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https://issues.apache.org/jira/browse/SPARK-15840?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ernst Sjöstrand updated SPARK-15840:
------------------------------------
Description:
When testing the new csv reader I found that it would not determine the input
schema as is stated in the documentation.
(I used this documentation:
https://people.apache.org/~pwendell/spark-nightly/spark-master-docs/latest/api/python/pyspark.sql.html#pyspark.sql.SQLContext
)
So either there is a bug in the implementation or in the documentation.
This also means that things like dateFormat are ignore it seems like.
Here's a quick test in pyspark (using Python3):
a = spark.read.csv("/home/ernst/test.csv")
a.printSchema()
print(a.dtypes)
a.show()
{noformat}
root
|-- _c0: string (nullable = true)
[('_c0', 'string')]
+---+
|_c0|
+---+
| 1|
| 2|
| 3|
| 4|
+---+
{noformat}
was:
When testing the new csv reader I found that it would not determine the input
schema as is stated in the documentation.
(I used this documentation:
https://people.apache.org/~pwendell/spark-nightly/spark-master-docs/latest/api/python/pyspark.sql.html#pyspark.sql.SQLContext
)
So either there is a bug in the implementation or in the documentation.
This also means that things like dateFormat are ignore it seems like.
Here's a quick test in pyspark (using Python3):
a = spark.read.csv("/home/ernst/test.csv")
a.printSchema()
print(a.dtypes)
a.show()
root
|-- _c0: string (nullable = true)
[('_c0', 'string')]
+---+
|_c0|
+---+
| 1|
| 2|
| 3|
| 4|
+---+
> New csv reader does not "determine the input schema"
> ----------------------------------------------------
>
> Key: SPARK-15840
> URL: https://issues.apache.org/jira/browse/SPARK-15840
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 2.0.0
> Reporter: Ernst Sjöstrand
>
> When testing the new csv reader I found that it would not determine the input
> schema as is stated in the documentation.
> (I used this documentation:
> https://people.apache.org/~pwendell/spark-nightly/spark-master-docs/latest/api/python/pyspark.sql.html#pyspark.sql.SQLContext
> )
> So either there is a bug in the implementation or in the documentation.
> This also means that things like dateFormat are ignore it seems like.
> Here's a quick test in pyspark (using Python3):
> a = spark.read.csv("/home/ernst/test.csv")
> a.printSchema()
> print(a.dtypes)
> a.show()
> {noformat}
> root
> |-- _c0: string (nullable = true)
> [('_c0', 'string')]
> +---+
> |_c0|
> +---+
> | 1|
> | 2|
> | 3|
> | 4|
> +---+
> {noformat}
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