SparkSQL on pyspark: how to generate time series?

Question

I'm using SparkSQL on pyspark to store some PostgreSQL tables into DataFrames and then build a query that generates several time series based on a start and stop columns of type date.

Suppose that my_table contains:

 start      | stop       
-------------------------
 2000-01-01 | 2000-01-05 
 2012-03-20 | 2012-03-23 

In PostgreSQL it's very easy to do that:

SELECT generate_series(start, stop, '1 day'::interval)::date AS dt FROM my_table

and it will generate this table:

 dt
------------
 2000-01-01
 2000-01-02
 2000-01-03
 2000-01-04
 2000-01-05
 2012-03-20
 2012-03-21
 2012-03-22
 2012-03-23

but how to do that using plain SparkSQL? Will it be necessary to use UDFs or some DataFrame methods?

Answer 1

EDIT
This creates a dataframe with one row containing an array of consecutive dates:

from pyspark.sql.functions import sequence, to_date, explode, col

spark.sql("SELECT sequence(to_date('2018-01-01'), to_date('2018-03-01'), interval 1 month) as date")

+------------------------------------------+
|                  date                    |
+------------------------------------------+
| ["2018-01-01","2018-02-01","2018-03-01"] |
+------------------------------------------+

You can use the explode function to "pivot" this array into rows:

spark.sql("SELECT sequence(to_date('2018-01-01'), to_date('2018-03-01'), interval 1 month) as date").withColumn("date", explode(col("date"))

+------------+
|    date    |
+------------+
| 2018-01-01 |
| 2018-02-01 |
| 2018-03-01 |
+------------+

(End of edit)

Spark v2.4 support sequence function:

sequence(start, stop, step) - Generates an array of elements from start to stop (inclusive), incrementing by step. The type of the returned elements is the same as the type of argument expressions.

Supported types are: byte, short, integer, long, date, timestamp.

Examples:

SELECT sequence(1, 5);

[1,2,3,4,5]

SELECT sequence(5, 1);

[5,4,3,2,1]

SELECT sequence(to_date('2018-01-01'), to_date('2018-03-01'), interval 1 month);

[2018-01-01,2018-02-01,2018-03-01]

https://docs.databricks.com/spark/latest/spark-sql/language-manual/functions.html#sequence

Answer 2

@Rakesh answer is correct, but I would like to share a less verbose solution:

import datetime
import pyspark.sql.types
from pyspark.sql.functions import UserDefinedFunction

# UDF
def generate_date_series(start, stop):
    return [start + datetime.timedelta(days=x) for x in range(0, (stop-start).days + 1)]    

# Register UDF for later usage
spark.udf.register("generate_date_series", generate_date_series, ArrayType(DateType()) )

# mydf is a DataFrame with columns `start` and `stop` of type DateType()
mydf.createOrReplaceTempView("mydf")

spark.sql("SELECT explode(generate_date_series(start, stop)) FROM mydf").show()