Bulk Write Operations

This tutorial explains how to take advantage of Motor’s bulk write operation features. Executing write operations in batches reduces the number of network round trips, increasing write throughput.

This example describes using Motor with Tornado. Beginning in version 0.5 Motor can also integrate with asyncio instead of Tornado.

Bulk Insert

A batch of documents can be inserted by passing a list or generator to the insert_many() method. Motor will automatically split the batch into smaller sub-batches based on the maximum message size accepted by MongoDB, supporting very large bulk insert operations.

>>> @gen.coroutine
... def f():
...     yield db.test.insert_many(({'i': i} for i in range(10000)))
...     count = yield db.test.count()
...     print("Final count: %d" % count)
>>>
>>> IOLoop.current().run_sync(f)
Final count: 10000

Mixed Bulk Write Operations

New in version 0.2.

Motor also supports executing mixed bulk write operations. A batch of insert, update, and delete operations can be executed together using the Bulk API.

Ordered Bulk Write Operations

Ordered bulk write operations are batched and sent to the server in the order provided for serial execution. The return value is a document describing the type and count of operations performed.

>>> from pprint import pprint
>>>
>>> @gen.coroutine
... def f():
...    bulk = db.test.initialize_ordered_bulk_op()
...    # Remove all documents from the previous example.
...    bulk.find({}).remove()
...    bulk.insert({'_id': 1})
...    bulk.insert({'_id': 2})
...    bulk.insert({'_id': 3})
...    bulk.find({'_id': 1}).update({'$set': {'foo': 'bar'}})
...    bulk.find({'_id': 4}).upsert().update({'$inc': {'j': 1}})
...    bulk.find({'j': 1}).replace_one({'j': 2})
...    result = yield bulk.execute()
...    pprint(result)
...
>>> IOLoop.current().run_sync(f)
{'nInserted': 3,
 'nMatched': 2,
 'nModified': 2,
 'nRemoved': 10000,
 'nUpserted': 1,
 'upserted': [{'_id': 4, 'index': 5}],
 'writeConcernErrors': [],
 'writeErrors': []}

The first write failure that occurs (e.g. duplicate key error) aborts the remaining operations, and Motor raises BulkWriteError. The details attibute of the exception instance provides the execution results up until the failure occurred and details about the failure - including the operation that caused the failure.

>>> from pymongo.errors import BulkWriteError
>>>
>>> @gen.coroutine
... def f():
...     bulk = db.test.initialize_ordered_bulk_op()
...     bulk.find({'j': 2}).replace_one({'i': 5})
...     # Violates the unique key constraint on _id.
...
...     bulk.insert({'_id': 4})
...     bulk.find({'i': 5}).remove_one()
...     try:
...         yield bulk.execute()
...     except BulkWriteError as err:
...         pprint(err.details)
...
>>> IOLoop.current().run_sync(f)
{'nInserted': 0,
 'nMatched': 1,
 'nModified': 1,
 'nRemoved': 0,
 'nUpserted': 0,
 'upserted': [],
 'writeConcernErrors': [],
 'writeErrors': [{'code': 11000,
                  'errmsg': '... duplicate key error ...',
                  'index': 1,
                  'op': {'_id': 4}}]}

Unordered Bulk Write Operations

Unordered bulk write operations are batched and sent to the server in arbitrary order where they may be executed in parallel. Any errors that occur are reported after all operations are attempted.

In the next example the first and third operations fail due to the unique constraint on _id. Since we are doing unordered execution the second and fourth operations succeed.

>>> @gen.coroutine
... def f():
...     bulk = db.test.initialize_unordered_bulk_op()
...     bulk.insert({'_id': 1})
...     bulk.find({'_id': 2}).remove_one()
...     bulk.insert({'_id': 3})
...     bulk.find({'_id': 4}).replace_one({'i': 1})
...     try:
...         yield bulk.execute()
...     except BulkWriteError as err:
...         pprint(err.details)
...
>>> IOLoop.current().run_sync(f)
{'nInserted': 0,
 'nMatched': 1,
 'nModified': 1,
 'nRemoved': 1,
 'nUpserted': 0,
 'upserted': [],
 'writeConcernErrors': [],
 'writeErrors': [{'code': 11000,
                  'errmsg': '... duplicate key error ...',
                  'index': 0,
                  'op': {'_id': 1}},
                 {'code': 11000,
                  'errmsg': '... duplicate key error ...',
                  'index': 2,
                  'op': {'_id': 3}}]}

Write Concern

By default bulk operations are executed with the write_concern() of the collection they are executed against, typically the default write concern {w: 1}. A custom write concern can be passed to the execute() method. Write concern errors (e.g. wtimeout) will be reported after all operations are attempted, regardless of execution order.

.. Standalone MongoDB raises "can't use w>1" with this example, so skip it.

>>> @gen.coroutine
... def f():
...     bulk = db.test.initialize_ordered_bulk_op()
...     bulk.insert({'a': 0})
...     bulk.insert({'a': 1})
...     bulk.insert({'a': 2})
...     bulk.insert({'a': 3})
...     try:
...         # Times out if the replica set has fewer than four members.
...         yield bulk.execute({'w': 4, 'wtimeout': 1})
...     except BulkWriteError as err:
...         pprint(err.details)
...
>>> IOLoop.current().run_sync(f)
{'nInserted': 4,
 'nMatched': 0,
 'nModified': 0,
 'nRemoved': 0,
 'nUpserted': 0,
 'upserted': [],
 'writeConcernErrors': [{'code': 64,
                         'errInfo': {'wtimeout': True},
                         'errmsg': 'waiting for replication timed out'}],
 'writeErrors': []}