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diff --git a/reveal.js/dataporten.html b/reveal.js/dataporten.html
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- SQLAlchemy & Odin Data Access
-Fifty Data Science
- -Simeon Simeonov
-Agenda
- --
-
- SQLAlchemy - Design & overview -
- SQLAlchemy - A small practical example -
- Odin Data Access - Overview and examples -
- Q & A -
What is SQLAlchemy?
- -SQLAlchemy is a Python library created by Mike Bayer to provide a high-level Pythonic interface to RDBMS such as PostgreSQL, SQLite, MySQL, Oracle, DB2.
-SQLAlchemy includes RDBMS-independent SQL expression language and an object-relational mapper (ORM).
-Why use SQLAlchemy?
- --
-
portability - the programming interface is independent of the type of RDBMS and connector used
- security - no more SQL injections
- abstraction - no need to bother with complex JOINs
- object-orientation - you work with objects and NOT tables and rows
- performance - exploits the likehood of reusing a particular query
- flexibility - you can override almost anything
-
Basic architecture
-SQLAlchemy consists of several components, including the the ORM.
--
-
- Engine- manages the connection pool and the RDBMS-independent SQL dialect layer -
- MetaData - used to collect and organize information about your table layout (schema) -
- Session - establishes all conversations with the RDBMS and represents a "holding zone" for all the objects which you've loaded or associated with it during its lifespan -
- SQL expression language - provides an API to execute your queries and updates against your tables, all from Python, and all in a database-independent way (low-level interface) -
- ORM - provides a convenient way to add database persistence to your Python objects withoutrequiring you to design your objects around the database, or the database around the objects (high-level interface) -
- Example
-SQLAlchemy gives us the choice between classical mapping and the newer declarative mapping
-
-
- import sqlalchemy
- sqlalchemy.__version__
-
- from sqlalchemy import create_engine
-
- # engine = create_engine("postgresql+psycopg2://user:zipassword@localhost/mydb" , echo=True)
- # The string form of the URL is dialect+driver://user:password@host/dbname[?key=value..],
- # where dialect is a database name such as mysql, oracle, postgresql, etc.,
- # and driver the name of a DBAPI, such as psycopg2, pyodbc, cx_oracle
- # The echo flag is a shortcut to setting up SQLAlchemy logging,
- # which is accomplished via Python’s standard logging module.
- # With it enabled, we’ll see all the generated SQL produced.
- # engine = create_engine("sqlite:///library.db", echo=True)
- engine = create_engine("sqlite:///:memory:", echo=True)
-
- metadata = MetaData()
-
- from sqlalchemy import Table, Column, Integer, String, MetaData, ForeignKey
-
- authors_table = Table(
- "authors",
- metadata,
- Column("id", Integer, primary_key=True),
- Column("name", String),
- ) # Column("name", String(50)) is possible
-
- books_table = Table(
- "books",
- metadata,
- Column("id", Integer, primary_key=True),
- Column("title", String),
- Column("description", String),
- Column("author_id", ForeignKey('authors.id')),
- )
-
- metadata.create_all(engine) # creates the tables
-
-
- Example (cont...)
-Use of SQL expression language
-
-
- insert_stmt = authors_table.insert(bind=engine)
- type(insert_stmt)
- # Out: <class 'sqlalchemy.sql.expression.Insert'>
- print(insert_stmt)
- # Out: INSERT INTO authors (id, name) VALUES (:id,:name)
-
- compiled_stmt = insert_stmt.compile()
- print(compiled_stmt.params)
- # Out: {'id': None, 'name': None}
-
- insert_stmt.execute(name="Alexandre Dumas") # insert a single entry
- insert_stmt.execute([{"name": 'Mr X'},{'name': 'Mr Y'}]) # a list of entries
-
- metadata.bind = engine # no need to explicitly bind the engine from now on
- select_stmt = authors_table.select(authors_table.c.id==2)
- result = select_stmt.execute()
- result.fetchall()
- # Out: [(1, u'Mr X')]
-
- del_stmt = authors_table.delete()
- del_stmt.execute(whereclause=text("name='Mr Y'"))
- del_stmt.execute() # delete all
-
-
- Example (cont...)
-Use of classical mapping
-
-
- from sqlalchemy.orm import mapper
- from sqlalchemy.orm import relationship, backref
-
- class Author:
- def __init__(self, name):
- self.name = name
-
- def __str__(self):
- return self.name
-
-
- class Book:
- def __init__(self, title, description, author):
- self.title = title
- self.description = description
- self.author = author
-
- def __str__(self):
- return self.title
-
- mapper(Book, books_table)
- mapper(Author, authors_table, properties = {"books": relation(Book, backref="author")})
-
-
- Example (cont...)
