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Dask Bags

Dask Bag implements operations like map, filter, groupby and aggregations on collections of Python objects. It does this in parallel and in small memory using Python iterators. It is similar to a parallel version of itertools or a Pythonic version of the PySpark RDD.

Dask Bags are often used to do simple preprocessing on log files, JSON records, or other user defined Python objects.

Full API documentation is available here: http://docs.dask.org/en/latest/bag-api.html

Start Dask Client for Dashboard

Starting the Dask Client is optional. It will provide a dashboard which is useful to gain insight on the computation.

The link to the dashboard will become visible when you create the client below. We recommend having it open on one side of your screen while using your notebook on the other side. This can take some effort to arrange your windows, but seeing them both at the same is very useful when learning.

[1]:
from dask.distributed import Client, progress
client = Client(n_workers=4, threads_per_worker=1)
client
[1]:

Client

Cluster

  • Workers: 4
  • Cores: 4
  • Memory: 8.36 GB

Create Random Data

We create a random set of record data and store it to disk as many JSON files. This will serve as our data for this notebook.

[2]:
import dask
import json
import os

os.makedirs('data', exist_ok=True)              # Create data/ directory

b = dask.datasets.make_people()                 # Make records of people
b.map(json.dumps).to_textfiles('data/*.json')   # Encode as JSON, write to disk
[2]:
['/home/travis/build/dask/dask-examples/data/0.json',
 '/home/travis/build/dask/dask-examples/data/1.json',
 '/home/travis/build/dask/dask-examples/data/2.json',
 '/home/travis/build/dask/dask-examples/data/3.json',
 '/home/travis/build/dask/dask-examples/data/4.json',
 '/home/travis/build/dask/dask-examples/data/5.json',
 '/home/travis/build/dask/dask-examples/data/6.json',
 '/home/travis/build/dask/dask-examples/data/7.json',
 '/home/travis/build/dask/dask-examples/data/8.json',
 '/home/travis/build/dask/dask-examples/data/9.json']

Read JSON data

Now that we have some JSON data in a file lets take a look at it with Dask Bag and Python JSON module.

[3]:
!head -n 2 data/0.json
{"age": 50, "name": ["Reena", "Rodriguez"], "occupation": "Site Agent", "telephone": "1-829-648-3617", "address": {"address": "1109 Gould Sideline", "city": "Terrell"}, "credit-card": {"number": "3467 024339 73355", "expiration-date": "08/20"}}
{"age": 63, "name": ["Karyl", "Sutton"], "occupation": "Radio Director", "telephone": "(702) 658-6214", "address": {"address": "911 Berkshire Field", "city": "Walnut Creek"}, "credit-card": {"number": "2397 1463 1900 3495", "expiration-date": "07/25"}}
[4]:
import dask.bag as db
import json

b = db.read_text('data/*.json').map(json.loads)
b
[4]:
dask.bag<loads, npartitions=10>
[5]:
b.take(2)
[5]:
({'age': 50,
  'name': ['Reena', 'Rodriguez'],
  'occupation': 'Site Agent',
  'telephone': '1-829-648-3617',
  'address': {'address': '1109 Gould Sideline', 'city': 'Terrell'},
  'credit-card': {'number': '3467 024339 73355', 'expiration-date': '08/20'}},
 {'age': 63,
  'name': ['Karyl', 'Sutton'],
  'occupation': 'Radio Director',
  'telephone': '(702) 658-6214',
  'address': {'address': '911 Berkshire Field', 'city': 'Walnut Creek'},
  'credit-card': {'number': '2397 1463 1900 3495',
   'expiration-date': '07/25'}})

Map, Filter, Aggregate

We can process this data by filtering out only certain records of interest, mapping functions over it to process our data, and aggregating those results to a total value.

[6]:
b.filter(lambda record: record['age'] > 30).take(2)  # Select only people over 30
[6]:
({'age': 50,
  'name': ['Reena', 'Rodriguez'],
  'occupation': 'Site Agent',
  'telephone': '1-829-648-3617',
  'address': {'address': '1109 Gould Sideline', 'city': 'Terrell'},
  'credit-card': {'number': '3467 024339 73355', 'expiration-date': '08/20'}},
 {'age': 63,
  'name': ['Karyl', 'Sutton'],
  'occupation': 'Radio Director',
  'telephone': '(702) 658-6214',
  'address': {'address': '911 Berkshire Field', 'city': 'Walnut Creek'},
  'credit-card': {'number': '2397 1463 1900 3495',
   'expiration-date': '07/25'}})
[7]:
b.map(lambda record: record['occupation']).take(2)  # Select the occupation field
[7]:
('Site Agent', 'Radio Director')
[8]:
b.count().compute()  # Count total number of records
[8]:
10000

Chain computations

It is common to do many of these steps in one pipeline, only calling compute or take at the end.

