I am experiencing a problem with caching in map tasks. For the following minimal example

import time

from typing import List

from flytekit import map_task, task, workflow

from flytekit.core.node_creation import create_node

USE_CACHE = True

CACHE_VERSION = "0.0.27"

SERIALIZE_CACHE = False

@task(

cache=USE_CACHE,

cache_version=CACHE_VERSION,

cache_serialize=SERIALIZE_CACHE,

)

def t1() -> str:

print("t1")

time.sleep(1)

return "t1"

@task(

cache=USE_CACHE,

cache_version=CACHE_VERSION,

cache_serialize=SERIALIZE_CACHE,

)

def t2(inp: str) -> str:

print("t2")

time.sleep(1)

return "t2"

@task(

cache=USE_CACHE,

cache_version=CACHE_VERSION,

cache_serialize=SERIALIZE_CACHE,

)

def t3_prep(inp: str) -> List[str]:

print("t3_prep")

return ["a", "b", "c"]

@task(

cache=USE_CACHE,

cache_version=CACHE_VERSION,

cache_serialize=SERIALIZE_CACHE,

)

def t3(inp: str) -> str:

print(f"t3 :: {inp}")

time.sleep(1)

return "t3"

@workflow

def wf1():

t1_out = t1()

t2_out = t2(inp=t1_out)

t3_prep_out = t3_prep(inp=t2_out)

t3_out = map_task(t3)(inp=t3_prep_out)

@workflow

def wf2():

wf1()

t1_out = t1()

t2_out = t2(inp=t1_out)

@workflow

def wf():

n1 = create_node(wf1)

n2 = create_node(wf2)

n1 >> n2

if __name__ == "__main__":

wf()

I get the (unexpected) output when running locally t1 t2 t3_prep t3 :: a t3 :: b t3 :: c t3 :: a t3 :: b t3 :: c Why is "t3" executed twice? I would expect it to be executed only once.

Hey @freezing-shampoo-67249, so currently,

cache_serialize

is not respected with executing maptasks. This is a construct of it being implemented as a plugin rather than a core component of Flyte, so it is essentially excluded from the core logic. Probably more information than you want, but we are currently working on an

ArrayNode

type (RFC here) that can be tracked with this issue. From a high-level this means that maptasks will be a included in the core Flyte execution logic, enabling things like

cache_serialize

, the cache overwrite during execution functionality, but also allowing mapping over all plugin types and even different Flyte node types.

Hey @hallowed-mouse-14616, thanks for your quick response. The

ArrayNode

feature is exactly what I need. Looking forward to having this in one of the next releases of Flyte. 🙂