-Doing the same thing the easy way with declarative mapping
-
-
- from sqlalchemy.ext.declarative import declarative_base
- from sqlalchemy.orm import relationship, backref
-
- Base = declarative_base()
-
- class Author(Base):
- __tablename__ = "authors"
-
- id = Column(Integer, primary_key=True)
- name = Column(String)
-
- def __init__(self, name):
- self.name = name
-
- def __str__(self):
- return self.name
-
-
- class Book(Base):
- __tablename__ = "books" # self.__table__ will be available for our objects
-
- id = Column(Integer, primary_key=True)
- title = Column(String)
- description = Column(String)
- author_id = Column(Integer, ForeignKey("authors.id"))
- author = relationship(Author, backref=backref("books", order_by=title))
-
- def __init__(self, title, description, author):
- self.title = title
- self.description = description
- self.author = author
-
- def __str__(self):
- return self.title
-
- Base.metadata.create_all(engine)# create tables
-
-
- Example (cont...)
-Creating instances
-
-
- from sqlalchemy.orm import sessionmaker
-
- Session = sessionmaker(bind=engine) # bound session
- session = Session()
-
- author_1 = Author("Richard Dawkins")
- author_2 = Author("Matt Ridley")
-
- book_1 = Book("The Red Queen", "A popular science book", author_2)
- book_2 = Book("The Selfish Gene", "A popular science book", author_1)
- book_3 = Book("The Blind Watchmaker", "The theory of evolutio", author_1) # typo
-
- session.add(author_1)
- session.add(author_2)
- session.add(book_1)
- session.add(book_2)
- session.add(book_3) # or simply session.add_all([author_1, author_2, book_1, book_2, book_3])
-
- # session.flush()
- session.commit() # flushes (issues the statements and sends them to the RDBMS) and commits
-
- book_3.description = "The theory of evolution" # update the object
- book_3 in session # check whether the object is in the session
- # Out: True
-
- session.commit()
-
-
- Example (cont...)
-Queries
-
-
- session.query(Book).order_by(Book.id) # returns a Query instance with a .statement attribute
- session.query(Book).order_by(Book.id).all() # returns an object-list
-
- # return all book objects where title == "The Selfish Gene"
- session.query(Book).filter(Book.title == "The Selfish Gene").order_by(Book.id).all()
-
- # using LIKE
- session.query(Book).filter(Book.title.like("The%")).order_by(Book.id).all()
-
- query = session.query(Book).filter(Book.id == 9).order_by(Book.id)
- query.count() # returns 0L
- query.all() # returns an empty list
- query.first() # returns None
- query.one() # raises NoResultFound exception
-
- query = session.query(Book).filter(Book.id == 1).order_by(Book.id)
- book_1 = query.one()
- book_1.description # returns "A popular science book"
- book_1.author.books # returns a list of Book-objects representing all the books from the same author.
-
- # get a list of all Book-instances where the author"s name is "Richard Dawkins"
- session.query(Book).filter(Book.author_id == Author.id).filter(Author.name == "Richard Dawkins").all()
- session.query(Book).join(Author).filter(Author.name == "Richard Dawkins").all()
- session.query(Book).\
- from_statement("SELECT b.* FROM books b, authors a WHERE b.author_id = a.id AND a.name=:name").\
- params(name="Richard Dawkins").all()
-
-
- ... simply amazing!
-Odin Data Access
-https://gitlab.fifty.eu/odin/data-science/odin-data-access
-Provides the odin_data_access module with some of the following entities:
--
-
- session module for creating Session objects in a Fifty environment via get_session and get_session_from_env -
- DBBase - a tiny base-class that can be used as a mixin for mapping -
- DBTableAccessor - tiny wrapper for Session and Table as well a "facade" for Query and pandas -
The DBTableAccessor:
--
-
- represents a single RDBMS-table that is introspected during instance initialization (__init__) -
- uses a mix of ORM and SQL expression language functionality -
- contins a mapper for mapping custom classes to its corresponding Table -
- used by Batman and Chuck Norris (source: incomplete Google search) -
Demo
-Now watch this well-rehearsed demo...
-Q & A
-
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diff --git a/reveal.js/demo.html b/reveal.js/demo.html
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- Dataporten & eValg 3.0
-Utviklerforum (31.08.2017)
--
- Simeon Simeonov -
-Agenda
--
-
-
Intro
- The big picture
- Flow
- Q & A
-
Introduction
-Evalg 3.0
--
-
Development started in 2017
- Dataporten / Feide (OIDC with discovery) ... perhaps others in the future?
- Python 3.6 / Flask / React / pyoic / pyJWT ...
- Using fnr, feide-id, names...
-
Challenges with Dataporten
--
-
React
- Flexibility (using other providers (OP))
- Testing (users, firewall(s))
-
The big picture
-
- Flow
-
- Challenges
--
-
Not distributing client secrets
- Checking state and verifying signatures
- Flexibility (using other providers (OP))
- Testing (users, firewall(s))
-
Q & A
-???
-“Judge a man by his questions rather than by his answers.” - Voltaire
-Pretty Code
-
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