stellata 0.0.1

A simple ORM for PostgreSQL.

Stellata is a simple PostgreSQL ORM for Python 3.

Connecting

To connect to a database on the default host/port with a threaded connection pool of size 10:

import stellata.database

db = stellata.database.initialize(
    name='database',
    user='user',
    password='password',
    host='localhost',
    port=5432,
    pool_size=10
)

Defining Models

A user model might look something like this:

import stellata.fields
import stellata.model

class User(stellata.model.Model):
    __table__ = 'users'

    id = stellata.fields.UUID(null=False)
    active = stellata.fields.Integer(default=1)
    name = stellata.fields.Text(null=False)
    hash = stellata.fields.Varchar(length=255)
    email = stellata.fields.Text()
    dt = stellata.fields.Timestamp(null=False)

The __table__ property is the name of the corresponding table in the PostgreSQL database. Each property corresponds to a column, and the classes in stellata.fields are used to define the column types.

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, lambda: A)

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)

Migration

Once you’ve defined your models, you can sync them with your database by performing a migration.

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

Here, db is the handle returned by the stellata.database.initialize call. If you’d like to do a dry run, without actually executing any queries, do:

stellata.schema.migrate(db)

In both cases, this function will return a list of queries needed for the migration.

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.

CRUD Operations

Finally, let’s walk through how to use Stellata to query your 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()

Finally, 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()