aiodataloader
Asyncio DataLoader implementation for Python
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.7 or later and an asyncio event loop; batch_load_fn must be an async function.
- 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 · maintenance · safety
permissive license (permissive) — Licensed permissively, allowing use in commercial and proprietary projects without significant restriction.
last release 2025-11-29 (258 days) · last repo commit 2025-11-29 · 294 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,735,516 downloads/mo, #3,607 on PyPI
Alternatives
Verify before relying
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)- 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
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 on this page. Verify before relying on it.
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
aiodataloader on PyPI
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.
Requires Python 3.7 or later and an asyncio event loop; batch_load_fn must be an async function.
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)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 | permissive license permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagetyping-extensions |
| Maintenance | Aging 258 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,735,516 / month, #3,607 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Software Development :: Libraries |
Evidence: aiodataloader-0.4.3-py3-none-any.whl
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See also async-cache · asyncache · aiohttp-client-cache · cachetools-async · py-memoize · aiocache · clr_loader · iden · litdata · runware