Define edge rules in pyspark graphframes

Question

I am using graphframes to represent a graph in pyspark from a similar dataframe:

data = [
    ("1990", "1995"),
    ("1980", "1996"),
    ("1993", "1994"),
    ("1990", "2002"),
    ("1996", "2002"),
    ("1999", "2008"),
    ("2003", "2014"),
]

Nodes are represented as a union of date strings, ie:

nodes = [
    ("1990_1995"),
    ("1980_1996"),
    ("1993_1994"),
    ("1990_2002"),
    ("1996_2002"),
    ("1999_2008"),
    ("2003_2014"),
]

My problem is that I know how to define the edges. In theory I know how to do it, but in the implementation I have some difficulties.

The edge definition is based on this rule: if a date range (e.g. "1990-12-10" - "1995-12-10") overlaps with another date range (e.g. "1980-12-10" - "1996-12-10"), then the two date ranges represent an edge. How can I implement the creation of edge according to this rule in pyspark graphframes?

The edges should be:

node_and_edges = [
    ("1990_1995", ["1980_1996", "1993_1994", "1990_2002"]),
    ("1980_1996", ["1990_1995", "1993_1994", "1990_2002"]),
    ("1993_1994", ["1990_1995", "1980_1996", "1990_2002"]),
    ("1990_2002", ["1990_1995", "1980_1996", "1993_1994", "1996_2002", "1999_2008"]),
    ("1996_2002", ["1993_1994", "1990_2002", "1999_2008"]),
    ("1999_2008", ["1990_2002", "1996_2002", "2003_2014"]),
    ("2003_2014", ["1999_2008"]),
]

Code below:

from pyspark.sql import functions as F
from pyspark.sql import SparkSession as ss
spark = ss.builder.appName("test").getOrCreate()

data = [
    ("1990", "1995"),
    ("1980", "1996"),
    ("1993", "1994"),
    ("1990", "2002"),
    ("1996", "2002"),
    ("1999", "2008"),
    ("2003", "2014"),
]

rdd = spark.sparkContext.parallelize(data)

columns = ["start_date", "end_date"]

df = rdd.toDF(columns)

# Creating nodes
df = df.withColumn("node", F.concat_ws("_", F.col("start_date"), F.col("end_date")))

# Creating edges
# how?

df.printSchema()
df.show()

Execution output:

root
 |-- start_date: string (nullable = true)
 |-- end_date: string (nullable = true)
 |-- node: string (nullable = false)

+----------+--------+---------+
|start_date|end_date|     node|
+----------+--------+---------+
|      1990|    1995|1990_1995|
|      1980|    1996|1980_1996|
|      1993|    1994|1993_1994|
|      1990|    2002|1990_2002|
|      1996|    2002|1996_2002|
|      1999|    2008|1999_2008|
|      2003|    2014|2003_2014|
+----------+--------+---------+

Overlap edges rule:

walking · Accepted Answer · 2022-07-23 15:03:42Z

How about a range join?

It involves a cartesian multipication under the hood which is not fun and might not work for too large of a dataset but you can optimize it, at least in Databricks runtime (not sure if available outside it) with the range_join hint.

from pyspark.sql.functions import col

data = [
    ("1990", "1995"),
    ("1980", "1996"),
    ("1993", "1994"),
    ("1990", "2002"),
    ("1996", "2002"),
    ("1999", "2008"),
    ("2003", "2014"),
]

df = spark.createDataFrame(data, schema="start string, end string") \
.withColumn("start", col("start").cast("integer")) \
.withColumn("end", col("end").cast("integer"))

df2 = df.toDF('start2', 'end2')

df \
.join(df2, on=col('start2').between(col('start'), col('end'))) \
.filter((col("start") != col("start2")) | (col("end") != col("end2"))) \
.show()


+-----+----+------+----+
|start|end |start2|end2|
+-----+----+------+----+
|1990 |1995|1993  |1994|
|1980 |1996|1990  |1995|
|1980 |1996|1993  |1994|
|1990 |1995|1990  |2002|
|1980 |1996|1990  |2002|
|1980 |1996|1996  |2002|
|1990 |2002|1990  |1995|
|1990 |2002|1993  |1994|
|1990 |2002|1996  |2002|
|1990 |2002|1999  |2008|
|1996 |2002|1999  |2008|
|1999 |2008|2003  |2014|
+-----+----+------+----+

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