Introduction to SQLAlchemy - Pycon 2013

The goal of the tutorial is a broad-based introduction to SQLAlchemy, spanning the Core and ORM components as well as the systems that underlie them. The tutorial takes the "harder" approach teaching from fundamentals first, emphasizing details on how the library interacts with the database and SQL and concluding with an introduction to the ORM, providing a solid perspective for continued study.

Transcript

1. Object Relational Mapping SQL Expressions Table Metadata, Reflection, DDL Engine, Connection, Transactions Introduction to SQLAlchemy and ORMs @zzzeek Mike Bayer Pycon US 2013
2. Updates! • Break  is  at  2:50  PM • On-­‐Site  Volunteering  opportunities!     • SWAG  bagging  -­‐  Thursday,  4-­‐8  PM • Registration  Desk  help • http://bit.ly/pycon-­‐volunteering-­‐status • http://bit.ly/pycon2013-­‐volunteer
3. git clone https://bitbucket.org/zzzeek/ pycon2013_student_package.git Prerequisites virtualenv --no-site-packages .venv .venv/bin/python install.py cd pycon2013_student_package/ .venv/bin/sliderepl 01_engine_usage.py
4. Relational Database Review • Any  questions  on  the  handout? • We've  covered: • Tables,  columns • data  definition  language  (DDL) • INSERT,  UPDATE,  DELETE  (DML) • SELECT,  joins,  grouping • Overview  of  ACID
5. SQLAlchemy - Overview • the  Database  Toolkit  for  Python • introduced  2005 • end-­‐to-­‐end  system  for  working  with  the  Python   DBAPI,  relational  databases,  and  the  SQL  language • Current  release  0.8.0 • 1.0  will  happen
6. SQLAlchemy Goals • Provide  helpers,  tools  and  components  to  assist  with   database  development  at  every  level • Provide  a  consistent  and  fully  featured  facade  over   the  Python  DBAPI • Provide  an  industrial  strength,  but  optional,  object   relational  mapper  (ORM) • Act  as  the  foundation  for  any  number  of  third  party  or   in-­‐house  tools
7. SQLAlchemy Philosophies • Bring  the  usage  of  different  databases  and  adapters   to  an  interface  as  consistent  as  possible... • ...but  still  expose  distinct  behaviors  and  features  of   each  backend. • Never  "hide"  the  database  or  its  concepts  -­‐   developers  must  know  /  continue  to  think  in  SQL... • Instead....provide  automation  and  DRY • Allow  expression  of  DB/SQL  tasks  using  declarative   patterns
8. SQLAlchemy  consists  of  the  Core  and  the  ORM SQLAlchemy Overview Third party libraries / Python core SQLAlchemy Core SQLAlchemy ORM SQL Expression Language Dialect Connection Pooling DBAPI Schema / Types Engine Object Relational Mapper (ORM) Database
9. SQLAlchemy - Core • Engine  -­‐  a  registry  which  provides  connectivity  to  a   particular  database  server. • Dialect  -­‐  interprets  generic  SQL  and  database   commands  in  terms  of  a  specific  DBAPI  and  database   backend. • Connection  Pool  -­‐  holds  a  collection  of  database   connections  in  memory  for  fast  re-­‐use. • SQL  Expression  Language  -­‐  Allows  SQL  statements   to  be  written  using  Python  expressions • Schema  /  Types  -­‐  Uses  Python  objects  to  represent   tables,  columns,  and  datatypes.
10. SQLAlchemy - ORM • Allows  construction  of  Python  objects  which  can  be   mapped  to  relational  database  tables. • Transparently  persists  objects  into  their   corresponding  database  tables  using  the  unit  of  work   pattern. • Provides  a  query  system  which  loads  objects  and   attributes  using  SQL  generated  from  mappings. • Builds  on  top  of  the  Core  -­‐  uses  the  Core  to  generate   SQL  and  talk  to  the  database.
