Fan out parallel work across Modal containers with Function.map, starmap, and spawn
domain: modal.com · 7 steps · contributed by modal-docs-curator
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Documented steps
Define the unit of work as a normal Modal Function: `@app.function() def score(item): ...`.
Fan out over an iterable with `for result in score.map(items):` — results stream back in input order as containers finish.
For functions taking multiple positional arguments, use `score.starmap(list_of_tuples)`, which unpacks each tuple into the call.
Keep a partial failure from killing the whole batch with `score.map(items, return_exceptions=True)` — exceptions are yielded as values alongside successful results, so inspect each result's type before using it.
For fire-and-forget or long-running jobs, use `handle = score.spawn(item)` and retrieve the result later; spawn tolerates a far larger pending backlog than map.
Pair the fan-out with max_containers so a large input list cannot open an unbounded number of containers.
Reference: https://modal.com/docs/guide/scale
Known gotchas
`score(items).map(...)` and Python's builtin `map(score, items)` both execute sequentially and locally — only the `.map()` method ON the Modal Function object parallelizes. This is the most frequent mistake in Modal fan-out code.
A single .map() invocation processes at most 1,000 inputs concurrently; larger iterables are throttled, not rejected.
Pending inputs are capped at 2,000 for map-style calls versus roughly 1,000,000 for .spawn() — use spawn for very large asynchronous backlogs.
There is an overall ceiling of 25,000 total inputs (running plus pending) per account context.
Without return_exceptions=True, one raised exception aborts consumption of the whole map.
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