Standard Functions for Date and Time

Table 1. (Subset of) Standard Functions for Date and Time
Name	Description
current_timestamp
date_format
to_date
to_timestamp
unix_timestamp	Converts current or specified time to Unix timestamp (in seconds)
window	Generates time windows (i.e. tumbling, sliding and delayed windows)

`date_format` Function

date_format(dateExpr: Column, format: String): Column

Internally, date_format creates a Column with DateFormatClass binary expression. DateFormatClass takes the expression from dateExpr column and format.

scala> val df = date_format($"date", "dd/MM/yyyy")
df: org.apache.spark.sql.Column = date_format(date, dd/MM/yyyy)

import org.apache.spark.sql.catalyst.expressions.DateFormatClass
val dfc = df.expr.asInstanceOf[DateFormatClass]
scala> println(dfc.prettyName)
date_format

scala> println(df.expr.numberedTreeString)
00 date_format('date, dd/MM/yyyy, None)
01 :- 'date
02 +- dd/MM/yyyy

`current_date` Function

to_date(e: Column, fmt: String): Column

Caution

FIXME

`current_timestamp` Function

current_timestamp(): Column

Caution

FIXME

Note	`current_timestamp` is also `now` function in SQL.

`to_date` Function

to_date(e: Column, fmt: String): Column

Caution

FIXME

`to_timestamp` Function

to_timestamp(s: Column): Column
to_timestamp(s: Column, fmt: String): Column

Caution

FIXME

Converting Current or Specified Time to Unix Timestamp — `unix_timestamp` Function

unix_timestamp(): Column  (1)
unix_timestamp(time: Column): Column (2)
unix_timestamp(time: Column, format: String): Column

Gives current timestamp (in seconds)
Converts time string in format yyyy-MM-dd HH:mm:ss to Unix timestamp (in seconds)

unix_timestamp converts the current or specified time in the specified format to a Unix timestamp (in seconds).

unix_timestamp supports a column of type Date, Timestamp or String.

// no time and format => current time
scala> spark.range(1).select(unix_timestamp as "current_timestamp").show
+-----------------+
|current_timestamp|
+-----------------+
|       1493362850|
+-----------------+

// no format so yyyy-MM-dd HH:mm:ss assumed
scala> Seq("2017-01-01 00:00:00").toDF("time").withColumn("unix_timestamp", unix_timestamp($"time")).show
+-------------------+--------------+
|               time|unix_timestamp|
+-------------------+--------------+
|2017-01-01 00:00:00|    1483225200|
+-------------------+--------------+

scala> Seq("2017/01/01 00:00:00").toDF("time").withColumn("unix_timestamp", unix_timestamp($"time", "yyyy/MM/dd")).show
+-------------------+--------------+
|               time|unix_timestamp|
+-------------------+--------------+
|2017/01/01 00:00:00|    1483225200|
+-------------------+--------------+

unix_timestamp returns null if conversion fails.

// note slashes as date separators
scala> Seq("2017/01/01 00:00:00").toDF("time").withColumn("unix_timestamp", unix_timestamp($"time")).show
+-------------------+--------------+
|               time|unix_timestamp|
+-------------------+--------------+
|2017/01/01 00:00:00|          null|
+-------------------+--------------+

Note	`unix_timestamp` is also supported in SQL mode. `scala> spark.sql("SELECT unix_timestamp() as unix_timestamp").show +--------------+ \|unix_timestamp\| +--------------+ \| 1493369225\| +--------------+`

Internally, unix_timestamp creates a Column with UnixTimestamp binary expression (possibly with CurrentTimestamp).

Generating Time Windows — `window` Function

window(
  timeColumn: Column,
  windowDuration: String): Column  (1)
window(
  timeColumn: Column,
  windowDuration: String,
  slideDuration: String): Column   (2)
window(
  timeColumn: Column,
  windowDuration: String,
  slideDuration: String,
  startTime: String): Column       (3)

Creates a tumbling time window with slideDuration as windowDuration and 0 second for startTime
Creates a sliding time window with 0 second for startTime
Creates a delayed time window

window generates tumbling, sliding or delayed time windows of windowDuration duration given a timeColumn timestamp specifying column.

Note	From Tumbling Window (Azure Stream Analytics): Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals.

Note	From Introducing Stream Windows in Apache Flink: Tumbling windows group elements of a stream into finite sets where each set corresponds to an interval. Tumbling windows discretize a stream into non-overlapping windows.

scala> val timeColumn = window('time, "5 seconds")
timeColumn: org.apache.spark.sql.Column = timewindow(time, 5000000, 5000000, 0) AS `window`

timeColumn should be of TimestampType, i.e. with java.sql.Timestamp values.