[9]:
result = (b.filter(lambda record: record['age'] > 30)
           .map(lambda record: record['occupation'])
           .frequencies(sort=True)
           .topk(10, key=1))
result
[9]:
dask.bag<topk-aggregate, npartitions=1>

As with all lazy Dask collections, we need to call compute to actually evaluate our result. The take method used in earlier examples is also like compute and will also trigger computation.

[10]:
result.compute()
[10]:
[('Audit Clerk', 16),
 ('Instrument Maker', 14),
 ('Plastics Consultant', 14),
 ('Shipyard Worker', 14),
 ('Administration Clerk', 14),
 ('Clerk', 13),
 ('Mineralologist', 13),
 ('Shift Controller', 13),
 ('Roof Tiler', 13),
 ('Machine Operator', 13)]

Transform and Store

Sometimes we want to compute aggregations as above, but sometimes we want to store results to disk for future analyses. For that we can use methods like to_textfiles and json.dumps, or we can convert to Dask Dataframes and use their storage systems, which we’ll see more of in the next section.

[11]:
(b.filter(lambda record: record['age'] > 30)  # Select records of interest
  .map(json.dumps)                            # Convert Python objects to text
  .to_textfiles('data/processed.*.json'))     # Write to local disk
[11]:
['/home/travis/build/dask/dask-examples/data/processed.0.json',
 '/home/travis/build/dask/dask-examples/data/processed.1.json',
 '/home/travis/build/dask/dask-examples/data/processed.2.json',
 '/home/travis/build/dask/dask-examples/data/processed.3.json',
 '/home/travis/build/dask/dask-examples/data/processed.4.json',
 '/home/travis/build/dask/dask-examples/data/processed.5.json',
 '/home/travis/build/dask/dask-examples/data/processed.6.json',
 '/home/travis/build/dask/dask-examples/data/processed.7.json',
 '/home/travis/build/dask/dask-examples/data/processed.8.json',
 '/home/travis/build/dask/dask-examples/data/processed.9.json']

Convert to Dask Dataframes

Dask Bags are good for reading in initial data, doing a bit of pre-processing, and then handing off to some other more efficient form like Dask Dataframes. Dask Dataframes use Pandas internally, and so can be much faster on numeric data and also have more complex algorithms.

However, Dask Dataframes also expect data that is organized as flat columns. It does not support nested JSON data very well (Bag is better for this).

Here we make a function to flatten down our nested data structure, map that across our records, and then convert that to a Dask Dataframe.

[12]:
b.take(1)
[12]:
({'age': 50,
  'name': ['Reena', 'Rodriguez'],
  'occupation': 'Site Agent',
  'telephone': '1-829-648-3617',
  'address': {'address': '1109 Gould Sideline', 'city': 'Terrell'},
  'credit-card': {'number': '3467 024339 73355', 'expiration-date': '08/20'}},)
[13]:
def flatten(record):
    return {
        'age': record['age'],
        'occupation': record['occupation'],
        'telephone': record['telephone'],
        'credit-card-number': record['credit-card']['number'],
        'credit-card-expiration': record['credit-card']['expiration-date'],
        'name': ' '.join(record['name']),
        'street-address': record['address']['address'],
        'city': record['address']['city']
    }

b.map(flatten).take(1)
[13]:
({'age': 50,
  'occupation': 'Site Agent',
  'telephone': '1-829-648-3617',
  'credit-card-number': '3467 024339 73355',
  'credit-card-expiration': '08/20',
  'name': 'Reena Rodriguez',
  'street-address': '1109 Gould Sideline',
  'city': 'Terrell'},)
[14]:
df = b.map(flatten).to_dataframe()
df.head()
[14]:
age occupation telephone credit-card-number credit-card-expiration name street-address city
0 50 Site Agent 1-829-648-3617 3467 024339 73355 08/20 Reena Rodriguez 1109 Gould Sideline Terrell
1 63 Radio Director (702) 658-6214 2397 1463 1900 3495 07/25 Karyl Sutton 911 Berkshire Field Walnut Creek
2 28 Stage Director 233.192.9838 4906 4832 6371 7576 04/20 Claude Santiago 582 Gould Gate Portage
3 50 Yacht Master (219) 387-2979 4533 3041 9152 8706 03/24 Armand Castaneda 932 Harriet Gate Kenmore
4 59 Grave Digger 1-959-176-1685 3795 412775 74402 04/20 Vanda Key 916 Louisiana Gardens Huntsville

We can now perform the same computation as before, but now using Pandas and Dask dataframe.

[15]:
df[df.age > 30].occupation.value_counts().nlargest(10).compute()
[15]:
Audit Clerk             16
Instrument Maker        14
Plastics Consultant     14
Shipyard Worker         14
Administration Clerk    14
Shift Controller        13
Roof Tiler              13
Flower Arranger         13
Mineralologist          13
Machine Operator        13
Name: occupation, dtype: int64

Learn More

You may be interested in the following links: