asyncio.eager_task_factory lets coroutines begin running immediately when a task is created, before normal event-loop scheduling. This can reduce overhead for very short operations, especially cache hits and in-memory checks, but it also changes execution order and requires careful handling of side effects, exceptions, cancellation, and tests.
What eager execution means
With the traditional asyncio model, calling asyncio.create_task() schedules a coroutine to run on a later event-loop turn. With an eager task factory, the coroutine starts synchronously during task construction. If it completes without blocking, it may finish without ever being placed on the regular scheduling queue.
This behavior can help applications that create many tiny coroutines. Typical examples include cache lookups, validation functions, memoized computations, and wrappers that often return immediately. The optimization is not automatically beneficial, however. It should be measured against realistic workloads rather than assumed from a microbenchmark alone.
How to enable the factory
import asyncio
async def read_cache(key, cache):
if key in cache:
return cache[key]
await asyncio.sleep(0.01)
return None
async def main():
loop = asyncio.get_running_loop()
loop.set_task_factory(asyncio.eager_task_factory)
cache = {"course": "Python"}
task = asyncio.create_task(read_cache("course", cache))
result = await task
print(result)
asyncio.run(main())The configuration applies to the current loop. In larger systems, configure it in one well-documented initialization path so different parts of the application do not silently use different task semantics.
When the task returns to normal scheduling
If the coroutine reaches an await that cannot complete immediately, the task is scheduled on the event loop in the usual way. Eager execution therefore mainly helps the initial synchronous part and coroutines that can finish without suspension.
This is especially relevant to cache-first functions. A cache hit may complete immediately, while a miss proceeds to network or database I/O and resumes through normal scheduling.
Execution-order differences
The most important semantic change is ordering. Code that previously created a task and then performed another statement before the task started may no longer behave that way.
import asyncio
async def work():
print("coroutine started")
return 42
async def main():
loop = asyncio.get_running_loop()
loop.set_task_factory(asyncio.eager_task_factory)
print("before")
task = asyncio.create_task(work())
print("after")
print(await task)
asyncio.run(main())With eager execution, the coroutine message may appear before after. This can reveal hidden ordering assumptions in logging, metrics, state mutation, callbacks, dependency injection, and resource setup.
Exceptions may surface earlier
A coroutine that raises before its first blocking await can fail during the initial eager execution. Tests should cover both immediate completion and suspended execution paths.
async def validate(value):
if value < 0:
raise ValueError("invalid value")
return valueIn systems using task groups, runners, or structured shutdown, review how early failures interact with cancellation and cleanup. The article on asyncio.Runner is useful for understanding loop ownership and lifecycle. For phase synchronization, see asyncio.Barrier.
Cache-oriented use case
The classic scenario is an asynchronous function that checks local state before performing I/O.
async def get_user(user_id, cache, client):
if user_id in cache:
return cache[user_id]
response = await client.get(f"/users/{user_id}")
cache[user_id] = response
return responseWhen the cache hit rate is high, eager execution can avoid repeated scheduling overhead. When most calls reach the network, the optimization may have little practical effect.
Shared-state risks
Because the coroutine may run immediately, changes to lists, dictionaries, counters, context variables, or global objects can happen sooner than expected. Prefer explicit state transitions, immutable values where practical, and synchronization mechanisms whose behavior is tested under the new ordering.
For thread-safe FIFO communication, review queue.SimpleQueue. For CPU-bound parallelism, see InterpreterPoolExecutor.
Benchmarking correctly
A useful benchmark should separate immediate completion, short suspension, and I/O-dominated tasks. Network-heavy workloads may show almost no difference, while millions of cache hits may benefit significantly.
import asyncio
import time
async def immediate():
return 1
async def measure(count):
start = time.perf_counter()
tasks = [asyncio.create_task(immediate()) for _ in range(count)]
await asyncio.gather(*tasks)
return time.perf_counter() - startRepeat runs, discard warm-up results, compare Python versions, and observe CPU time, allocations, throughput, and tail latency. A small average improvement is not valuable if ordering bugs or worse p99 latency appear.
Testing strategy
Test log order, callbacks, exceptions before the first await, cache hits, cache misses, cancellation, timeouts, and cleanup. Do not assert only final values when sequence is part of the behavioral contract.
Interaction with cancellation
A task that completes eagerly may finish before cancellation code has a chance to run. A task that suspends still follows normal cancellation rules. This split means tests need both fast and slow paths, especially in libraries that expose timeout or cancellation guarantees.
Observability and debugging
Metrics and tracing hooks may observe tasks in a different order. Ensure spans, counters, and structured logs are created before task construction when they must include eager work. Low-level tools should not assume that all tasks first appear in the regular ready queue.
Production rollout
Introduce the factory behind a configuration switch, benchmark representative traffic, and compare error rates and latency distributions. Document the semantic change for maintainers. Roll out gradually when the application has complex callbacks or shared state.
Best practices
Enable the factory centrally, avoid accidental ordering dependencies, keep coroutines small and explicit, measure real workloads, preserve cleanup guarantees, and maintain regression tests. Treat eager execution as a semantic optimization rather than a harmless performance flag.
The official asyncio task documentation describes the API and its caveats. The broader asyncio documentation explains the event-loop model and related primitives.
Conclusion
asyncio.eager_task_factory can reduce scheduling overhead for short coroutines and cache-heavy code, but it changes exactly when coroutine code begins. Use it only with measured benefits, strong tests, and code designed for earlier execution.
