{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Use Case 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find the companies where X has worked, and their roles at those companies" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What questions would we have to ask of our data?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "_\"Which companies has X worked for, and in what roles?\"_\n", "\n", "Reviewing this question we can identify several entities, attributes and relationships. We have the concept of a _company_, a _person_ (X), and a _role_. Further, a person _worked for_ a company.\n", "\n", "_Company_ and _Person_ are both entities, which we'll model as vertices with appropriate labels. For now, we'll assume a direct relationship between a person and a company: a person _WORKED FOR_ a company. We'll make _role_ an attribute of this relationship.\n", "\n", "Adding in a few properties – _firstName_ and _lastName_ for a person, _name_ for a company – we end up with the following data model:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Keep it simple" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Over the course of this exercise we'll see _role_ change place several times. At this stage it's a simple attribute of a relationship. In later steps we'll see it promoted to being a vertex in its own right.\n", "\n", "As far as our current use case is concerned, role appears to be a simple value type, much like colour, height or weight. If it were a complex value type with several fields – such as address – or if there were some explicit structural relations between values – as there are in a category hierarchy – we would consider making it a vertex from the outset." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sample dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll now create a sample dataset in line with our model. We'll include enough data to ensure that our queries have to exclude some portions of the graph in order to return a correct result." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating our sample data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%load_ext ipython_unittest\n", "%run '../util/neptune.py'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "neptune.clear()\n", "g = neptune.graphTraversal()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(g.\n", " addV('Person').property(id,'p-1').property('firstName','Martha').property('lastName','Rivera').\n", " addV('Person').property(id,'p-2').property('firstName','Richard').property('lastName','Roe').\n", " addV('Person').property(id,'p-3').property('firstName','Li').property('lastName','Juan').\n", " addV('Person').property(id,'p-4').property('firstName','John').property('lastName','Stiles').\n", " addV('Person').property(id,'p-5').property('firstName','Saanvi').property('lastName','Sarkar').\n", " addV('Company').property(id,'c-1').property('name','Example Corp').\n", " addV('Company').property(id,'c-2').property('name','AnyCompany').\n", " V('p-1').addE('WORKED_FOR').to(V('c-1')).property('role','Principal Analyst'). \n", " V('p-2').addE('WORKED_FOR').to(V('c-1')).property('role','Senior Analyst'). \n", " V('p-3').addE('WORKED_FOR').to(V('c-1')).property('role','Analyst').\n", " V('p-4').addE('WORKED_FOR').to(V('c-1')).property('role','Analyst'). \n", " V('p-5').addE('WORKED_FOR').to(V('c-2')).property('role','Manager').\n", " V('p-3').addE('WORKED_FOR').to(V('c-2')).property('role','Associate Analyst').\n", " toList())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Querying the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Query 1 – Which companies has Li worked for, and in what roles?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To answer this question, we'll have to perform the following steps:\n", "\n", " 1. Start at the Person vertex representing Li\n", " 2. Follow WORKED_FOR edges to find each Company for whom Li has worked\n", " 3. Select the Company details, and the role property of the relationship" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Write a failing unit test" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%unittest\n", "\n", "results = None # TODO\n", "\n", "assert results == [{'company': 'Example Corp', 'role': 'Analyst'}, \n", " {'company': 'AnyCompany', 'role': 'Associate Analyst'}]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### And write the query to make it pass" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%unittest\n", "\n", "results = (g.V('p-3').\n", " outE('WORKED_FOR').as_('e').\n", " otherV().\n", " project('company', 'role').\n", " by('name').\n", " by(select('e').values('role')).\n", " toList())\n", "\n", "assert results == [{'company': 'Example Corp', 'role': 'Analyst'}, \n", " {'company': 'AnyCompany', 'role': 'Associate Analyst'}]" ] } ], "metadata": { "kernelspec": { "display_name": "conda_python3", "language": "python", "name": "conda_python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }