Building data-driven web apps with Pyramid and SQLAlchemy Transcripts
Chapter: MongoDB edition
Lecture: Concepts: MongoEngine and MongoDB
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Let's close out this chapter by reviewing
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some of the concepts that we discussed
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while going through our conversion
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from SQLAlchemy to MongoDB.
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First thing, we have to register connections
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so pretty straight forward.
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We just pass an alias in a name for
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the database, and off it goes.
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So here we said core, pypi_demo, and we
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call MongoEngine.register_connection.
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As long as our entities use the same alias
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which is called db_alias in their little meta data thing
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long as they use the same one, it just uses this connection.
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Alright, it's kind of this ambient way to get to the database
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so you don't have to go through that session stuff.
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The classes that we map to the database derive from
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a built-in base class mongoengine.Document.
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Everything that's going to be saved to a
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top level collection is a mongoengine.Document.
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Here we have a package.
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In this one, we're changing what the id is, like we did
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we used the string instead of auto-incrementing id.
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If we want to take a BSON, auto-generated BSON id
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we can just omit that line
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but this one we want to make sure it's a string
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which is that unique, I guess we don't say unique here
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we should say unique but that's a feature
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but it's not one that we set.
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Anyway, we want to make sure this is a unique thing
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and that is just the name of the package.
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And we have DatetimeField, StringField and so on.
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Pretty straight forward.
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What's really powerful though, is that we don't have to
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limit ourselves to just tabular data.
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We can have things like name, summary and homepage
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that would be tabular, but then we can embed stuff.
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Normally in a relational database, this takes other tables
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to manage these many-to-many relationships.
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Here we just have maintainers
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and we're just embedding a list of items.
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If we look at the record in the database
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it looks like this.
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We have our id, a date, the summary, and then we have
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a bunch of maintainers that were put in there.
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And this is just the id, this is like a relationship
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back to the user table, cause it's many-to-many
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we're not embedding users in here, right
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but we can't avoid that maintainers table
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that is the normalization table.
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If you want to do a simple query, so easy.
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Just package.objects(), column equals value
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for direct equal, and you want one you say first()
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if you want to do all of them, you just iterate over it
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or you say .all(), things like that.
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If you want to filter on one or more of the fields
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if you want to filter on more than one and do an and
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just comma separate them like any keyword arguments.
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So in this case, the ID must match
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the name of the past package name.
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Like Request or SQLAlchemy.
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We call first, and this actually triggers
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the execution of the query.
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If we didn't call that, we'd have to loop over it
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or something, until then we could keep adding to it.
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Now, it's going to return either one or None.
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Let's imagine a world that we haven't been working in yet
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that's a little bit more complicated.
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We're doing a hotel, or Airbnb or something
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and we have hotel rooms, those rooms have bookings
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and the bookings store which guests are in them.
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So we have a booking, object, it has an embedded object
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called guest, that embedded guest object has an id.
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If we want to answer the question of
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show me all of the bookings for a given user
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or given owner, we'd come through and say
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go find that owner, going to do a simple query to get them
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and then I can go to the room and say
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go to the room's bookings, this double underscore
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right there says, navigate the hierarchy.
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We have bookings, a list of guests or something like that
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or an embedded singular object called guest
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and it has a guest id, so we're going into the
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bookings list or embedded item.
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So we go to the guest id, and then we want to do an
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in query which is a list of IDs.
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So find me all the bookings who have a guest
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who is contained within the owners family IDs.
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Pretty awesome that we can do this.
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And the way you do it, is these double underscores
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both navigate hierarchies, and they apply operators like
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in, so __in, that's the operator
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that's being applied in the query.
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Now one of the patterns that I like to use
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is make sure there's no data access outside this function.
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We could return kind of a prepared query that's not
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actually executed, and then if we run into an error
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that error might happen in a view model
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even maybe in a template.
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So my sort of best practice for you is
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to make sure all the data access is done
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by the time you leave this function.
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In this case a real easy way to do that is
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convert this to an in-memory list.
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That means the whole thing is exhausted
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and we're not using data access anymore
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we're just now working in memory.
