Spark 2.0 Timestamp Difference in Milliseconds using Scala

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I am using Spark 2.0 and looking for a way to achieve the following in Scala:

Need the time-stamp difference in milliseconds between two Data-frame column values.

Value_1 = 06/13/2017 16:44:20.044
Value_2 = 06/13/2017 16:44:21.067

Data-types for both is timestamp.

Note:Applying the function unix_timestamp(Column s) on both values and subtracting works but not upto the milliseconds value which is the requirement.

Final query would look like this:

Select **timestamp_diff**(Value_2,Value_1) from table1

this should return the following output:

1023 milliseconds

where timestamp_diff is the function that would calculate the difference in milliseconds.

apache-spark-2.0 apache-spark-sql user-defined-functions timestamp scala
3 Answers

One way would be to use Unix epoch time, the number of milliseconds since 1 January 1970. Below is an example using an UDF, it takes two timestamps and returns the difference between them in milliseconds.

val timestamp_diff = udf((startTime: Timestamp, endTime: Timestamp) => {
  (startTime.getTime() - endTime.getTime())
})

val df = // dataframe with two timestamp columns (col1 and col2)
  .withColumn("diff", timestamp_diff(col("col2"), col("col1")))

Alternatively, you can register the function to use with SQL commands:

val timestamp_diff = (startTime: Timestamp, endTime: Timestamp) => {
  (startTime.getTime() - endTime.getTime())
}

spark.sqlContext.udf.register("timestamp_diff", timestamp_diff)
df.createOrReplaceTempView("table1")

val df2 = spark.sqlContext.sql("SELECT *, timestamp_diff(col2, col1) as diff from table1")
user_75795470

The same for PySpark:

import datetime

def timestamp_diff(time1: datetime.datetime, time2: datetime.datetime):
    return int((time1-time2).total_seconds()*1000)

int and *1000 are only to output milliseconds

Example usage:

spark.udf.register("timestamp_diff", timestamp_diff)    

df.registerTempTable("table1")

df2 = spark.sql("SELECT *, timestamp_diff(col2, col1) as diff from table1")

It's not an optimal solution since UDFs are usually slow, so you might run into performance issues.

user_42192020

Bit late to the party, but hope it's still useful.

import org.apache.spark.sql.Column
def getUnixTimestamp(col: Column): Column = (col.cast("double") * 1000).cast("long")

df.withColumn("diff", getUnixTimestamp(col("col2")) - getUnixTimestamp(col("col1")))

Of course you can define a separate method for the difference:

def timestampDiff(col1: Column, col2: Column): Column = getUnixTimestamp(col2) - getUnixTimestamp(col1)

df.withColumn("diff", timestampDiff(col("col1"), col("col2")))

To make life easier one can define an overloaded method for Strings with a default diff name:

def timestampDiff(col1: String, col2: String): Column = timestampDiff(col(col1), col(col2)).as("diff")

Now in action:

scala> df.show(false)
+-----------------------+-----------------------+
|min_time               |max_time               |
+-----------------------+-----------------------+
|1970-01-01 01:00:02.345|1970-01-01 01:00:04.786|
|1970-01-01 01:00:23.857|1970-01-01 01:00:23.999|
|1970-01-01 01:00:02.325|1970-01-01 01:01:07.688|
|1970-01-01 01:00:34.235|1970-01-01 01:00:34.444|
|1970-01-01 01:00:34.235|1970-01-01 01:00:34.454|
+-----------------------+-----------------------+


scala> df.withColumn("diff", timestampDiff("min_time", "max_time")).show(false)
+-----------------------+-----------------------+-----+
|min_time               |max_time               |diff |
+-----------------------+-----------------------+-----+
|1970-01-01 01:00:02.345|1970-01-01 01:00:04.786|2441 |
|1970-01-01 01:00:23.857|1970-01-01 01:00:23.999|142  |
|1970-01-01 01:00:02.325|1970-01-01 01:01:07.688|65363|
|1970-01-01 01:00:34.235|1970-01-01 01:00:34.444|209  |
|1970-01-01 01:00:34.235|1970-01-01 01:00:34.454|219  |
+-----------------------+-----------------------+-----+


scala> df.select(timestampDiff("min_time", "max_time")).show(false)
+-----+
|diff |
+-----+
|2441 |
|142  |
|65363|
|209  |
|219  |
+-----+
user_64575990
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