Hey team, quick one about mapping. if I have some code like so:

with Flow(
    name="horse_racing_data",
) as flow:
    dates = get_dates_task(days_back=days_back, days_ahead=days_ahead, dt_format="%d-%b-%Y")

    raw_sectional_data = apply_map(get_puntingform_sectional_dump_task, date=dates)


    spell_stats_data = apply_map(
        query_db_for_df_task, path_to_sql=unmapped("sql/select_spell_count.sql")
    )
    
    enriched_pf_data = apply_map(
        calculate_runners_spell_stats_task, pf_sectional_df=raw_sectional_data, spell_data=spell_stats_data
    )

I am making multiple separate

apply_map

calls and I just wanted to make sure if I can guarantee that when calling

calculate_runners_spell_stats_task

I can guarantee the order of the returned maps? What i mean is that

raw_sectional_data

and

spell_stats_data

are iterables and as they are provided to the function it is important that they maintain the same order. Am I all good here?

Why are you using

apply_map

over map? Seems like you only have one task per apply map? I think that order is guaranteed though. Order for mapped task is guaranteed and I went over the code and all it does is unpack the

apply_map

and make a flow for it. Just note that the lengths should be equal per these comments

haha I thought apply_map is map, what is the difference ? I think someone pointed me to apply_map on this channel before

Apply map is for a complex sequence of tasks. In the example, the

inc_or_negate

is not a task. It’s a function that uses tasks.

ah, right, so are there performance benefits in changing to map?

Simplifies the code I think. I think performance difference will be minimal, but yes it is redundant.

hmm, kind of

For apply_map or map? Could you show me the code snippet?

give me a second, I might have missed the point of apply_map and it actually solves my issue

Cuz consecutive mapped tasks are compressed and executed together, which is what guarantees the order (on Dask)