Manoranjan Kumar created SPARK-25517:
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Summary: Spark DataFrame option inferSchema="true",
dataFormat=MM/dd/yyyy, fails to detect date type from the csv file while reading
Key: SPARK-25517
URL: https://issues.apache.org/jira/browse/SPARK-25517
Project: Spark
Issue Type: Improvement
Components: SQL
Affects Versions: 2.3.1, 2.3.0
Environment: Spark 2.3.0
Reporter: Manoranjan Kumar
spark.read.format("csv").option("inferSchema", true).option("dateFormat",
"MM/dd/yyyy") fails to detect or infer the date type while reading the csv file
having date column in the specified format(MM/dd/yyyy)
For example:-
An employee csv file (employee.csv) has following two sample dummy records
(with header):
emp_id,emp_name,joining_date,emp_age, emp_in_time,emp_salary
100,Bradd Pitt,09/25/2018,26,09/25/2018 10:12:36,10000.00
101,Angel Joli,08/20/2018,28,08/20/2018 11:32:58,12000.00
when I read the above csv file as dataframe like below:
val empDF = spark.read.format("csv").option("inferSchema",
true).option("dataFormat","MM/dd/yyyy").option("timestampFormat","MM/dd/yyyy
HH:mm:ss").load(employee.csv)
empDF.printSchema()
results/output:
root
|-- emp_id: integer (nullable = true)
|-- emp_name: string (nullable = true)
|-- {color:#d04437}joining_date: string{color} (nullable = true)
|-- emp_age: integer (nullable = true)
|-- {color:#d04437}emp_in_time: timestamp{color} (nullable = true)
|-- emp_salary: double (nullable = true)
Please notice above (marked in red color) the data type automatically inferred
by spark for joining_date and emp_in_time, for joining_date, it fails to detect
as date type and the type remains as string as it is, whereas it detects well
for emp_in_time as timestamp
This was the issue that I struggled with for a complete day, and when I dived
deep into the spark source code, i found the implementation for date type is
missing whereas the implementation for timestamp is present in all its glory.
I am new to this place (exactly first timer), please get back in case of
further information or live example with running code.
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