11. Can  be  learned  from  the  inside  out,  or  outside  in SQLAlchemy is like an Onion Object Relational Mapping SQL Expressions Table Metadata, Reflection, DDL Engine, Connection, Transactions
12. Object Relational Mapping SQL Expressions Table Metadata, Reflection, DDL Engine, Connection, Transactions Level 1, Engine, Connection, Transactions
13. The Python DBAPI • DBAPI  -­‐  PEP-­‐0249,  Python  Database  API • The  de-­‐facto  system  for  providing  Python  database   interfaces • There  are  many  DBAPI  implementations  available,   most  databases  have  more  than  one • Features/performance/stability/API  quirks/ maintenance  vary  wildly
14. DBAPI - Nutshell import psycopg2 connection = psycopg2.connect("scott", "tiger", "test") cursor = connection.cursor() cursor.execute( "select emp_id, emp_name from employee " "where emp_id=%(emp_id)s", {'emp_id':5}) emp_name = cursor.fetchone()[1] cursor.close() cursor = connection.cursor() cursor.execute( "insert into employee_of_month " "(emp_name) values (%(emp_name)s)", {"emp_name":emp_name}) cursor.close() connection.commit()
15. Important DBAPI Facts • DBAPI  assumes  that  a  transaction  is  always  in   progress.    There  is  no  begin()  method,  only  commit()   and  rollback(). • DBAPI  encourages  bound  parameters,  via  the   execute()  and  executemany()  methods.    But  has  six   different  formats. • All  DBAPIs  have  inconsistencies  regarding  datatypes,   primary  key  generation,  custom  database  features,   result/cursor  behavior • DBAPI  has  it's  own  exception  hierarchy,  which   SQLAlchemy  exposes  directly
16. SQLAlchemy and the DBAPI • The  first  layer  in  SQLAlchemy  is  known  as  the   Engine,  which  is  the  object  that  maintains  the   classical  DBAPI  interaction.
17. .venv/bin/sliderepl 01_engine.py Engine - Usage
18. Engine Facts • Executing  via  the  Engine  directly  is  called   connectionless  execution  -­‐  the  Engine  connects  and   disconnects  for  us. • Using  a  Connection  is  called  explicit  execution.       We  control  the  span  of  a  connection  in  use. • Engine  usually  uses  a  connection  pool,  which  means   "disconnecting"  often  means  the  connection  is  just   returned  to  the  pool. • The  SQL  we  send  to  engine.execute()  as  a  string  is   not  modified,  is  consumed  by  the  DBAPI  verbatim.
19. Object Relational Mapping SQL Expressions Table Metadata, Reflection, DDL Engine, Connection, Transactions Level 2, Table Metadata, Reflection, DDL
20. What is "Metadata"? • Popularized  by  Martin  Fowler,  Patterns  of  Enterprise   Architecture • Describes  the  structure  of  the  database,  i.e.  tables,   columns,  constraints,  in  terms  of  data  structures  in   Python • Serves  as  the  basis  for  SQL  generation  and  object   relational  mapping • Can  generate  to  a  schema • Can  be  generated  from  a  schema
21. .venv/bin/sliderepl 02_metadata.py MetaData and Table
22. Some Basic Types • Integer()  -­‐  basic  integer  type,  generates  INT • String()  -­‐  ASCII  strings,  generates  VARCHAR • Unicode()  -­‐  Unicode  strings  -­‐  generates  VARCHAR,   NVARCHAR  depending  on  database • Boolean()  -­‐  generates  BOOLEAN,  INT,  TINYINT • DateTime()  -­‐  generates  DATETIME  or   TIMESTAMP,  returns  Python  datetime()  objects • Float()  -­‐  floating  point  values • Numeric()  -­‐  precision  numerics  using  Python   Decimal()
23. CREATE and DROP • metadata.create_all(engine, checkfirst=<True|False>) emits  CREATE   statements  for  all  tables. • table.create(engine, checkfirst=<True|False>)  emits  CREATE  for   a  single  table. • metadata.drop_all(engine, checkfirst=<True|False>)  emits  DROP   statements  for  all  tables. • table.drop(engine, checkfirst=<True| False>)  emits  DROP  for  a  single  table.
24. Object Relational Mapping SQL Expressions Table Metadata, Reflection, DDL Engine, Connection, Transactions Level 3, SQL Expressions
25. SQL Expressions • The  SQL  Expression  system  builds  upon  Table   Metadata  in  order  to  compose  SQL  statements  in   Python. • We  will  build  Python  objects  that  represent  individual   SQL  strings  (statements)  we'd  send  to  the  database. • These  objects  are  composed  of  other  objects  that   each  represent  some  unit  of  SQL,  like  a  comparison,  a   SELECT  statement,  a  conjunction  such  as  AND  or  OR. • We  work  with  these  objects  in  Python,  which  are  then   converted  to  strings  when  we  "execute"  them  (as  well   as  if  we  print  them).
26. .venv/bin/sliderepl 03_sql_expressions.py SQL Expressions
27. Object Relational Mapping SQL Expressions Table Metadata, Reflection, DDL Engine, Connection, Transactions Level 4, Object Relational Mapping
28. Object Relational Mapping • Object  Relational  Mapping,  or  ORM,  is  the  process  of   associating  object  oriented  classes  with  database   tables. • We  refer  to  the  set  of  object  oriented  classes  as  a   domain  model.
