Frequently Asked Questions

Is PyMongo thread-safe?

PyMongo is thread-safe and provides built-in connection pooling for threaded applications.

How does connection pooling work in PyMongo?

Every MongoClient instance has a built-in connection pool. The client opens sockets on demand to support the number of concurrent MongoDB operations your application requires. There is no thread-affinity for sockets.

The client instance opens one additional socket per server in your MongoDB topology for monitoring the server’s state.

The size of each connection pool is capped at maxPoolSize, which defaults to 100. When a thread in your application begins an operation on MongoDB, if all other sockets are in use and the pool has reached its maximum, the thread pauses, waiting for a socket to be returned to the pool by another thread.

The default configuration for a MongoClient works for most applications:

client = MongoClient(host, port)

Create this client once when your program starts up, and reuse it for all operations. It is a common mistake to create a new client for each request, which is very inefficient.

To support extremely high numbers of concurrent MongoDB operations within one process, increase maxPoolSize:

client = MongoClient(host, port, maxPoolSize=200)

... or make it unbounded:

client = MongoClient(host, port, maxPoolSize=None)

By default, any number of threads are allowed to wait for sockets to become available, and they can wait any length of time. Override waitQueueMultiple to cap the number of waiting threads. E.g., to keep the number of waiters less than or equal to 500:

client = MongoClient(host, port, maxPoolSize=50, waitQueueMultiple=10)

When 500 threads are waiting for a socket, the 501st that needs a socket raises ExceededMaxWaiters. Use this option to bound the amount of queueing in your application during a load spike, at the cost of additional exceptions.

Once the pool reaches its max size, additional threads are allowed to wait indefinitely for sockets to become available, unless you set waitQueueTimeoutMS:

client = MongoClient(host, port, waitQueueTimeoutMS=100)

A thread that waits more than 100ms (in this example) for a socket raises ConnectionFailure. Use this option if it is more important to bound the duration of operations during a load spike than it is to complete every operation.

When close() is called by any thread, all sockets are closed.

Does PyMongo support Python 3?

PyMongo supports Python 3.x where x >= 2. See the Python 3 FAQ for details.

Does PyMongo support asynchronous frameworks like Gevent, Tornado, or Twisted?

PyMongo fully supports Gevent.

To use MongoDB with Tornado see the Motor project.

For Twisted, see TxMongo. Compared to PyMongo, TxMongo is less stable, lacks features, and is less actively maintained.

Key order in subdocuments – why does my query work in the shell but not PyMongo?

The key-value pairs in a BSON document can have any order (except that _id is always first). The mongo shell preserves key order when reading and writing data. Observe that “b” comes before “a” when we create the document and when it is displayed:

> // mongo shell.
> db.collection.insert( { "_id" : 1, "subdocument" : { "b" : 1, "a" : 1 } } )
WriteResult({ "nInserted" : 1 })
> db.collection.find()
{ "_id" : 1, "subdocument" : { "b" : 1, "a" : 1 } }

PyMongo represents BSON documents as Python dicts by default, and the order of keys in dicts is not defined. That is, a dict declared with the “a” key first is the same, to Python, as one with “b” first:

>>> print {'a': 1.0, 'b': 1.0}
{'a': 1.0, 'b': 1.0}
>>> print {'b': 1.0, 'a': 1.0}
{'a': 1.0, 'b': 1.0}

Therefore, Python dicts are not guaranteed to show keys in the order they are stored in BSON. Here, “a” is shown before “b”:

>>> print collection.find_one()
{u'_id': 1.0, u'subdocument': {u'a': 1.0, u'b': 1.0}}

To preserve order when reading BSON, use the SON class, which is a dict that remembers its key order. First, get a handle to the collection, configured to use SON instead of dict:

>>> from bson import CodecOptions, SON
>>> opts = CodecOptions(document_class=SON)
>>> opts  
CodecOptions(document_class=<class 'bson.son.SON'>,
>>> collection_son = collection.with_options(codec_options=opts)

Now, documents and subdocuments in query results are represented with SON objects:

>>> print collection_son.find_one()
SON([(u'_id', 1.0), (u'subdocument', SON([(u'b', 1.0), (u'a', 1.0)]))])

The subdocument’s actual storage layout is now visible: “b” is before “a”.

Because a dict’s key order is not defined, you cannot predict how it will be serialized to BSON. But MongoDB considers subdocuments equal only if their keys have the same order. So if you use a dict to query on a subdocument it may not match:

>>> collection.find_one({'subdocument': {'a': 1.0, 'b': 1.0}}) is None

Swapping the key order in your query makes no difference:

>>> collection.find_one({'subdocument': {'b': 1.0, 'a': 1.0}}) is None

... because, as we saw above, Python considers the two dicts the same.

There are two solutions. First, you can match the subdocument field-by-field:

>>> collection.find_one({'subdocument.a': 1.0,
...                      'subdocument.b': 1.0})
{u'_id': 1.0, u'subdocument': {u'a': 1.0, u'b': 1.0}}

The query matches any subdocument with an “a” of 1.0 and a “b” of 1.0, regardless of the order you specify them in Python or the order they are stored in BSON. Additionally, this query now matches subdocuments with additional keys besides “a” and “b”, whereas the previous query required an exact match.

The second solution is to use a SON to specify the key order:

>>> query = {'subdocument': SON([('b', 1.0), ('a', 1.0)])}
>>> collection.find_one(query)
{u'_id': 1.0, u'subdocument': {u'a': 1.0, u'b': 1.0}}

The key order you use when you create a SON is preserved when it is serialized to BSON and used as a query. Thus you can create a subdocument that exactly matches the subdocument in the collection.

