---
id: aiodataloader
version: "0.4.3"
license: unclear
license_treatment: permissive
maintenance: aging
---

# aiodataloader — Asyncio DataLoader implementation for Python

License: permissive · Maintenance: aging · Downloads: 1.7M/mo

## What it is and what it does

Aiodataloader is a Python port of Facebook's original DataLoader pattern, designed to optimize data fetching in asyncio applications by batching and caching. It solves the N+1 query problem by automatically coalescing multiple individual load requests that occur within a single event-loop tick into a single batch operation sent to your data source. You provide a batch loading function that accepts a list of keys and returns a list of values; the loader handles deduplication, caching, and scheduling.

The package is commonly used in GraphQL servers but applies broadly to any scenario where you fetch data from a backend (database, API, service) and want to minimize round-trips. It maintains a per-request in-memory cache, so the same key loaded multiple times within one request returns the same future without re-fetching. Caching is request-scoped by design—you typically create a new loader instance per request to avoid cross-user data leakage.

Use it for:

- Implementing GraphQL resolvers that fetch related entities without triggering N+1 queries against a database
- Batching API calls to external services when handling concurrent requests in a web framework
- Deduplicating redundant data fetches within a single request lifecycle in async web applications
- Building data access layers that transparently batch individual load calls into efficient bulk operations
- Caching computed or fetched values for the duration of a single request to reduce memory churn

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Batches and caches concurrent data requests in asyncio applications, coalescing multiple loads into single batch operations to reduce backend round-trips.

Yes, if you are building an asyncio application (GraphQL service, async web framework, etc.) that fetches data from a backend and you want to eliminate N+1 query patterns. The package is production-stable, has no known vulnerabilities, and carries permissive licensing. The aging maintenance status is a minor concern but not a blocker—the core pattern is mature and the codebase is not archived. Install if batching and per-request caching solve a real problem in your data layer.

## Install

pip install aiodataloader
uv add aiodataloader
poetry add aiodataloader

## Installing aiodataloader

Before you install: Low friction install with a single runtime dependency (typing-extensions). Maintenance status is aging—last commit was 258 days ago, though the repository remains active and not archived.

License in practice: Licensed permissively, allowing use in commercial and proprietary projects without significant restriction.

Quickstart:

pip install aiodataloader

from aiodataloader import DataLoader

class UserLoader(DataLoader):
    async def batch_load_fn(self, keys):
        # Fetch multiple users by ID in one call
        return await fetch_users_by_ids(keys)

user_loader = UserLoader()
user = await user_loader.load(user_id)

Requires Python 3.7 or later and an asyncio event loop; batch_load_fn must be an async function.

Verify before relying:

- Whether the package is actively maintained or in maintenance-only mode despite the aging status
- Performance characteristics under high concurrency or with very large batch sizes
- Compatibility with recent asyncio changes in Python 3.13 and 3.14

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 1.7M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

asyncio batch data loading, request deduplication cache, graphql dataloader python, concurrent request batching, async data fetching layer, n+1 query prevention, memoization for async calls, graphql, async-batching, request-caching

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