Stealth Mode

SWE CS 373 Presentation

Transcript

1. Series-Z The Internet Startup Database Austin Thomas, Moiz Rizvi, Danyal Rizvi, and Amanda Keebler
2. Introduction
3. The Team Stats: • 15 commits • 13 issues • 10 unit tests Major Responsibilities: • API endpoints • Docker/deployment Stats: • 19 commits • 12 issues • 8 unit tests Major Responsibilities: • Data scraping • Data models Stats: • 13 commits • 9 issues • 10 unit tests Major Responsibilities: • API Doc • Unit Testing Stats: • 11 commits • 10 issues • 6 unit tests Major Responsibilities: • Frontend • Search Danyal Rizvi Austin Thomas Amanda Keebler Moiz Rizvi
4. Issue Tracking 47 of 47 issues closed!
5. The Goal We set out to create a startup database that presented only the most fundamental company data and provided simple and useful contextual information by relating: Startups: startup popularity, number of founders, primary market, and location Founders: founder popularity, number of startups, founder rank, and location Cities: city popularity, number of startups, number of founders, and number of investors
6. Intended Consumer Startups looking to get noticed Programmers looking for startups New grads looking to move
7. Technical Details
8. Data Source ◇ AngelList API ￭ Startups ￭ Users (employees of any kind) ￭ Tags (market or location) ◇ API Python Wrapper, https://github.com/bugra/angel-list ￭ MIT Licensed Python library to access API of Angellist In[1] angel.get_jobs() Out[1]: {u'jobs': [{u'angellist_url': u'https://angel.co/jobs?startup_id=357384', u'equity_cliff': u'1.0', u'equity_max': u'3.0', ... }
9. Scraping city_ids companies founders City Company Founder foreach foreach foreach GET /tags/:id/startups GET /startup_roles?v=1 GET /tags/:id Database Query DB If not in DB
10. UML Diagram
11. Database SQLALchemy Tables class Founder(Base): __tablename__ = 'founders' id = Column(Integer, primary_key=True) ... city_id = Column(Integer, ForeignKey('cities.id')) city = relationship("City", back_populates="founders") startups = relationship("Startup", secondary=startup_to_founder, back_populates="founders") Startup_to_founder Association table created to manage the many-to-many relationship between Startup and Founder startup_to_founder = Table("startup_to_founder", Base.metadata, Column("startup_id", Integer, ForeignKey("startups.id"), primary_key=True), Column("founder_id", Integer, ForeignKey("founders.id"), primary_key=True) )
12. API Endpoints ◇ Startups ￭ startups/ -- Return all startups ￭ startups/:id -- Return startups with id ◇ Founders ￭ founders/ -- Return all founders ￭ founders/:id -- Return founder with id ◇ Cities ￭ cities/ -- Return all cities ￭ cities/:id -- Return city with id
13. Tools Used
14. Demonstration
15. Critiques
16. Critique of CATastrophe’s IDB ❏ Neatly designed, informative and unique home page ❏ We learned that using postgres probably works best with python scripts than MySQL as it appears they used MySQL with IDB1 and IDB2 but postgres with IDB3 ❏ They could’ve done a better job of making their 3 pages of data complete [i.e. if they weren’t going to include certain information, to not have columns for them in their tables] ❏ We are puzzled why they didn’t include the number of a particular breed available to adopt on the “Breeds” page
17. Critique of Our IDB ❏ We created a clean, attractive and easy to use website without much front-end development experience on our team ❏ We learned a myriad of tools from this project but most of all, how to use Docker ❏ We could’ve had a slightly more informative site [for instance how data is listed on each data page] ❏ We were puzzled by the best choice of database to use with Docker
18. Questions?