Motor 0.5’s 2015 beta made asyncio and Python 3.5’s native async/await syntax first-class options, while changing aggregate() to return a cursor immediately. The release is now historical: MongoDB says Motor is deprecated as of May 14, 2026, and recommends PyMongo Async for new asynchronous Python applications.
What Motor 0.5 beta changed
A. Jesse Jiryu Davis announced the beta on November 10, 2015. It added asyncio integration alongside Motor’s existing Tornado support, Python 3.5 compatibility, native coroutine syntax, and a simpler aggregation interface. The beta installation command was:
python -m pip install --pre motor==0.5b0The beta depended on PyMongo 2.8.0, which Davis described at the time as outdated. That dependency is a period detail, not a suitable version recommendation for current projects. Davis’s Motor 0.5 beta announcement
Using Motor 0.5 with asyncio
Motor 0.5 exposed AsyncIOMotorClient for applications using Python’s asyncio event loop. The announcement’s generator-based coroutine example used yield from and explicitly ran the coroutine to completion:
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import asyncio
from motor.motor_asyncio import AsyncIOMotorClient
client = AsyncIOMotorClient()
db = client.test_database
@asyncio.coroutine
def f():
yield from db.test_collection.insert_one({'_id': 1})
asyncio.get_event_loop().run_until_complete(f())Asyncio supplied the event loop and coroutine framework, not an HTTP server or web framework. Davis pointed readers to aiohttp for those separate application components. Motor 0.5 beta announcement
Writing native coroutines and iterating cursors
With Python 3.5, Motor 0.5 supported async def and await. Its announcement showed a native coroutine such as:
async def f():
await collection.insert({'_id': 1})Cursors returned by find(), aggregate(), and MotorGridFS.find() could be consumed using asynchronous iteration:
async def print_documents(collection):
async for document in collection.find():
print(document)For context, the release compared the older fetch_next pattern, an explicit await cursor.fetch_next loop, and async for. The cleaner iteration form also performed better in Davis’s reported example:
async def print_aggregate(collection, pipeline):
async for document in collection.aggregate(pipeline):
print(document)For a collection of 10,000 documents on Davis’s system in 2015, he reported 0.14 seconds for the older fetch_next loop and 0.04 seconds for async for, describing the latter as three times faster in that example. He also said to_list was twice as fast as async for, with a chunk size required. These are historical, author-reported measurements—not an independent benchmark or a prediction for current workloads. Motor 0.5 beta announcement
documents = await cursor.to_list(length=100)Use a bounded list when processing a batch at once is useful and the chosen chunk size fits the application’s memory and latency needs. Use asynchronous iteration when you want to handle results progressively without collecting a batch into a list.
How aggregation changed in Motor 0.5
The key API change was that aggregate() became cursor-first. In Motor 0.4 and earlier, code yielded the aggregate call with a cursor option, then iterated using fetch_next. In Motor 0.5, the call returned a cursor immediately: there was no yield on the call and no cursor={} argument.
| Motor version | Aggregation pattern |
|---|---|
| 0.4 and earlier | cursor = yield collection.aggregate(pipeline, cursor={}), then consume results through fetch_next. |
| 0.5 | cursor = collection.aggregate(pipeline); consume it with cursor iteration, including async for under Python 3.5. |
The change made aggregation fit the same cursor workflow as other result-producing operations. It did not eliminate a compatibility concern for older MongoDB servers: MongoDB 2.4 and earlier did not support aggregation cursors. Motor 0.5 retained cursor=False for those servers, causing results to be returned in the command response rather than through an aggregation cursor. Motor changelog
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Motor 0.5 explains an important transition in Python MongoDB development, but it should not be treated as a current setup guide. MongoDB’s documentation says Motor will be deprecated on May 14, 2026, and recommends migration to PyMongo Async. MongoDB describes the implementation difference this way: Motor delegates network operations to a thread pool, while PyMongo Async uses Python asyncio directly. MongoDB’s Motor-to-PyMongo Async migration guide
MongoDB’s May 14, 2025 announcement for Motor 3.7.1 says critical bug fixes will continue until May 14, 2027. That is a stated maintenance horizon for critical fixes, not a reason to begin new work on Motor. Motor 3.7.1 announcement
- Starting a new asynchronous application: evaluate PyMongo Async and use MongoDB’s migration documentation as the current starting point.
- Maintaining an existing Motor application: plan and test a migration rather than assuming the 2015 API examples or historical performance measurements represent current driver behavior.
- Reading old Motor code: recognize that Motor 0.5’s defining shifts were asyncio support, native coroutine syntax, and immediate cursor return from
aggregate().
MongoDB’s migration guide includes operation-level throughput comparisons. Results depend on the operations and workload, so the architectural distinction—thread-pool delegation versus direct asyncio execution—does not by itself establish which option will be faster for a particular application.
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