Getting Started

This page gives a good introduction in how to get started with Recipe. This assumes you already have Recipe installed. If you do not, head over to Installing Recipe.

First, make sure that:

Let’s gets started with some simple use cases and examples.

Creating a Shelf

A Shelf is a place to store SQL fragments. In recipe these are called Ingredients.

Ingredients can contain columns that should be part of the SELECT portion of a query, filters that are part of a WHERE clause of a query, group_bys that contribute to a query’s GROUP BY and havings which add HAVING limits to a query.

You won’t have to construct an Ingredient with all these parts directly because Recipe contains convenience classes that help you build the most common SQL fragments. The two most common Ingredient subclasses are Dimension which provides both a column and a grouping on that column and Metric which provides a column aggregation.

Shelf acts like a dictionary. The keys are strings and the values are Ingredients. The keys are a shortcut name for the ingredient. Here’s an example.

from recipe import *

# Define a database connection
oven = get_oven('sqlite://')
Base = declarative_base(bind=oven.engine)

# Define a SQLAlchemy mapping
class Census(Base):
    state = Column('state', String(), primary_key=True)
    sex = Column('sex', String())
    age = Column('age', Integer())
    pop2000 = Column('pop2000', Integer())
    pop2008 = Column('pop2008', Integer())

    __tablename__ = 'census'
    __table_args__ = {'extend_existing': True}

# Use that mapping to define a shelf.
shelf = Shelf({
    'state': Dimension(Census.state),
    'age': WtdAvgMetric(Census.age, Census.pop2000),
    'population': Metric(func.sum(Census.pop2000))

This is a shelf with two metrics (a weighted average of age, and the sum of population) and a dimension which lets you group on US State names.

Using the Shelf to build a Recipe

Now that you have the shelf, you can build a Recipe.

r = Recipe(shelf=shelf, session=oven.Session())\


This results in

West Virginia,38.555058651148165,West Virginia
Rhode Island,37.20343773873182,Rhode Island

Note that a recipe contains data from a single table.`

Defining Shelves and Recipes Using Configuration

Recipes and shelves can be defined using plain ole’ python objects. In the following example we’ll use YAML. For instance, we can define the shelf using this yaml config.

    kind: Dimension
    field: state
    kind: WtdAvgMetric
    field: age
    weight: pop2000
    kind: Metric
    field: pop2000

We can load this config by parsing it against any selectable, which can be a SQLAlchemy mapping, a SQLAlchemy select, or another Recipe.

shelf_yaml = yaml.load('shelf.yaml')
s = Shelf.from_config(shelf_yaml, Census)

We can also define a Recipe with Configuration

- age
- population
- state
- '-age'

If we load that we get a Recipe

recipe_yaml = yaml.load('shelf.yaml')
recipe = Recipe.from_config(s, recipe_yaml, session=oven.Session())

This results in a list of the oldest US states and their populations:

West Virginia,38.555058651148165,1805847,West Virginia
Rhode Island,37.20343773873182,1047200,Rhode Island

Adding Features with Extensions

Using extensions, you can add features to Recipe. Here are a few interesting thing you can do. This example mixes in two extensions.

AutomaticFilters defines filters (where clauses) using configuration. In this case were are filtering to states that start with the letter C.

CompareRecipe mixes in results from another recipe. In this case, we are using this comparison recipe to calculate an average age across all states.

recipe_yaml = yaml.load(r)
recipe = Recipe.from_config(s, recipe_yaml, session=oven.Session(),
    extension_classes=(AutomaticFilters, CompareRecipe))\
    .automatic_filters({'state__like': 'C%'})\
    .compare(Recipe(shelf=s, session=oven.Session()).metrics('age'))

The output looks like this

SELECT census.state AS state,
    CAST(sum(census.age * census.pop2000) AS FLOAT) / (coalesce(CAST(sum(census.pop2000) AS FLOAT), 0.0) + 1e-09) AS age,
    sum(census.pop2000) AS population,
    avg(anon_1.age) AS age_compare
FROM census
(SELECT CAST(sum(census.age * census.pop2000) AS FLOAT) / (coalesce(CAST(sum(census.pop2000) AS FLOAT), 0.0) + 1e-09) AS age
FROM census) AS anon_1 ON 1=1
WHERE census.state LIKE 'C%'
GROUP BY census.state
ORDER BY CAST(sum(census.age * census.pop2000) AS FLOAT) / (coalesce(CAST(sum(census.pop2000) AS FLOAT), 0.0) + 1e-09) DESC


Now, go check out the API Documentation or look at an Overview of Recipe Concepts.