# -*- coding: utf-8 -*-
"""
author SparkByExamples.com
"""

from pyspark.sql import SparkSession

# Create SparkSession
spark = SparkSession.builder \
          .appName('SparkByExamples.com') \
          .getOrCreate()

inputData = [("2019-07-01 12:01:19",
            "07-01-2019 12:01:19", 
            "07-01-2019")]
columns=["timestamp_1","timestamp_2","timestamp_3"]
df=spark.createDataFrame(
        data = inputData,
        schema = columns)
df.printSchema()
df.show(truncate=False)

from pyspark.sql.functions import *
df2 = df.select( 
      unix_timestamp(col("timestamp_1")).alias("timestamp_1"), 
      unix_timestamp(col("timestamp_2"),"MM-dd-yyyy HH:mm:ss").alias("timestamp_2"), 
      unix_timestamp(col("timestamp_3"),"MM-dd-yyyy").alias("timestamp_3"), 
      unix_timestamp().alias("timestamp_4") 
   )
df2.printSchema()
df2.show(truncate=False)

df3=df2.select(
    from_unixtime(col("timestamp_1")).alias("timestamp_1"),
    from_unixtime(col("timestamp_2"),"MM-dd-yyyy HH:mm:ss").alias("timestamp_2"),
    from_unixtime(col("timestamp_3"),"MM-dd-yyyy").alias("timestamp_3"),
    from_unixtime(col("timestamp_4")).alias("timestamp_4")
  )
df3.printSchema()
df3.show(truncate=False)

#SQL