relativity 20.1.0

Dict to List

Imagine programming without hashmaps. For example, let’s say we have a list of Restaurant objects and City objects, and we want to get how many Restaurants are in each City.

Normally this is simple:

restaurants_in_city = {}

for restaurant in restaurants:
    city = restaurant.city
    restaurants_in_city[city] = restaurants_in_city.get(city, 0) + 1

def get_restaurant_count(city):
    return restaurants_in_city.get(city, 0)

But, imagine how you would approach the problem if the only available data structure was a list.

cities = []
restaurants_in_city = []

for restaurant in restaurants:
    missing = True
    for idx, city in enumerate(cities):
        if city == restaurant.city:
            restaurants_in_city[idx] += 1
            missing = False
    if missing:
        cities.append(restaurant.city)
        restaurants_in_city.append(1)

def get_restaurant_count(city):
    for idx, city2 in enumerate(cities):
        if city == city2:
            return restaurants_in_city[idx]
    return 0

Comparing the two examples, there are a few key differences:

• there are more low value local values (idx)

• single data structures split into multiple, which must then be kept in sync

• the code is longer, therefore harder to read, modify, and debug

Let’s leave this dystopian data structure wasteland behind for now and go back to regular python.

Dict to M2M

The same differences that showed up when programming with and without hashmaps will come up again when comparing programming with single-index hashmaps to relativistic multi-index hashmaps.

Returning to the restaurants and cities example, what if a restaurant can have multiple locations and we need to keep track of which cities each restaurant is in, as well as which restaurants are in each city.

Note that we allow a restaurant to have multiple locations within the same city, so sets must be used to avoid double counting.

restaurants_in_city = {}
cities_of_restaurant = {}

for restaurant in restaurants:
    for location in restaurant.locations:
        restaurants_in_city.setdefault(location.city, set()).add(restaurant)
        cities_of_restaurant.setdefault(restaurant, set()).add(location.city)

def get_restaurants_in_city(city):
    return restaurants_in_city.get(city, set())

def get_cities_of_restaurant(restaurant):
    return cities_of_restaurant.get(restaurant, set())

Relativity’s most basic data structure is a many-to-many mapping M2M. M2M is a systematic abstraction over associating every key with a set of values, and every value with a set of keys. See how M2M simplifies the problem:

restaurant_city_m2m = M2M()

for restaurant in restaurants:
    for location in restaurant.locations:
        restaurant_city_m2m.add(restaurant, location.city)

get_restaurants_in_city = restaurant_city_m2m.inv.get
get_cities_of_restaurant = restaurant_city_m2m.get

Recall that the advantages of having single-index hashmaps were shorter code, with fewer long lived data structures and fewer local values. M2M doesn’t replace dict any more than dict replaces list. Rather it is a new layer of abstraction that can greatly simplify a broad class of problems.

Is it possible to go further? Are there higher levels of abstraction that can represent more complex relationships in fewer data structures, and be manipulated with fewer lines of code and intermediate values?

M2M to M2MGraph

Where relativity really shines is releiving the programmer of the burden of keeping data structures consistent with updates. Let’s consider our restaurant example if we need to be able to add and remove locations one at a time and still be able to query.

With M2M objects, the problem is doable, but fiddly to implement:

restaurant_location = M2M()
location_city = M2M()

def add_location(location):
    restaurant_location.add(location.restaurant, location)
    location_city.add(location, location.city)

def remove_location(location):
    del location_city[location]
    del restaurant_location.inv[location]

def restaurants_in_city(city):
    restaurants = set()
    for location in location_city.inv[city]:
        for restaurant in restaurant_location.inv[location]:
            restaurants.add(restaurant)
    return restaurants

def cities_of_restaurant(restaurant):
    cities = set()
    for location in restaurant_location[restaurant]:
        for city in location_city[location]:
            cities.add(city)
    return cities

This problem can be simplified by stepping up a level of abstraction. Where M2M is a data structure of keys and values, M2MGraph is a higher-level data structure of M2M s. With M2MGraph, this problem becomes simple and intuitive:

data = M2MGraph([('restaurant', 'location'), ('location', 'city')])

def add_location(location):
    data['restaurant', 'location', 'city'].add(
        location.restaurant, location, location.city)

def remove_location(location):
    data.remove('location', location)

def restaurants_in_city(city):
    return data.pairs('city', 'restaurant').get(city)

def cities_of_restaurant(restaurant):
    return data.pairs('restaurant', 'city').get(restaurant)

Introducing Chain

Graphs are good for representing arbitrary sets of data, but they are awkward to query overy. M2MChain``s sequences of ``M2M``s, where the keys of ``M2M n are meant to be drawn from the same pool as the values of M2M n - 1.

A simple way to construct a chain is with the chain helper function.

students2classes = M2M([
    ('alice', 'math'),
    ('alice', 'english'),
    ('bob', 'english'),
    ('carol', 'math'),
    ('doug', 'chemistry')])

classmates = chain(students2clases, students2classes.inv)

By chaining the student:class map to itself, we can easily query which students have classes together.

