Field Types

• BigInteger

• Boolean

• Integer

• Numeric

• Text

• Timestamp

• UUID

• Varchar

Relations

Like any good ORM, Stellata supports relations among models. Here are two related models, A and B:

import stellata.model
import stellata.fields
import stellata.relations

class A(stellata.model.Model):
    __table__ = 'a'

    id = stellata.fields.UUID()
    foo = stellata.fields.Text()
    bar = stellata.fields.Integer(default=0, null=False)

    b = stellata.relations.HasMany(lambda: B.a_id)

class B(stellata.model.Model):
    __table__ = 'b'

    id = stellata.fields.UUID()
    a_id = stellata.fields.UUID()
    bar = stellata.fields.Integer(null=False)

    a = stellata.relations.BelongsTo(lambda: B.a_id, lambda: A)

Indexes

Indexes make your queries go fast. Let’s add a couple indexes to our table:

import stellata.model
import stellata.fields
import stellata.index
import stellata.relations

class A(stellata.model.Model):
    __table__ = 'a'

    id = stellata.fields.UUID()
    foo = stellata.fields.Text()
    bar = stellata.fields.Integer(default=0, null=False)

    b = stellata.relations.HasMany(lambda: B.A_id, lambda: A)

    primary_key = stellata.index.PrimaryKey(lambda: A.id)
    foo_index = stellata.index.Index(lambda: A.foo, unique=True)

Serialization

Once you have some data, it won’t be long until you want to convert it to JSON. To do so, use:

stellata.model.serialize(model)

This will recursively serialize objects/relations, and you can pass it an object, dictionary, list, etc.

Meta

In some cases, it’s handy to be able to iterate over all of the models you’ve defined. For example, you might want to truncate tables for a unit test. In that case, you can do this:

for model in stellata.model.registered():
    # do something with model

Resetting

In some development scripts, you might want to clean your database. If you so desire, you can do this:

stellata.schema.drop_tables_and_lose_all_data(db, execute=True)

As its name suggests, this function is very destructive, so don’t do this on a production database.

Create

Let’s create a new instance of A.

a = A.create(A(foo='bar', bar=5))
a.id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca'
a.foo == 'bar'
a.bar == 4

Or, if we want to create in bulk:

result = A.create([
    A(foo='bar', bar=6),
    A(foo='baz', bar=7)
])

len(result) == 2

If you created a unique index on some fields, you can take advantage of the PostgreSQL ON CONFLICT feature:

A.create(A(foo='baz', bar=9), unique=(A.foo,))

Now, if there’s already a row with foo having a value of baz, then the bar column will be updated to have a value of 9, rather than creating a new row.

Read

To read from the database, we’ll want to use the where method. Let’s get the instance of A we created before:

a = A.where(A.id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca').get()
a.id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca'

Jeez Rick, what’s that syntax? We’re using operator overloading, Morty. What else can we do?

A.where(A.bar < 5).get()
A.where(A.bar > 1).get()
A.where(A.id << ['2a12f545-c587-4b99-8fd2-57e79f7c8bca', '31be0c81-f5ee-49b9-a624-356402427f76']).get()

That last one is a where in query, in case that wasn’t—burp—obvious. We can also use AND and OR in our queries like so:

A.where((A.id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca') | (A.bar < 5)).get()
A.where((A.id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca') & (A.bar > 1)).get()

Other bells and whistles:

A.where(A.bar < 5).order(A.bar, 'asc').limit(5).get()

A common read operation is to find all rows where a column matches some value, so we can use a shorthand:

A.find('2a12f545-c587-4b99-8fd2-57e79f7c8bca')

By default, the id field will be used, but you can also specify your own field:

A.find(5, A.bar)

If given a list, find will return a dictionary keyed on the value of the field you specify:

a = A.find(['2a12f545-c587-4b99-8fd2-57e79f7c8bca', '31be0c81-f5ee-49b9-a624-356402427f76'])
a['2a12f545-c587-4b99-8fd2-57e79f7c8bca'].id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca'
a['31be0c81-f5ee-49b9-a624-356402427f76'].id == '31be0c81-f5ee-49b9-a624-356402427f76'

Joins

We can use those relations we set up earlier with joins. Let’s say we create the following:

a = A.create(A(foo='bar', bar=5))
a.id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca'
b = B.create([
    B(a_id='2a12f545-c587-4b99-8fd2-57e79f7c8bca', qux=3)
    B(a_id='2a12f545-c587-4b99-8fd2-57e79f7c8bca', qux=5)
])

Now, we can do this:

a = A.join(A.b).where(A.id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca').get()
a.id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca'
len(a.b) == 2
a.b[0].qux == 3
a.b[1].qux == 5

Or, the other way:

b = B.join(B.a).where(B.qux << [3, 5]).get()
len(b) == 2
b[0].qux == 3
b[0].a.id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca'
b[1].qux == 5
b[1].a.id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca'

By default, joins will be executed via multiple SELECT queries. If you’d prefer to do a JOIN instead, just do this:

a = A.join_with('join').join(A.b).where(A.id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca').get()

The result is the same as before, but the underlying query was different. Which method you use is entirely up to you, and may vary with different queries.

Update

As you might expect, update queries combine the syntax for creating and reading:

A.where(A.id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca').update(A(bar=7))

Delete

This one is easy now.

A.where(id == '2a12f545-c587-4b99-8fd2-57e79f7c8bca').delete()

Or, if you prefer a single TRUNCATE operation:

A.truncate()

Transactions

Stellata also has support for PostgreSQL transactions:

A.begin()
A.truncate()
A.create([A(bar=1), A(bar=2)])
A.commit()