Your instincts are solid. Here’s a pragmatic way to structure things in Prefect 3.x, plus a starter repo layout and some code patterns you can copy. High-level guidelines - Task granularity - Good splits: network/IO boundaries, retryable steps, parallelizable loops, cacheable/expensive work, long-running steps, or steps that produce useful logs/artifacts. - Don’t over-split pure-Python glue into many tiny tasks if it adds no retries/parallelism/caching benefit—keep those inline in a task or flow for readability. - Flows vs subflows - Use subflows for “phases” that you’d want to run independently, retry as a unit, schedule separately later, or deploy independently. Keep the top-level orchestrator thin. - Parameters and typing - Flow function args are your parameters—type annotate them. Keep secrets out of code; pass them via parameters or Prefect Variables/blocks. - Reliability features - Add

retries

,

retry_delay_seconds

, and

timeout_seconds

on tasks that call external systems. - Use caching for expensive/idempotent tasks. - Concurrency and mapping - Use a task runner (threads/processes) inside a flow for local parallelism. - Use tags to enforce global concurrency limits across all flows. - Data passing - Pass small data between tasks. For large payloads, store in durable storage (e.g., S3) and pass references. - Logging/observability - Use

get_run_logger()

inside tasks/flows; add artifacts for key diagnostics/tables/links. Suggested repo layout (v1 that evolves cleanly) - Starting simple (matches your plan) - One file per small flow (with its tasks), plus one orchestrator module. - Add tests alongside flows. - Evolution path - If duplication grows, extract shared, “pure” utilities to

lib/

(no Prefect imports), and optionally

components/

for reusable Prefect tasks/subflows. Example layout

your-project/
  prefect.yaml            # project config for deployments
  pyproject.toml          # deps and tool config
  flows/
    ingest_dataset.py     # small flow + its tasks
    transform_users.py    # another small flow + its tasks
  orchestrators/
    daily_pipeline.py     # top-level orchestrator calling subflows
  lib/                    # optional later: pure utilities
    io.py
    transforms.py
  tests/
    test_ingest_dataset.py
  README.md

Concrete patterns - Small flow with clear task boundaries, retries, caching, and logging

from datetime import timedelta
from typing import Any, Iterable
from prefect import flow, task, get_run_logger
from prefect.tasks import task_input_hash
from prefect.task_runners import ThreadPoolTaskRunner
from prefect.futures import as_completed

@task(
    name="fetch_api",
    retries=3,
    retry_delay_seconds=[5, 15, 30],
    timeout_seconds=30,
    cache_key_fn=task_input_hash,
    cache_expiration=timedelta(hours=1),
    tags=["api"]
)
def fetch_api(url: str, token: str) -> dict[str, Any]:
    logger = get_run_logger()
    <http://logger.info|logger.info>(f"Fetching {url}")
    # ... perform request, raise on non-200s ...
    return {"items": []}

@task(name="parse_payload", tags=["parse"])
def parse_payload(resp: dict[str, Any]) -> list[dict[str, Any]]:
    # ... extract/validate/normalize ...
    return resp.get("items", [])

@task(name="write_to_db", retries=2, retry_delay_seconds=10, timeout_seconds=60, tags=["db"])
def write_to_db(records: Iterable[dict[str, Any]]) -> int:
    # ... bulk upsert / transactional write ...
    return len(list(records))

@flow(name="ingest_dataset")
def ingest_dataset(url: str, token: str) -> int:
    raw = fetch_api(url, token)
    parsed = parse_payload(raw)
    written = write_to_db(parsed)
    return written

- Orchestrator flow coordinating subflows and parallelism ``` from prefect import flow from prefect.task_runners import ThreadPoolTaskRunner from prefect.futures import as_completed from flows.ingest_dataset import ingest_dataset @flow(name="daily_pipeline", task_runner=ThreadPoolTaskRunner(max_workers=8))

def daily_pipeline(urls: list[str], token: str): runs = [ingest_dataset.submit(u, token) for u in urls] for fut in as_completed(runs): # surface errors early fut.result() if name == "__main__": # local dev entrypoint daily_pipeline(["https://api.example.com/a", "https://api.example.com/b"], "TOKEN")