What does CursorNotFound cursor id not valid at server mean?

Cursors in MongoDB can timeout on the server if they’ve been open for a long time without any operations being performed on them. This can lead to an CursorNotFound exception being raised when attempting to iterate the cursor.

How do I change the timeout value for cursors?

MongoDB doesn’t support custom timeouts for cursors, but cursor timeouts can be turned off entirely. Pass no_cursor_timeout=True to find().

How can I store decimal.Decimal instances?

MongoDB only supports IEEE 754 floating points - the same as the Python float type. The only way PyMongo could store Decimal instances would be to convert them to this standard, so you’d really only be storing floats anyway - we force users to do this conversion explicitly so that they are aware that it is happening.

I’m saving 9.99 but when I query my document contains 9.9900000000000002 - what’s going on here?

The database representation is 9.99 as an IEEE floating point (which is common to MongoDB and Python as well as most other modern languages). The problem is that 9.99 cannot be represented exactly with a double precision floating point - this is true in some versions of Python as well:

>>> 9.99

The result that you get when you save 9.99 with PyMongo is exactly the same as the result you’d get saving it with the JavaScript shell or any of the other languages (and as the data you’re working with when you type 9.99 into a Python program).

Can you add attribute style access for documents?

This request has come up a number of times but we’ve decided not to implement anything like this. The relevant jira case has some information about the decision, but here is a brief summary:

  1. This will pollute the attribute namespace for documents, so could lead to subtle bugs / confusing errors when using a key with the same name as a dictionary method.
  2. The only reason we even use SON objects instead of regular dictionaries is to maintain key ordering, since the server requires this for certain operations. So we’re hesitant to needlessly complicate SON (at some point it’s hypothetically possible we might want to revert back to using dictionaries alone, without breaking backwards compatibility for everyone).
  3. It’s easy (and Pythonic) for new users to deal with documents, since they behave just like dictionaries. If we start changing their behavior it adds a barrier to entry for new users - another class to learn.

What is the correct way to handle time zones with PyMongo?

Prior to PyMongo version 1.7, the correct way is to only save naive datetime instances, and to save all dates as UTC. In versions >= 1.7, the driver will automatically convert aware datetimes to UTC before saving them. By default, datetimes retrieved from the server (no matter what version of the driver you’re using) will be naive and represent UTC. In newer versions of the driver you can set the MongoClient tz_aware parameter to True, which will cause all datetime instances returned from that MongoClient to be aware (UTC). This setting is recommended, as it can force application code to handle timezones properly.


Be careful not to save naive datetime instances that are not UTC (i.e. the result of calling

Something like pytz can be used to convert dates to localtime after retrieving them from the database.

How can I save a instance?

PyMongo doesn’t support saving instances, since there is no BSON type for dates without times. Rather than having the driver enforce a convention for converting instances to datetime.datetime instances for you, any conversion should be performed in your client code.

When I query for a document by ObjectId in my web application I get no result

It’s common in web applications to encode documents’ ObjectIds in URLs, like:


Your web framework will pass the ObjectId portion of the URL to your request handler as a string, so it must be converted to ObjectId before it is passed to find_one(). It is a common mistake to forget to do this conversion. Here’s how to do it correctly in Flask (other web frameworks are similar):

from pymongo import MongoClient
from bson.objectid import ObjectId

from flask import Flask, render_template

client = MongoClient()
app = Flask(__name__)

def show_post(_id):
   # NOTE!: converting _id from string to ObjectId before passing to find_one
   post = client.db.posts.find_one({'_id': ObjectId(_id)})
   return render_template('post.html', post=post)

if __name__ == "__main__":

How can I use PyMongo from Django?

Django is a popular Python web framework. Django includes an ORM, django.db. Currently, there’s no official MongoDB backend for Django.

django-mongodb-engine is an unofficial MongoDB backend that supports Django aggregations, (atomic) updates, embedded objects, Map/Reduce and GridFS. It allows you to use most of Django’s built-in features, including the ORM, admin, authentication, site and session frameworks and caching.

However, it’s easy to use MongoDB (and PyMongo) from Django without using a Django backend. Certain features of Django that require django.db (admin, authentication and sessions) will not work using just MongoDB, but most of what Django provides can still be used.

One project which should make working with MongoDB and Django easier is mango. Mango is a set of MongoDB backends for Django sessions and authentication (bypassing django.db entirely).

Does PyMongo work with mod_wsgi?

Yes. See the configuration guide for PyMongo and mod_wsgi.

How can I use something like Python’s json module to encode my documents to JSON?

The json module won’t work out of the box with all documents from PyMongo as PyMongo supports some special types (like ObjectId and DBRef) that are not supported in JSON. We’ve added some utilities for working with JSON in the json_util module.

Why do I get OverflowError decoding dates stored by another language’s driver?

PyMongo decodes BSON datetime values to instances of Python’s datetime.datetime. Instances of datetime.datetime are limited to years between datetime.MINYEAR (usually 1) and datetime.MAXYEAR (usually 9999). Some MongoDB drivers (e.g. the PHP driver) can store BSON datetimes with year values far outside those supported by datetime.datetime.

There are a few ways to work around this issue. One option is to filter out documents with values outside of the range supported by datetime.datetime:

>>> from datetime import datetime
>>> coll = client.test.dates
>>> cur = coll.find({'dt': {'$gte': datetime.min, '$lte': datetime.max}})

Another option, assuming you don’t need the datetime field, is to filter out just that field:

>>> cur = coll.find({}, projection={'dt': False})