Yes — your diagnosis is basically right. What’s happening - In an async flow, if you run heavy sync CPU-bound work directly, it can block the event loop. - When the event loop is blocked long enough, heartbeat-related behavior can become delayed enough that your automation interprets the run as dead/zombie and marks it CRASHED. - The run may still finish later, which is why you see the false

CRASHED -> COMPLETED

situation. So your two mitigations make sense: 1. increasing the automation window 2. offloading blocking sync work so it does not monopolize the async flow’s event loop Is

asyncio.to_thread()

the right fix? - Yes, as a tactical fix for calling sync code from an async flow. - But for CPU-bound computation specifically, threads are not always the best long-term choice because of the Python GIL. - For truly heavy compute, a process-based approach is often better. Recommended approach 1. Keep the automation threshold tolerant enough - This is still useful because transient scheduling delays can happen. - But it should be a buffer, not the primary fix. 2. Do not run heavy sync CPU work inline in an async flow/task - If you do, you risk blocking the event loop. 3. Prefer one of these patterns: Option A: Offload ad hoc sync work from async code Use

anyio.to_thread.run_sync(...)

or

asyncio.to_thread(...)

when you must call sync code from async code.

from prefect import flow, task
import anyio

@task
def heavy_sync_compute(x: int) -> int:
    total = 0
    for i in range(50_000_000):
        total += i
    return total + x

@flow
async def my_flow():
    result = await anyio.to_thread.run_sync(heavy_sync_compute.fn, 10)
    return result

A small note: if you want Prefect task semantics, retries, state tracking, etc., don’t bypass them too much by only calling

.fn

everywhere. This pattern is best for isolated cases, not as your main task execution strategy. Option B: Use a task runner better suited for CPU work For CPU-heavy tasks,

ProcessPoolTaskRunner

is usually the better design.

from prefect import flow, task
from prefect.task_runners import ProcessPoolTaskRunner

@task
def heavy_sync_compute(x: int) -> int:
    total = 0
    for i in range(50_000_000):
        total += i
    return total + x

@flow(task_runner=ProcessPoolTaskRunner())
def my_flow():
    future = heavy_sync_compute.submit(10)
    return future.result()

Why this is better: - runs compute in separate processes - avoids blocking the main flow execution context - better for real CPU-bound workloads Option C: Separate async I/O tasks and sync compute tasks cleanly If your workflow is mixed: - keep HTTP / DB tasks async - keep compute tasks sync - submit compute tasks through an appropriate task runner instead of invoking them directly inside async code That tends to be the cleanest architecture. What I’d recommend in your case If these are genuinely computation-heavy tasks, I would not want to manually wrap each one with

asyncio.to_thread()

forever. I’d instead prefer: - an appropriate task runner for those tasks, especially

ProcessPoolTaskRunner

for CPU-bound work - or restructuring so the heavy compute happens in separately submitted Prefect tasks, not inline inside the async flow body So: you are not missing something, but

to_thread()

is more of a workaround than the ideal general solution for CPU-heavy work. Practical rule of thumb - I/O-bound sync code ->

ThreadPoolTaskRunner

or

to_thread

- CPU-bound sync code ->

ProcessPoolTaskRunner

- async libraries available -> use native async One more thing to check If you’re on an older Prefect 3 release, upgrading is worth considering, since heartbeat behavior has seen improvements. You can check your current settings with:

prefect config view

CLI verified. Relevant docs / references - Prefect task runners: Task runners

- Prefect flows/tasks concepts: Flows - Prefect settings/config: Settings and profiles If you want, I can help you refactor one of your current async flows into a pattern that keeps async I/O and CPU-bound Prefect tasks working together cleanly.

