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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

Scheduler: tcp://127.0.0.1:46735
Dashboard: http://127.0.0.1:8787/status

Cluster

Workers: 4
Cores: 4
Memory: 7.84 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": 28, "name": ["Jesus", "Pittman"], "occupation": "Pet Minder", "telephone": "175-589-0589", "address": {"address": "386 States Mews", "city": "Sherwood"}, "credit-card": {"number": "5467 5401 5191 9156", "expiration-date": "02/25"}}
{"age": 30, "name": ["Lanora", "Fernandez"], "occupation": "Advertising Contractor", "telephone": "921.844.7314", "address": {"address": "670 Iowa Alley", "city": "Camarillo"}, "credit-card": {"number": "3708 304408 53408", "expiration-date": "02/22"}}

[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': 28,
  'name': ['Jesus', 'Pittman'],
  'occupation': 'Pet Minder',
  'telephone': '175-589-0589',
  'address': {'address': '386 States Mews', 'city': 'Sherwood'},
  'credit-card': {'number': '5467 5401 5191 9156',
   'expiration-date': '02/25'}},
 {'age': 30,
  'name': ['Lanora', 'Fernandez'],
  'occupation': 'Advertising Contractor',
  'telephone': '921.844.7314',
  'address': {'address': '670 Iowa Alley', 'city': 'Camarillo'},
  'credit-card': {'number': '3708 304408 53408', 'expiration-date': '02/22'}})

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': 34,
  'name': ['Johnson', 'Mcintosh'],
  'occupation': 'Plant Operator',
  'telephone': '+1-(659)-499-8160',
  'address': {'address': '668 Catalina Circle', 'city': 'Ozark'},
  'credit-card': {'number': '4323 8626 2486 4950',
   'expiration-date': '09/24'}},
 {'age': 35,
  'name': ['Aleen', 'Franks'],
  'occupation': 'Health Advisor',
  'telephone': '1-125-989-3945',
  'address': {'address': '192 Martha Concession road', 'city': 'Pontiac'},
  'credit-card': {'number': '3766 259608 43043', 'expiration-date': '03/19'}})

[7]:

b.map(lambda record: record['occupation']).take(2)  # Select the occupation field

[7]:

('Pet Minder', 'Advertising Contractor')

[8]:

b.count().compute()  # Count total number of records

[8]:

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]:

[('Plant Operator', 14),
 ('Optical Advisor', 14),
 ('Sales Director', 14),
 ('Sports Coach', 13),
 ('Pilot', 13),
 ('Word Processing Operator', 13),
 ('Reprographic Assistant', 13),
 ('Toll Collector', 13),
 ('Upholsterer', 13),
 ('Advertising Staff', 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': 28,
  'name': ['Jesus', 'Pittman'],
  'occupation': 'Pet Minder',
  'telephone': '175-589-0589',
  'address': {'address': '386 States Mews', 'city': 'Sherwood'},
  'credit-card': {'number': '5467 5401 5191 9156',
   'expiration-date': '02/25'}},)

[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': 28,
  'occupation': 'Pet Minder',
  'telephone': '175-589-0589',
  'credit-card-number': '5467 5401 5191 9156',
  'credit-card-expiration': '02/25',
  'name': 'Jesus Pittman',
  'street-address': '386 States Mews',
  'city': 'Sherwood'},)

[14]:

df = b.map(flatten).to_dataframe()
df.head()

[14]:

	age	occupation	telephone	credit-card-number	credit-card-expiration	name	street-address	city
0	28	Pet Minder	175-589-0589	5467 5401 5191 9156	02/25	Jesus Pittman	386 States Mews	Sherwood
1	30	Advertising Contractor	921.844.7314	3708 304408 53408	02/22	Lanora Fernandez	670 Iowa Alley	Camarillo
2	34	Plant Operator	+1-(659)-499-8160	4323 8626 2486 4950	09/24	Johnson Mcintosh	668 Catalina Circle	Ozark
3	35	Health Advisor	1-125-989-3945	3766 259608 43043	03/19	Aleen Franks	192 Martha Concession road	Pontiac
4	52	Publisher	524-525-4349	5474 9782 5887 4893	04/19	Jacquetta Nolan	1147 Lucy Arcade	North Port

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]:

Sales Director              14
Optical Advisor             14
Plant Operator              14
Toll Collector              13
Pilot                       13
Upholsterer                 13
Sports Coach                13
Word Processing Operator    13
Advertising Staff           13
Reprographic Assistant      13
Name: occupation, dtype: int64

Learn More¶

You may be interested in the following links:

Dask Bag Documentation
API Documentation
dask tutorial, notebook 02, for a more in-depth introduction.