- Concurrency limits by tag
  - Tag your tasks (`tags=["api"]`, `tags=["db"]`) as above, then set tag-based concurrency limits in the UI or API to throttle globally across all flows.
  - Docs: <https://docs-3.prefect.io/v3/how-to-guides/workflows/tag-based-concurrency-limits|Tag-based concurrency limits>

- Deployments (Prefect 3.x)
  - CLI: run from your project root with a `prefect.yaml` present.
  - Entrypoint points at the flow definition: `./orchestrators/daily_pipeline.py:daily_pipeline`

prefect deploy ./orchestrators/daily_pipeline.py:daily_pipeline -n daily-pipeline ``` - Programmatic alternative (when you’re ready):

flow.from_source(...).deploy(...)

- Note: In Prefect 3.x, don’t use

Deployment.build_from_flow()

or

prefect deployment build

. When to split tasks vs keep together - Split when: - There’s an external call (HTTP, DB, file system) that benefits from retries/timeouts. - You want to run steps in parallel (e.g., per-file, per-record shard). - The step is a natural checkpoint with its own logging/artifacts/metrics. - The step’s output is a good candidate for caching or persistence. - Keep together when: - It’s quick, pure-Python glue that only serves to move data between two lines. - Splitting would create a lot of boilerplate without adding resilience or parallelism. Useful references - Workflow design patterns - Retries - Caching concepts - Task mapping at scale - Prefect 3 GA announcement A couple of clarifying questions to tailor this further: - Roughly how many items might you process in parallel in the orchestrator (tens, hundreds, thousands)? - Where will you run workers (local, Docker, Kubernetes)? This can influence task-runner choice and concurrency settings.

Short answer: Mermaid is the most common text format folks use to describe Prefect workflows in docs/notes (renders on GitHub and many wikis). Graphviz DOT and PlantUML are also popular. If you need formal business semantics, BPMN exists but is verbose. How to represent flows/subflows/tasks with Mermaid - Use a flowchart with subgraphs for each flow; tasks are nodes; edges show dependencies. Parallelism can be shown with a “fan-out” node or notes. Example for your orchestrator + ingest subflow

flowchart TD
  %% Orchestrator
  subgraph F0[flow: daily_pipeline]
    A0([start]) --> B0{for each URL}
    B0 -->|submit| C0[ingest_dataset(url)]
    C0 -. repeats for all urls .- C0
  end

  %% Subflow
  subgraph F1[flow: ingest_dataset]
    T1[fetch_api]
    T2[parse_payload]
    T3[write_to_db]
    T1 --> T2 --> T3
  end

  %% Visual hint that the orchestrator calls the subflow
  C0 -->|calls| T1

Minimal template you can copy for new flows

flowchart LR
  subgraph F[flow: <name>]
    T1[<task 1>] --> T2[<task 2>] --> T3[<task 3>]
    %% add parallel branches if needed:
    T2 --> B1[branch A]
    T2 --> B2[branch B]
  end

Alternatives (if Mermaid isn’t your thing) - Graphviz DOT (huge ecosystem of renderers)

digraph G {
  subgraph cluster_daily_pipeline {
    label="flow: daily_pipeline";
    A0 -> B0 -> C0;
  }
  subgraph cluster_ingest_dataset {
    label="flow: ingest_dataset";
    T1 -> T2 -> T3;
  }
  C0 -> T1 [label="calls"];
}

- PlantUML Activity (great for forks/joins)

@startuml
title daily_pipeline
start
:For each URL;
fork
:ingest_dataset(url);
fork again
:ingest_dataset(url);
end fork
stop
@enduml

Notes - Prefect UI already renders your flow graph during runs; that’s handy for screenshots/links, but there’s no built-in export-to-Mermaid/DOT in Prefect 3.x today. - If you want a single source of truth, you can keep a small Mermaid file per flow in your repo (e.g., docs/diagrams/flow_name.mmd) and update alongside code. Quick links - Mermaid: mermaid.js.org - Graphviz DOT: Graphviz DOT language - PlantUML Activity: PlantUML activity diagrams - D2 (another clean text-diagram language): d2lang.com If you want, I can turn your current flow layout into a Mermaid snippet you can paste into your README—just share the flow/task names you want to show.