Data Factory & DataFlows - Tutorial

Posted on 30 August 2018 by Adam Kariv

Data Factory is an open framework for building and running lightweight data processing workflows quickly and easily. We recommend reading this introductory blogpost to gain a better understanding of underlying Data Factory concepts before diving into the tutorial below.

Learn how to write your own processing flows

Let’s start with the traditional ‘hello, world’ example:

from dataflows import Flow

data = [
  {'data': 'Hello'},
  {'data': 'World'}
]

def lowerData(row):
	row['data'] = row['data'].lower()

f = Flow(
      data,
      lowerData
)
data, *_ = f.results()

print(data)

# -->
# [
#   [
#     {'data': 'hello'},
#     {'data': 'world'}
#   ]
# ]

This very simple flow takes a list of dicts and applies a row processing function on each one of them.

We can load data from a file instead:

from dataflows import Flow, load

# beatles.csv:
# name,instrument
# john,guitar
# paul,bass
# george,guitar
# ringo,drums

def titleName(row):
    row['name'] = row['name'].title()

f = Flow(
      load('beatles.csv'),
      titleName
)
data, *_ = f.results()

print(data)

# -->
# [
#   [
#     {'name': 'John', 'instrument': 'guitar'},
#     {'name': 'Paul', 'instrument': 'bass'},
#     {'name': 'George', 'instrument': 'guitar'},
#     {'name': 'Ringo', 'instrument': 'drums'}
#   ]
# ]

The source file can be a CSV file, an Excel file or a JSON file. You can use a local file name or a URL for a file hosted somewhere on the web.

Data sources can be generators and not just lists or files. Let’s take as an example a very simple scraper:

from dataflows import Flow

from xml.etree import ElementTree
from urllib.request import urlopen

# Get from Wikipedia the population count for each country
def country_population():
    # Read the Wikipedia page and parse it using etree
    page = urlopen('https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population').read()
    tree = ElementTree.fromstring(page)
    # Iterate on all tables, rows and cells
    for table in tree.findall('.//table'):
        if 'wikitable' in table.attrib.get('class', ''):
            for row in table.findall('tr'):
                cells = row.findall('td')
                if len(cells) > 3:
                    # If a matching row is found...
                    name = cells[1].find('.//a').attrib.get('title')
                    population = cells[2].text
                    # ... yield a row with the information
                    yield dict(
                        name=name,
                        population=population
                    )

f = Flow(
      country_population(),
)
data, *_ = f.results()

print(data)
# --->
# [
#   [
#     {'name': 'China', 'population': '1,391,090,000'},
#     {'name': 'India', 'population': '1,332,140,000'},
#     {'name': 'United States', 'population': '327,187,000'},
#     {'name': 'Indonesia', 'population': '261,890,900'},
#     ...
#   ]
# ]

This is nice, but we do prefer the numbers to be actual numbers and not strings.

In order to do that, let’s simply define their type to be numeric:

from dataflows import Flow, set_type

def country_population():
    # same as before
	...

f = Flow(
	country_population(),
    set_type('population', type='number', groupChar=',')
)
data, *_ = f.results()

print(data)
# -->
# [
#   [
#     {'name': 'China', 'population': Decimal('1391090000')},
#     {'name': 'India', 'population': Decimal('1332140000')},
#     {'name': 'United States', 'population': Decimal('327187000')},
#     {'name': 'Indonesia', 'population': Decimal('261890900')},
#     ...
#   ]
# ]

Data is automatically converted to the correct native Python type.

Apart from data-types, it’s also possible to set other constraints to the data. If the data fails validation (or does not fit the assigned data-type) an exception will be thrown - making this method highly effective for validating data and ensuring data quality.

What about large data files? In the above examples, the results are loaded into memory, which is not always preferable or acceptable. In many cases, we’d like to store the results directly onto a hard drive - without having the machine’s RAM limit in any way the amount of data we can process.

We do it by using dump processors:

from dataflows import Flow, set_type, dump_to_path

def country_population():
    # same as before
	...

f = Flow(
	country_population(),
    set_type('population', type='number', groupChar=','),
    dump_to_path('country_population')
)
*_ = f.process()

Running this code will create a local directory called county_population, containing two files:

├── country_population
│   ├── datapackage.json
│   └── res_1.csv

The CSV file - res_1.csv - is where the data is stored. The datapackage.json file is a metadata file, holding information about the data, including its schema.

We can now open the CSV file with any spreadsheet program or code library supporting the CSV format - or using one of the data package libraries out there, like so:

from datapackage import Package
pkg = Package('country_population/res_1.csv')
it = pkg.resources[0].iter(keyed=True)
print(next(it))
# prints:
# {'name': 'China', 'population': Decimal('1391110000')}

Note how using the data package meta-data, data-types are restored and there’s no need to ‘re-parse’ the data. This also works with other types too, such as dates, booleans and even lists and dicts.

So far we’ve seen how to load data, process it row by row, and then inspect the results or store them in a data package.