>>> classmates.only('alice')
M2MChain([M2M([('alice', 'math'), ('alice', 'english')]), M2M([('math', 'carol'), ('math', 'alice'), ('english', 'bob'), ('english', 'alice')])])

>>> classmates.only('alice').m2ms[1]
M2M([('math', 'carol'), ('math', 'alice'), ('english', 'bob'), ('english', 'alice')])

>>> classmates.only('alice').m2ms[1].inv.keys()
['bob', 'carol', 'alice']

M2M + ORM

Let’s consider an example from Django to start.

from django.db import models

class Student(models.model):
    name = models.StringField()

class Course(models.model):
    name = models.StringField()
    students = models.ManyToMany(Student)

Students take many courses, and each course has many students.

Construting an M2M over these relationships is very natural:

from relativity import M2M
StudentCourse = Course.students.through

enrollments = M2M(
    StudentCourse.objects.all().values_list('student', 'course'))

DB Feature Sets

A typical SQL database, such as PostGres, MySQL, SQLServer, Oracle, or DB2 offers many features which can be split into four categories:

• relational data model and queries

• network protocol and multiple concurrent connections

• transactions, atomic updates, and MVCC

• persistent storage, backups, and read replicas

Let’s call these “relational”, “network”, “transactional”, and “persistence” feature sets.

“Alternative” Databases

The most widely used alternative is probably SQLite. SQLite has relational, transactional, and persistence feature sets but does not have a network protocol. Instead it must be embedded as a library inside another application.

Another example is the venerable ZODB. ZODB has network, transactional, and persistence feature sets but replaces the relational data model with an object data model.

As an extreme example of how less can be more, memcached has only network features. Data is stored ephemerally in the form of opaque blobs without any data model. There is no atomicity of updates: there is no way to ensure that two writes either both succeed or both fail.

The so-called “NoSQL” databases (cassandra, couchdb, mongodb, etc) generally provide network and persistence features but lack a relational data model and transactionality.

Relativity: Relational à la carte

In this design space, Relativity offers a relational feature set and nothing else. Relativity allows you to build in-memory data structures that represent relationships among arbitrary Python objects and then execute queries over those objects and relationships via a very natural and pythonic API.

M2MChain and M2MStar: rows of relations

M2MChain and M2MStar are implemented as thin wrappers over a list of M2M. The main feature they bring provide “row-iteration”. The difference between them is how they defined a row. M2MChain represents relationships that connect end-to-end. M2MStar represents relationships that all point to the same base object, similar to a star schema.

Shared M2M s

All of the higher order data structures are concerned with the structure between and among M2M s. The contents within a particular M2M does not need to maintain any invariants. That is, if all of the M2M s within one of the higher order data structures were scrambled up, the higher order data structure would still be valid.

(Contrast with, if you were to scramble the key sets and val sets around within an M2M, it would be totally inconsistent.)

This has important consequences, because it means that various instances of M2MGraph, M2MChain, and M2MStar may share their underlying M2M s, and continue to update them. This means that all of these higher order data structures can be treated as cheap and ephemeral.

For example, M2MGraph.chain(*cols) will construct and return a new M2MChain over the M2M s linking the passed columns. All that actually happens here is the M2MGraph is queried for the underlying M2M s, then the list of M2M s is passed to the M2MChain constructor which simply holds a reference to them.

Another way to think of M2MGraph, M2MChain and M2MStar is as cheap views over the underlying M2M s. No matter how much data is in the underlying M2M s, assembling one of these higher order data structures over top has a fixed, low cost.

Pandas

Both Relativity and Pandas enable clean extraction of data from a SQL database to an in-memory data structure which may be further processed. Both libraries provide data structures that can easily express queries over the in-memory data-set that would otherwise be very difficult and tempt a developer to go back to the database multiple times.

This sounds like Relativity and Pandas should be in competition; but, in practice they are complementary. Whereas Pandas is excellent at representing tabular data in rows and columns, Relativity excels at representing the foreign key relationships that connect rows in different tables. Pandas makes it easy to take a SQL result set and further refine it by filtering rows and addding columns. Relativity makes it easy to extract the foreign key relationships among many tables and further refine them by filtering by connectedness and adding additional relationships.

class Enrollment(models.Model):
     student = models.ForeignKey(Student)
     class = models.ForeignKey(Class)
     grade = models.FloatField()  # 0.0 - 5.0

# Pandas is great at determining each students GPA
enrollments_data_frame.group_by(['student']).mean()

>>> import pandas
>>> import relativity
>>> s = pandas.Series(data=[1, 2, 2], index=['a', 'a', 'b'])
>>> s
a    1
a    2
b    2
dtype: int64
>>> m2m = relativity.M2M(s.items())
>>> m2m
M2M([('a', 1L), ('a', 2L), ('b', 2L)])
>>> keys, vals = zip(*m2m.iteritems())
>>> s2 = pandas.Series(data=vals, index=keys)
>>> s2
a    1
a    2
b    2
dtype: int64

NetworkX

NetworkX is the “graph theory library” of Python:

“NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.”

NetworkX is great at representing arbitrarily connections among a group of nodes. Relativity has relationship-centric APIs and data-structures, wehere the M2M represents a single relationship, and M2MChain, M2MStar, and M2MGraph build higher order connections.

Underneath, both are backed by dict.