Pyspark : Custom window function

Question

I am currently trying to extract series of consecutive occurrences in a PySpark dataframe and order/rank them as shown below (for convenience I have ordered the initial datafram

Answer 1

This is a pretty common pattern and can be expressed using window functions in a few steps. First import required functions:

from pyspark.sql.functions import sum as sum_, lag, col, coalesce, lit
from pyspark.sql.window import Window

Next define a window:

w = Window.partitionBy("user_id").orderBy("timestamp")

Mark first row for each group:

is_first = coalesce(
  (lag("actions", 1).over(w) != col("actions")).cast("bigint"),
  lit(1)
)

Define order:

order = sum_("is_first").over(w)

And combine all part together with an aggregation:

(df
    .withColumn("is_first", is_first)
    .withColumn("order", order)
    .groupBy("user_id", "actions", "order")
    .count())

If you define df as:

df = sc.parallelize([
    (217498, 100000001, 'A'), (217498, 100000025, 'A'), (217498, 100000124, 'A'),
    (217498, 100000152, 'B'), (217498, 100000165, 'C'), (217498, 100000177, 'C'),
    (217498, 100000182, 'A'), (217498, 100000197, 'B'), (217498, 100000210, 'B'),
    (854123, 100000005, 'A'), (854123, 100000007, 'A')
]).toDF(["user_id", "timestamp", "actions"])

and order the result by user_id and order you'll get:

+-------+-------+-----+-----+ 
|user_id|actions|order|count|
+-------+-------+-----+-----+
| 217498|      A|    1|    3|
| 217498|      B|    2|    1|
| 217498|      C|    3|    2|
| 217498|      A|    4|    1|
| 217498|      B|    5|    2|
| 854123|      A|    1|    2|
+-------+-------+-----+-----+