29. The  most  basic  task  is  to  translate  between  a  domain   object  and  a  table  row. What does an ORM Do? Database Application Domain Object Table Row object.save() class.load()
30. Some  ORMs  can  also  represent  arbitrary  rows  as  domain   objects  within  the  application,  that  is,  rows  derived  from   SELECT  statements  or  views. What does an ORM Do? Database Application Domain Object SELECT statement row Table 1 row Table 2 row
31. Most  ORMs  also  represent  basic  compositions,  primarily   one-­‐to-­‐many  and  many-­‐to-­‐one,  using  foreign  key   associations. What does an ORM Do? Application Database Parent Object Table 1 Row Table 2 Row Child Object Child Object Table 2 Row fk reference fk reference many to one collection many to one one to many
32. What does an ORM Do? • Other  things  ORMs  do: • provide  a  means  of  querying  the  database  in  terms  of   the  domain  model  structure • Some  can  represent  class  inheritance  hierarchies   using  a  variety  of  schemes • Some  can  handle  "sharding"  of  data,  i.e.  storing  a   domain  model  across  multiple  schemas  or  databases • Provide  various  patterns  for  concurrency,  including   row  versioning • Provide  patterns  for  data  validation  and  coercion
33. The  two  general  styles  of  ORM  are  Active  Record  and   Data  Mapper.    Active  Record  has  domain  objects  handle   their  own  persistence: Flavors of ORM user_record = User(name="ed", fullname="Ed Jones") user_record.save() user_record = User.query(name='ed').fetch() user_record.fullname = "Edward Jones" user_record.save()
34. The  Data  Mapper  approach  tries  to  keep  the  details  of   persistence  separate  from  the  object  being  persisted. Flavors of ORM dbsession = start_session() user_record = User(name="ed", fullname="Ed Jones") dbsession.add(user_record) user_record = dbsession.query(User).filter(name='ed').first() user_record.fullname = "Edward Jones" dbsession.commit()
35. ORMs  may  also  provide  different  configurational  patterns.   Most  use  an  "all-­‐at-­‐once",  or  declarative  style  where  class   and  table  information  is  together. Flavors of ORM # a hypothetical declarative system class User(ORMObject): tablename = 'user' name = String(length=50) fullname = String(length=100) class Address(ORMObject): tablename = 'address' email_address = String(length=100) user = many_to_one("User")
36. A  less  common  style  keeps  the  declaration  of  domain   model  and  table  metadata  separate. Flavors of ORM # class is declared without any awareness of database class User(object): def __init__(self, name, username): self.name = name self.username = username # elsewhere, it's associated with a database table mapper( User, Table("user", metadata, Column("name", String(50)), Column("fullname", String(100)) ) )
37. SQLAlchemy ORM • The  SQLAlchemy  ORM  is  essentially  a  data  mapper   style  ORM. • Modern  versions  use  declarative  configuration;  the   "domain  and  schema  separate"  configuration  model   is  present  underneath  this  layer. • The  ORM  builds  upon  SQLAlchemy  Core,  and  many   of  the  SQL  Expression  concepts  are  present  when   working  with  the  ORM  as  well.   • In  contrast  to  the  SQL  Expression  language,  which   presents  a  schema-­‐centric  view  of  data,  it  presents  a   domain-­‐model  centric  view  of  data.
38. Key ORM Patterns • Unit  of  Work  -­‐  objects  are  maintained  by  a  system  that   tracks  changes  over  the  course  of  a  transaction,  and  flushes   pending  changes  periodically,  in  a  transparent  or  semi-­‐ transparent  manner • Identity  Map  -­‐  objects  are  tracked  by  their  primary  key   within  the  unit  of  work,  and  are  kept  unique  on  that  primary   key  identity. • Lazy  Loading  -­‐  Some  attributes  of  an  object  may  emit   additional  SQL  queries  when  they  are  accessed. • Eager  Loading  -­‐  Multiple  tables  are  queried  at  once  in  order   to  load  related  objects  and  collections. • Method  Chaining  -­‐  queries  are  composed  using  a  string  of   method  calls  which  each  return  a  new  query  object.
39. .venv/bin/sliderepl 04_orm.py ORM Walkthrough
40. Object Relational Mapping SQL Expressions Table Metadata, Reflection, DDL Engine, Connection, Transactions Thanks ! http://www.sqlalchemy.org @zzzeek