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Ed Costello
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More cross linking cleanup fixing intersphinx links
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source/use-cases/metadata-and-asset-management.txt

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@@ -150,7 +150,8 @@ Procedure
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`````````
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The most common operations inside of a CMS center on creating and
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editing content. Consider the following :py:meth:`<pymongo:pymongo.collection.Collection.insert>`
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editing content. Consider the following
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:py:meth:`insert() <pymongo:pymongo.collection.Collection.insert>`
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operation:
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.. code-block:: python
@@ -265,7 +266,7 @@ upon the basic update procedure:
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Because uploading the photo spans multiple documents and is a
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non-atomic operation, you must "lock" the file during upload by
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writing :py:class:`datetime.utcnow() <python:datetime.utcnow>` in the
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writing :py:meth:`datetime.utcnow() <python:datetime.datetime.utcnow>` in the
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record. This helps when there are multiple concurrent editors and lets
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the application detect stalled file uploads. This operation assumes
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that, for photo upload, the last update will succeed:

source/use-cases/product-catalog.txt

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@@ -525,24 +525,27 @@ per-connection or per-operation basis. In :api:`PyMongo
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<python/current>`, set the :py:attr:`read_preference
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<pymongo.collection.Collection.read_preference>` argument.
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The :py:class:`SECONDARY <pymongo:pymongo.ReadPreference>` property in
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the following example, permits reads from a :term:`secondary` (as well
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as a primary) for the entire connection .
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The
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:py:attr:`SECONDARY <pymongo:pymongo.connection.Connection.read_preference>`
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property in the following example, permits reads from a
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:term:`secondary` (as well as a primary) for the entire connection .
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.. code-block:: python
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conn = pymongo.Connection(read_preference=pymongo.SECONDARY)
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Conversely, the :py:class:`SECONDARY_ONLY
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<pymongo:pymongo.ReadPreference>` read preference means that the
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Conversely, the
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:py:attr:`SECONDARY_ONLY <pymongo:pymongo.connection.Connection.read_preference>`
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read preference means that the
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client will only send read operation only to the secondary member
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.. code-block:: python
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conn = pymongo.Connection(read_preference=pymongo.SECONDARY_ONLY)
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You can also specify :py:attr:`read_preference
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<pymongo.collection.Collection.read_preference>` for specific queries,
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You can also specify
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:py:attr:`read_preference <pymongo.collection.Collection.read_preference>`
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for specific queries,
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as follows:
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.. code-block:: python

source/use-cases/storing-log-data.txt

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@@ -409,7 +409,8 @@ following command:
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>>> db.events.ensure_index([('time', 1), ('host', 1)])
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To analyze the performance for the above query using this index, issue
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the :py:meth:`q_events.explain() <pymongo:pymongo.collection.Collection.explain>`
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the
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:py:meth:`q_events.explain() <pymongo:pymongo.cursor.Cursor.explain>`
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method in a Python console. This will return something that resembles:
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.. code-block:: pycon
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>>> db.events.ensure_index([('host', 1), ('time', 1)])
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Use the :py:meth:`q_events.explain() <pymongo:pymongo.collection.Collection.explain>`
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Use the
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:py:meth:`q_events.explain() <pymongo:pymongo.cursor.Cursor.explain>`
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operation to test the performance:
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.. code-block:: pycon
@@ -489,9 +491,9 @@ Aggregation
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The :term:`aggregation framework` provides the capacity for queries
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that select, process, and aggregate results from large numbers of
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documents. The :method:`aggregate()` (and :dbcommand:`aggregate`
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documents. The :method:`aggregate() <db.collection.aggregate()>` (and :dbcommand:`aggregate`
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:term:`command <database command>`) offers greater flexibility,
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capacity with less complexity than the existing :dbcommand:`mapreduce`
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capacity with less complexity than the existing :dbcommand:`mapReduce <mapReduce>`
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and :dbcommand:`group` aggregation.
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Consider the following aggregation :term:`pipeline`: [#sql-aggregation-equivalents]_

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