Reporting and analysis systems rely on coherent and reliable data, often from disparate sources. To that end, a series of well established data warehousing practices have emerged to extract data and produce a consistent data store. This talk will look at some options for composing workflows using Python. In particular, we'll explore beyond Celery's asynchronous task processing functionality into its workflow (aka Canvas) system and how it can be used in conjunction with SQLAlchemy's architecture to provide the building blocks for data stream processing.

1. Building data flows with Celery
and SQLAlchemy
PyCon Australia 2013
Roger Barnes
@mindsocket
roger@mindsocket.com.au
http://slideshare.net/mindsocket

2. Coming up
● Data warehousing
– AKA data integration
● Processing data flows
– SQLAlchemy
– Celery
● Tying it all together

3. About me
● 15 years doing all things software
● 11 years at a Business Intelligence vendor
● Currently contracting
– This talk based on a real reporting system

4. Why Data Warehousing?

5. Why Data Warehousing?
But we need reports that are
● Timely
● Unambiguous
● Accurate
● Complete
● … and don't impact production systems

6. What is a Data Warehouse
"… central repository of data
which is created by integrating
data from one or more
disparate sources" - Wikipedia

9. Extract, Transform, Load
Source: www.imc.com

10. Python can help!
● Rapid prototyping
● Code re-use
● Existing libraries
● Decouple
– data flow management
– data processing
– business logic

11. Existing solutions
● Not a lot available in the Python space
● People roll their own
● Bubbles (Brewery 2)
– Framework for Python 3
– "Focus on the process, not the data technology"

12. Ways to move data around
● Flat files
● NOSQL data stores
● RDBMS

13. SQLAlchemy is...
Python SQL toolkit
&
Object Relational Mapper

14. About SQLAlchemy
● Full featured
● Mature, robust, documented, maintained
● Flexible

15. Enterprise!

16. DB support
● SQLite
● Postgresql
● MySQL
● Oracle
● MS-SQL
● Firebird
● Sybase
● ...

17. Python support
cPython 2.5+
cPython 3+
Jython 2.5+
Pypy 1.5+

18. Structure

19. SQLAlchemy Core
● Abstraction over Python's DBAPI
● SQL language via generative Python
expressions

20. SQLAlchemy Core
● Good for DB performance
– bulk operations
– complex queries
– fine-tuning
– connection/tx management

21. Create a table
from sqlalchemy import *
engine = create_engine('sqlite:///:memory:')
metadata = MetaData()
vehicles_table = Table('vehicles', metadata,
Column('model', String),
Column('registration', String),
Column('odometer', Integer),
Column('last_service', Date),)
vehicles_table.create(bind=engine)

22. Insert data
values = [
{'model': 'Ford Festiva',
'registration': 'HAX00R',
'odometer': 3141 },
{'model': 'Lotus Elise',
'registration': 'DELEG8',
'odometer': 31415 },
]
rows = engine.execute(
vehicles_table.insert(),
list(values)).rowcount

23. Query data
query = select(
[vehicles_table]
).where(
vehicles_table.c.odometer < 100
)
results = engine.execute(query)
for row in results:
print row

24. Encapsulating a unit of work

25. Example Processor Types
● Extract
– Extract from CSV
– Extract from DB table
– Scrape web page
● Transform
– Copy table from extract layer
– Derive column
– Join tables

26. Abstract Processor
class BaseProcessor(object):
def dispatch(self):
return self._run()
def _run(self):
return self.run()
def run(self):
raise NotImplementedError

27. Abstract Database Processor
class DatabaseProcessor(BaseProcessor):
db_class = None
engine = None
metadata = None
@contextlib.contextmanager
def _with_session(self):
with self.db_class().get_engine() as engine:
self.engine = engine
self.metadata = MetaData(bind=engine)
yield
def _run(self):
with self._with_session():
return self.run()

28. CSV Extract Mixin
class CSVExtractMixin(object):
input_file = None
def _run(self):
with self._with_engine():
self.reader = csv.DictReader(
self.input_file
)
return self.run()

29. A Concrete Extract
class SalesHistoryExtract(CSVExtractMixin,
DatabaseProcessor):
target_table_name = 'SalesHistoryExtract'
input_file = SALES_FILENAME
def run(self):
target_table = Table(self.target_table_name,
self.metadata)
columns = self.reader.next()
[target_table.append_column(Column(column, ...))
for column in columns if column]
target_table.create()
insert = target_table.insert()
new_record_count = self.engine.execute(insert,
list(self.reader)).rowcount
return new_record_count

30. An Abstract Derive Transform
class AbstractDeriveTransform(DatabaseProcessor):
table_name = None
key_columns = None
select_columns = None
target_columns = None
def process_row(self, row):
raise NotImplementedError
...
# Profit!

31. A Concrete Transform
from business_logic import derive_foo
class DeriveFooTransform(AbstractDeriveTransform):
table_name = 'SalesTransform'
key_columns = ['txn_id']
select_columns = ['location', 'username']
target_columns = [Column('foo', FOO_TYPE)]
def process_row(self, row):
foo = derive_foo(row.location, row.username)
return {'foo': foo}

32. Introducing Celery
Distributed Task Queue

33. A Processor Task
class AbstractProcessorTask(celery.Task):
abstract = True
processor_class = None
def run(self, *args, **kwargs):
processor = self.processor_class(
*args, **kwargs)
return processor.dispatch()
class DeriveFooTask(AbstractProcessorTask):
processor_class = DeriveFooTransform
DeriveFooTask().apply_async() # Run it!

34. Canvas: Designing Workflows
● Combines a series of tasks
● Groups run in parallel
● Chains run in series
● Can be combined in different ways
>>> new_user_workflow = (create_user.s() | group(
... import_contacts.s(),
... send_welcome_email.s()))
... new_user_workflow.delay(username='artv',
... first='Art',
... last='Vandelay',
... email='art@vandelay.com')

35. Sample Data Processing Flow
Extrac
t sales
Extract
customers
Extract
product
s
Copy sales to
transform
Copy customers
to transform
Copy products
to transform
Join
table
s
Aggregate sales
by customer
Normalis
e
currency
Aggregate
sales by region
Customer data
exception report

36. Sample Data Processing Flow
extract_flow = group((
ExtractSalesTask().si(),
ExtractCustTask().si(),
ExtractProductTask().si()))
transform_flow = group((
CopySalesTask().si() | NormaliseCurrencyTask().si(),
CopyCustTask().si(),
CopyProductTask().si())) | JoinTask().si()
load_flow = group((
QualityTask().si(),
AggregateTask().si('cust_id'),
AggregateTask().si('region_id')))
all_flow = extract_flow | transform_flow | load_flow

37. Monitoring – celery events

38. Monitoring – celery flower

39. Turning it up to 11
● A requires/depends structure
● Incremental data loads
● Parameterised flows
● Tracking flow history
● Hooking into other libraries
– NLTK
– SciPy/NumPy
– ...

40. Summary
● Intro to data warehousing
● Process data with SQLAlchemy
● Task dependencies with Celery
canvas

41. Resources
● SQLAlchemy core: http://bit.ly/10FdYZo
● Celery Canvas: http://bit.ly/MOjazT
● http://databrewery.org
– Bubbles: http://bit.ly/14hNsV0
– Pipeline: http://bit.ly/15RXvWa
● http://schoolofdata.org

42. Thank You!
Questions?
http://slideshare.net/mindsocket