Yes — with 10k deeply nested Python objects, this can absolutely be mostly Prefect overhead, even if the task body itself only takes ~1s. What you’re seeing is usually not “pass by memory is free” in practice. Even in-memory handoff can still be expensive because Prefect may need to: - inspect task parameters - traverse nested collections - resolve futures/states inside inputs - collect dependency information - possibly serialize/store results - reconstruct objects in some cases So the delay can happen between tasks, even when the actual work is tiny. Why

quote()

helped That strongly suggests traversal/introspection of the nested object is a major part of the cost. If you used:

from prefect.utilities.annotations import quote

then yes, that tells Prefect to stop recursively inspecting the object, which is why things improved. But if you still have 10 minutes of delay, then the object is likely so large/deep that: - introspection is only part of the issue - result handling / serialization / repeated task handoff is also expensive - your flow has too many task boundaries relative to how cheap each task is Short answer I would not keep passing a huge deeply nested object through many small tasks if each task only does ~1s of work. Your best options are usually: 1. Reduce the number of task boundaries 2. Pass references, not giant payloads 3. Persist/spill large intermediate data and reload where needed 4. Use

quote()

only as a targeted optimization, not the whole architecture What I recommend, in order Option 1: Combine tiny tasks into a coarser-grained task If task execution is 1s but orchestration overhead is minutes, the tasks are too fine-grained for the payload size. So yes, combining several steps into one task is often the right move. Instead of:

task_a(huge_data) -> task_b(huge_data) -> task_c(huge_data)

prefer:

@task
def process_all(huge_data):
    x = step_a(huge_data)
    y = step_b(x)
    z = step_c(y)
    return z

This usually gives the biggest win. Tradeoff: - less task-level observability - fewer retries at intermediate step granularity But if overhead dominates, this is usually worth it. Option 2: Spill large data and pass a reference If the object is huge and repeatedly passed around, it is often better to: - write it to disk / object storage / DB - pass only a path, key, or ID between tasks For example: - write to a file - write to S3 / GCS / blob storage - write to a database table or temp table - pass

file_path

,

s3_uri

, or

dataset_id

This avoids repeated deep traversal and object handoff overhead. Conceptually:

@task
def build_data() -> str:
    path = "/tmp/data.pkl"
    # write data there
    return path

@task
def process_data(path: str):
    # load from path
    ...

This is especially useful if: - many downstream tasks use the same dataset - the object is large and nested - the object is expensive for Prefect to inspect repeatedly Option 3: Keep using

quote()

when appropriate

quote()

is good when: - the object is already fully resolved - it does not contain Prefect futures you need Prefect to inspect - you want to suppress traversal cost But I would treat it as an optimization, not the primary data-passing strategy. Option 4: Avoid too many micro-tasks A common Prefect anti-pattern is orchestrating super small units of work as separate tasks while moving very large Python objects between them. If the task body is ~1 second, then you generally want: - larger tasks - less data movement - fewer orchestration boundaries Prefect is an orchestrator, not a super-efficient in-memory pipeline engine for giant nested Python structures. Should you spill to disk instead of memory? For your case: very possibly yes. Not because memory itself is bad, but because repeated orchestration over a giant in-memory nested object is expensive.

Passing a small reference is often much better than passing the full object. Good heuristic If data is: - small -> pass directly - large and reused across many tasks -> persist it and pass a reference - large but only used within one logical processing unit -> keep it inside one larger task Would

gather

help? Not really for this particular bottleneck.

gather

helps when you have many concurrent async operations and want to await them efficiently. But if the bottleneck is: - deep parameter traversal - repeated task orchestration - serializing/handling a huge nested object then

gather

won’t solve the core issue. Practical recommendation for your case Based on what you described, I would do this: Best likely design - async tasks for HTTP / DB calls - one or a few coarse-grained compute tasks - avoid passing the 10k nested object through lots of task boundaries - if multiple tasks need that large data, persist it once and pass a reference So among your two ideas: - “gather all my tasks so there will be only one final task” -> Yes, likely beneficial, at least partially. Don’t necessarily collapse everything, but definitely reduce the number of tiny tasks touching the huge object. - “spill on disk not to pass data through memory” -> Also yes, if the large object must be shared across several task boundaries. In many cases the right answer is actually both: - fewer tasks - references to persisted data instead of giant Python payloads Rule of thumb If Prefect overhead is much larger than task runtime: - your tasks are too granular - or your payloads are too large for task-to-task handoff - or both Concrete guidance I’d try this order: 1. Profile one representative chain Measure time: - before task submission - task start - task body start - task body end - downstream task start 2. Collapse adjacent 1-second tasks - especially tasks that only transform the same huge object 3. Persist the big object once - pass a path/ID/key downstream 4. Keep using