Let’s see how we can do more complex processing by manipulating the entire data stream:

from dataflows import Flow, set_type, dump_to_path

# Generate all triplets (a,b,c) so that 1 <= a <= b < c <= 20
def all_triplets():
    for a in range(1, 20):
        for b in range(a, 20):
            for c in range(b+1, 21):
                yield dict(a=a, b=b, c=c)

# Yield row only if a^2 + b^2 == c^1
def filter_pythagorean_triplets(rows):
    for row in rows:
        if row['a']**2 + row['b']**2 == row['c']**2:
            yield row

f = Flow(
    all_triplets(),
    set_type('a', type='integer'),
    set_type('b', type='integer'),
    set_type('c', type='integer'),
    filter_pythagorean_triplets,
    dump_to_path('pythagorean_triplets')
)
_ = f.process()

# -->
# pythagorean_triplets/res_1.csv contains:
# a,b,c
# 3,4,5
# 5,12,13
# 6,8,10
# 8,15,17
# 9,12,15
# 12,16,20

The filter_pythagorean_triplets function takes an iterator of rows, and yields only the ones that pass its condition.

The flow framework knows whether a function is meant to handle a single row or a row iterator based on its parameters:

if it accepts a single row parameter, then it’s a row processor.
if it accepts a single rows parameter, then it’s a rows processor.
if it accepts a single package parameter, then it’s a package processor.

Let’s see a few examples of what we can do with a package processors.

First, let’s add a field to the data:

from dataflows import Flow, load, dump_to_path


def add_is_guitarist_column_to_schema(package):
	# Add a new field to the first resource
    package.pkg.resources[0]
               .descriptor['schema']['fields']
               .append(dict(
            name='is_guitarist',
            type='boolean'
    ))
    # Must yield the modified datapackage
    yield package.pkg
    # And its resources
    yield from package

def add_is_guitarist_column(row):
	row['is_guitarist'] = row['instrument'] == 'guitar'
    return row

f = Flow(
    # Same one as above
    load('beatles.csv'),
    add_is_guitarist_column_to_schema,
    add_is_guitarist_column,
    dump_to_path('beatles_guitarists')
)
_ = f.process()

In this example we create two steps - one for adding the new field (is_guitarist) to the schema and another step to modify the actual data.

We can combine the two into one step:

from dataflows import Flow, load, dump_to_path


def add_is_guitarist_column(package):

    # Add a new field to the first resource
    package.pkg.resources[0].descriptor['schema']['fields'].append(dict(
        name='is_guitarist',
        type='boolean'
    ))
    # Must yield the modified datapackage
    yield package.pkg

    # Now iterate on all resources
    resources = iter(package)
    # Take the first resource
    beatles = next(resources)

    # And yield it with with the modification
    def f(row):
        row['is_guitarist'] = row['instrument'] == 'guitar'
        return row

    yield map(f, beatles)

f = Flow(
    # Same one as above
    load('beatles.csv'),
    add_is_guitarist_column,
    dump_to_path('beatles_guitarists')
)
_ = f.process()

The contract for the package processing function is simple:

First modify package.pkg (which is a Package instance) and yield it.

Then, yield any resources that should exist on the output, with or without modifications.

In the next example we’re removing an entire resource in a package processor - this next one filters the list of Academy Award nominees to those who won both the Oscar and an Emmy award:

    from dataflows import Flow, load, dump_to_path

    def find_double_winners(package):

        # Remove the emmies resource -
        #    we're going to consume it now
        package.pkg.remove_resource('emmies')
        # Must yield the modified datapackage
        yield package.pkg

        # Now iterate on all resources
        resources = iter(package)

        # Emmies is the first -
        # read all its data and create a set of winner names
        emmy = next(resources)
        emmy_winners = set(
            map(lambda x: x['nominee'],
                filter(lambda x: x['winner'],
                       emmy))
        )

        # Oscars are next -
        # filter rows based on the emmy winner set
        academy = next(resources)
        yield filter(lambda row: (row['Winner'] and
                                  row['Name'] in emmy_winners),
                     academy)

    f = Flow(
        # Emmy award nominees and winners
        load('emmy.csv', name='emmies'),
        # Academy award nominees and winners
        load('academy.csv', encoding='utf8', name='oscars'),
        find_double_winners,
        dump_to_path('double_winners')
    )
    _ = f.process()

# -->
# double_winners/academy.csv contains:
# 1931/1932,5,Actress,1,Helen Hayes,The Sin of Madelon Claudet
# 1932/1933,6,Actress,1,Katharine Hepburn,Morning Glory
# 1935,8,Actress,1,Bette Davis,Dangerous
# 1938,11,Actress,1,Bette Davis,Jezebel
# ...

Builtin Processors

DataFlows comes with a few built-in processors which do most of the heavy lifting in many common scenarios, leaving you to implement only the minimum code that is specific to your specific problem.

A complete list, which also includes an API reference for each one of them, can be found in the DataFlows Built-in Processors page.