Thank you for your excellent question about Spark stages! I really appreciate your interest in understanding the relationship between actions and stage creation.

In Spark, the number of stages created depends on the shuffle operations (also called "wide transformations") in your lineage, not directly on the action itself. Let me explain with some examples:

1. Single Stage Example:

result = rdd.map(lambda x: x * 2) \

.filter(lambda x: x > 10) \

.count()

This creates just 1 stage because map() and filter() are narrow transformations (no shuffle needed). The count() action triggers the execution but doesn't create an additional stage.

2. Multiple Stages Example:

result = rdd.map(lambda x: (x, 1)) \

.reduceByKey(lambda x, y: x + y) \

.count()

This creates 2 stages because reduceByKey() requires a shuffle operation, which creates a stage boundary.

3. More Complex Example:

result = rdd.map(lambda x: (x % 10, x)) \

.groupByKey() \

.map(lambda x: sum(x[1])) \

.repartition(10) \

.count()

This creates 3 stages because there are two shuffle operations: one for groupByKey() and another for repartition().

Key points to remember:

- Actions trigger execution but don't determine stage count

- Shuffle operations create stage boundaries

- Narrow transformations (map, filter) are grouped into the same stage

- Wide transformations (reduceByKey, groupByKey, repartition) create new stages

I hope this helps clarify the relationship between actions and stages! Feel free to ask if you have any follow-up questions. I'm always excited to discuss Spark's internal workings and help fellow developers understand these concepts better.

Best regards,

Pratik

P.S. If you're interested in more detailed examples or specific use cases, I'm planning future articles that will dive deeper into Spark's execution model. Consider following me on Medium for updates!