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https://issues.apache.org/jira/browse/SPARK-20457?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Himanshu Gupta updated SPARK-20457:
-----------------------------------
Description:
I have a CSV file, test.csv:
{code:xml}
col
1
2
3
4
{code}
When I read it using Spark, it gets the schema of data correct:
{code:java}
val df = spark.read.option("header", "true").option("inferSchema",
"true").csv("test.csv")
df.printSchema
root
|-- col: integer (nullable = true)
{code}
But when I override the `schema` of CSV file and make `inferSchema` false, then
SparkSession is picking up custom schema partially.
{code:java}
val df = spark.read.option("header", "true").option("inferSchema",
"false").schema(StructType(List(StructField("custom", StringType,
false)))).csv("test.csv")
df.printSchema
root
|-- custom: string (nullable = true)
{code}
I mean only column name (`custom`) and DataType (`StringType`) are getting
picked up. But, `nullable` part is being ignored, as it is still coming
`nullable = true`, which is incorrect.
I am not able to understand this behavior.
was:
I have a CSV file, test.csv:
{code:csv}
col
1
2
3
4
{code}
When I read it using Spark, it gets the schema of data correct:
{code:java}
val df = spark.read.option("header", "true").option("inferSchema",
"true").csv("test.csv")
df.printSchema
root
|-- col: integer (nullable = true)
{code}
But when I override the `schema` of CSV file and make `inferSchema` false, then
SparkSession is picking up custom schema partially.
{code:java}
val df = spark.read.option("header", "true").option("inferSchema",
"false").schema(StructType(List(StructField("custom", StringType,
false)))).csv("test.csv")
df.printSchema
root
|-- custom: string (nullable = true)
{code}
I mean only column name (`custom`) and DataType (`StringType`) are getting
picked up. But, `nullable` part is being ignored, as it is still coming
`nullable = true`, which is incorrect.
I am not able to understand this behavior.
> Spark CSV is not able to Override Schema while reading data
> -----------------------------------------------------------
>
> Key: SPARK-20457
> URL: https://issues.apache.org/jira/browse/SPARK-20457
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Himanshu Gupta
>
> I have a CSV file, test.csv:
> {code:xml}
> col
> 1
> 2
> 3
> 4
> {code}
> When I read it using Spark, it gets the schema of data correct:
> {code:java}
> val df = spark.read.option("header", "true").option("inferSchema",
> "true").csv("test.csv")
>
> df.printSchema
> root
> |-- col: integer (nullable = true)
> {code}
> But when I override the `schema` of CSV file and make `inferSchema` false,
> then SparkSession is picking up custom schema partially.
> {code:java}
> val df = spark.read.option("header", "true").option("inferSchema",
> "false").schema(StructType(List(StructField("custom", StringType,
> false)))).csv("test.csv")
> df.printSchema
> root
> |-- custom: string (nullable = true)
> {code}
> I mean only column name (`custom`) and DataType (`StringType`) are getting
> picked up. But, `nullable` part is being ignored, as it is still coming
> `nullable = true`, which is incorrect.
> I am not able to understand this behavior.
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