quote()

only where safe - after futures are resolved - where you know Prefect doesn’t need to inspect internals 5. Consider simpler payloads - convert custom nested objects into slimmer representations if possible - avoid carrying unnecessary fields through the pipeline If you want, I can help you design a specific Prefect pattern for this, for example: - Pattern A: async fetch -> persist dataset -> coarse compute task(s) - Pattern B: async fetch -> batch/chunk -> mapped compute tasks - Pattern C: one orchestration task, many internal Python functions, minimal Prefect task boundaries If you share a simplified shape of your flow and the object type, I can suggest which of those three is best.

Short answer: you can use Prefect result persistence to spill task outputs to disk/object storage, but it will not fully solve “don’t pass the object through memory” on its own. Important distinction Prefect result persistence is great for: - durability - retries - caching - not keeping every result only in RAM - writing large outputs to disk / object storage But when a downstream task takes the upstream result as a parameter, Prefect will still generally materialize that full object in memory for the downstream task. So: - Can Prefect persist task results to disk? -> Yes - Does that automatically make downstream tasks pass only references? -> No That’s the key point. What this means for your use case If you do this:

@task(persist_result=True)
def make_big_data():
    return huge_nested_object

@task
def consume(data):
    ...

and then:

x = make_big_data.submit()
consume.submit(x)

Prefect may store `make_big_data`’s result on disk/object storage, but when

consume

runs, Prefect will still resolve

x

into the actual Python object and hand that object to

consume

. So persistence helps with storage/durability, but not with avoiding full object materialization in downstream tasks. When Prefect persistence is still useful It is useful if you want to: - avoid keeping all previous task outputs in memory - survive retries / restarts - store large intermediates somewhere durable - manually reload them later For example, a better pattern is:

@task(persist_result=True)
def make_big_data() -> str:
    # write data yourself to a file/object store
    # return only a path/key/reference
    return "/tmp/big_data.pkl"

@task
def consume(path: str):
    # load it here when needed
    ...

In this pattern, Prefect is only passing a small string reference, not the huge nested object. So do you need to spill it yourself? If your goal is specifically to avoid passing the huge object between tasks: yes, usually you should spill it yourself and pass a reference. That is typically the better design. Recommended pattern For large intermediate data, I’d recommend: 1. Your code writes the large data - local disk - S3 / GCS / Azure Blob - database / table / parquet dataset - temp file / object store 2. Return a lightweight reference - file path - URI - dataset ID - object key 3. Downstream tasks accept the reference - load only when needed Example shape:

from prefect import task, flow

@task
def prepare_data() -> str:
    path = "/tmp/data.json"
    # write huge data to path
    return path

@task
def transform_data(path: str) -> str:
    # read from path, transform, write new file
    new_path = "/tmp/transformed.json"
    return new_path

@task
def load_data(path: str):
    # final use
    pass

@flow
def pipeline():
    p1 = prepare_data.submit()
    p2 = transform_data.submit(p1)
    load_data.submit(p2)

Now the payload crossing task boundaries is tiny. Best practice: local disk vs remote storage - Local disk is fine if everything runs on the same machine and retries won’t move elsewhere. - Remote storage is safer if you use containers, workers, Kubernetes, autoscaling, or retries that may run on a different machine. If you’re on distributed/ephemeral infrastructure, prefer remote storage. What about Prefect’s

persist_result

then? Use it for: - task/flow results you want durable - caching / reproducibility - keeping Prefect from relying only on RAM But don’t expect it to behave like a zero-copy shared reference system for huge Python objects. My recommendation for you Given: - 10k deeply nested objects - many small tasks - huge orchestration overhead I would do both: 1. reduce task granularity 2. spill large intermediates yourself and pass references And optionally also: 3. use Prefect result persistence for durability/caching of those small references or final outputs

So the practical answer is: If the goal is “avoid passing giant Python objects between tasks”, spill it yourself and pass a reference. If you want, I can give you a concrete Prefect pattern for: - same-machine local disk spilling, or - S3/object-storage-based spilling depending on where your workers run.