Interpret timestamp fields in Spark while reading json - json

Question

I am trying to read a pretty printed json which has time fields in it. I want to interpret the timestamps columns as timestamp fields while reading the json itself. However, it's still reading them as string when I printSchema
E.g.
Input json file -
[{
"time_field" : "2017-09-30 04:53:39.412496Z"
}]
Code -
df = spark.read.option("multiLine", "true").option("timestampFormat","yyyy-MM-dd HH:mm:ss.SSSSSS'Z'").json('path_to_json_file')
Output of df.printSchema() -
root
|-- time_field: string (nullable = true)
What am I missing here?

Answer 1

My own experience with option timestampFormat is that it doesn't quite work as advertised. I would simply read the time fields as strings and use to_timestamp to do the conversion, as shown below (with slightly generalized sample input):
# /path/to/jsonfile
[{
"id": 101, "time_field": "2017-09-30 04:53:39.412496Z"
},
{
"id": 102, "time_field": "2017-10-01 01:23:45.123456Z"
}]
In Python:
from pyspark.sql.functions import to_timestamp
df = spark.read.option("multiLine", "true").json("/path/to/jsonfile")
df = df.withColumn("timestamp", to_timestamp("time_field"))
df.show(2, False)
+---+---------------------------+-------------------+
|id |time_field |timestamp |
+---+---------------------------+-------------------+
|101|2017-09-30 04:53:39.412496Z|2017-09-30 04:53:39|
|102|2017-10-01 01:23:45.123456Z|2017-10-01 01:23:45|
+---+---------------------------+-------------------+
df.printSchema()
root
|-- id: long (nullable = true)
|-- time_field: string (nullable = true)
|-- timestamp: timestamp (nullable = true)
In Scala:
val df = spark.read.option("multiLine", "true").json("/path/to/jsonfile")
df.withColumn("timestamp", to_timestamp($"time_field"))

Answer 2

It's bug in Spark version 2.4.0 Issues SPARK-26325
For Spark Version 2.4.4
import org.apache.spark.sql.types.TimestampType
//String to timestamps
val df = Seq(("2019-07-01 12:01:19.000"),
("2019-06-24 12:01:19.000"),
("2019-11-16 16:44:55.406"),
("2019-11-16 16:50:59.406")).toDF("input_timestamp")
val df_mod = df.select($"input_timestamp".cast(TimestampType))
df_mod.printSchema
Output
root
|-- input_timestamp: timestamp (nullable = true)