Tip	Use java.sql.Timestamp.from or java.sql.Timestamp.valueOf factory methods to create `Timestamp` instances.

// https://docs.oracle.com/javase/8/docs/api/java/time/LocalDateTime.html
import java.time.LocalDateTime
// https://docs.oracle.com/javase/8/docs/api/java/sql/Timestamp.html
import java.sql.Timestamp
val levels = Seq(
  // (year, month, dayOfMonth, hour, minute, second)
  ((2012, 12, 12, 12, 12, 12), 5),
  ((2012, 12, 12, 12, 12, 14), 9),
  ((2012, 12, 12, 13, 13, 14), 4),
  ((2016, 8,  13, 0, 0, 0), 10),
  ((2017, 5,  27, 0, 0, 0), 15)).
  map { case ((yy, mm, dd, h, m, s), a) => (LocalDateTime.of(yy, mm, dd, h, m, s), a) }.
  map { case (ts, a) => (Timestamp.valueOf(ts), a) }.
  toDF("time", "level")
scala> levels.show
+-------------------+-----+
|               time|level|
+-------------------+-----+
|2012-12-12 12:12:12|    5|
|2012-12-12 12:12:14|    9|
|2012-12-12 13:13:14|    4|
|2016-08-13 00:00:00|   10|
|2017-05-27 00:00:00|   15|
+-------------------+-----+

val q = levels.select(window($"time", "5 seconds"), $"level")
scala> q.show(truncate = false)
+---------------------------------------------+-----+
|window                                       |level|
+---------------------------------------------+-----+
|[2012-12-12 12:12:10.0,2012-12-12 12:12:15.0]|5    |
|[2012-12-12 12:12:10.0,2012-12-12 12:12:15.0]|9    |
|[2012-12-12 13:13:10.0,2012-12-12 13:13:15.0]|4    |
|[2016-08-13 00:00:00.0,2016-08-13 00:00:05.0]|10   |
|[2017-05-27 00:00:00.0,2017-05-27 00:00:05.0]|15   |
+---------------------------------------------+-----+

scala> q.printSchema
root
 |-- window: struct (nullable = true)
 |    |-- start: timestamp (nullable = true)
 |    |-- end: timestamp (nullable = true)
 |-- level: integer (nullable = false)

// calculating the sum of levels every 5 seconds
val sums = levels.
  groupBy(window($"time", "5 seconds")).
  agg(sum("level") as "level_sum").
  select("window.start", "window.end", "level_sum")
scala> sums.show
+-------------------+-------------------+---------+
|              start|                end|level_sum|
+-------------------+-------------------+---------+
|2012-12-12 13:13:10|2012-12-12 13:13:15|        4|
|2012-12-12 12:12:10|2012-12-12 12:12:15|       14|
|2016-08-13 00:00:00|2016-08-13 00:00:05|       10|
|2017-05-27 00:00:00|2017-05-27 00:00:05|       15|
+-------------------+-------------------+---------+

windowDuration and slideDuration are strings specifying the width of the window for duration and sliding identifiers, respectively.

Tip	Use `CalendarInterval` for valid window identifiers.

Note	`window` is available as of Spark 2.0.0.

Internally, window creates a Column (with TimeWindow expression) available as window alias.

// q is the query defined earlier
scala> q.show(truncate = false)
+---------------------------------------------+-----+
|window                                       |level|
+---------------------------------------------+-----+
|[2012-12-12 12:12:10.0,2012-12-12 12:12:15.0]|5    |
|[2012-12-12 12:12:10.0,2012-12-12 12:12:15.0]|9    |
|[2012-12-12 13:13:10.0,2012-12-12 13:13:15.0]|4    |
|[2016-08-13 00:00:00.0,2016-08-13 00:00:05.0]|10   |
|[2017-05-27 00:00:00.0,2017-05-27 00:00:05.0]|15   |
+---------------------------------------------+-----+

scala> println(timeColumn.expr.numberedTreeString)
00 timewindow('time, 5000000, 5000000, 0) AS window#22
01 +- timewindow('time, 5000000, 5000000, 0)
02    +- 'time

Example — Traffic Sensor

Note	The example is borrowed from Introducing Stream Windows in Apache Flink.

The example shows how to use window function to model a traffic sensor that counts every 15 seconds the number of vehicles passing a certain location.

Standard Functions for Date and Time