ohad",6.234,2002-03-22
> ```
> I am getting this schema:
> ```
> int_col=int
> string_col=string
> decimal_col=double
> date_col=string
> ```
> When I am duplicating this file, I am getting the same schema.
> The strange part is when I am adding new int column, it looks like spark is
> getting confused and think that the column that already identified as int are
> now string:
> ```
> File1:
> "int_col","string_col","decimal_col","date_col"
> 1,"hello",1.43,2022-02-23
> 2,"world",5.534,2021-05-05
> 3,"my name",86.455,2011-08-15
> 4,"is ohad",6.234,2002-03-22
> File2:
> "int_col","string_col","decimal_col","date_col","int2_col"
> 1,"hello",1.43,2022-02-23,234
> 2,"world",5.534,2021-05-05,5
> 3,"my name",86.455,2011-08-15,32
> 4,"is ohad",6.234,2002-03-22,2
> ```
> result:
> ```
> int_col=string
> string_col=string
> decimal_col=string
> date_col=string
> int2_col=int
> ```
> When I am reading only the second file, it looks fine:
> ```
> File2:
> "int_col","string_col","decimal_col","date_col","int2_col"
> 1,"hello",1.43,2022-02-23,234
> 2,"world",5.534,2021-05-05,5
> 3,"my name",86.455,2011-08-15,32
> 4,"is ohad",6.234,2002-03-22,2
> ```
> result:
> ```
> int_col=int
> string_col=string
> decimal_col=double
> date_col=string
> int2_col=int
> ```
> For conclusion, it looks like there is a bug mixing the two features: header
> recognition and merge schema.
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