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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: 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]:
['data/0.json',
 'data/1.json',
 'data/2.json',
 'data/3.json',
 'data/4.json',
 'data/5.json',
 'data/6.json',
 'data/7.json',
 'data/8.json',
 '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": 41, "name": ["Sol", "Thompson"], "occupation": "Records Supervisor", "telephone": "184.459.9042", "address": {"address": "805 Pelton Townline", "city": "Lake Charles"}, "credit-card": {"number": "4602 6867 3210 7701", "expiration-date": "09/20"}}
{"age": 63, "name": ["Myles", "Perkins"], "occupation": "Hypnotherapist", "telephone": "+1-(606)-949-0826", "address": {"address": "1016 Flower Freeway", "city": "Caldwell"}, "credit-card": {"number": "3407 146562 58915", "expiration-date": "08/20"}}
[4]:
import dask.bag as db
import json

b = db.read_text('data/*.json').map(json.loads)
b
[4]:
dask.bag<loads-6..., npartitions=10>
[5]:
b.take(2)
[5]:
({'age': 41,
  'name': ['Sol', 'Thompson'],
  'occupation': 'Records Supervisor',
  'telephone': '184.459.9042',
  'address': {'address': '805 Pelton Townline', 'city': 'Lake Charles'},
  'credit-card': {'number': '4602 6867 3210 7701',
   'expiration-date': '09/20'}},
 {'age': 63,
  'name': ['Myles', 'Perkins'],
  'occupation': 'Hypnotherapist',
  'telephone': '+1-(606)-949-0826',
  'address': {'address': '1016 Flower Freeway', 'city': 'Caldwell'},
  'credit-card': {'number': '3407 146562 58915', 'expiration-date': '08/20'}})

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': 41,
  'name': ['Sol', 'Thompson'],
  'occupation': 'Records Supervisor',
  'telephone': '184.459.9042',
  'address': {'address': '805 Pelton Townline', 'city': 'Lake Charles'},
  'credit-card': {'number': '4602 6867 3210 7701',
   'expiration-date': '09/20'}},
 {'age': 63,
  'name': ['Myles', 'Perkins'],
  'occupation': 'Hypnotherapist',
  'telephone': '+1-(606)-949-0826',
  'address': {'address': '1016 Flower Freeway', 'city': 'Caldwell'},
  'credit-card': {'number': '3407 146562 58915', 'expiration-date': '08/20'}})
[7]:
b.map(lambda record: record['occupation']).take(2)  # Select the occupation field
[7]:
('Records Supervisor', 'Hypnotherapist')
[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-ag..., 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]:
[('Aerobic Instructor', 17),
 ('Laundry Staff', 14),
 ('Bakery Manager', 14),
 ('Marine Geologist', 14),
 ('Payroll Manager', 14),
 ('Youth Worker', 13),
 ('Literary Agent', 13),
 ('Technician', 13),
 ('Sound Artist', 13),
 ('Physician', 12)]

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]:
['data/processed.0.json',
 'data/processed.1.json',
 'data/processed.2.json',
 'data/processed.3.json',
 'data/processed.4.json',
 'data/processed.5.json',
 'data/processed.6.json',
 'data/processed.7.json',
 'data/processed.8.json',
 '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': 41,
  'name': ['Sol', 'Thompson'],
  'occupation': 'Records Supervisor',
  'telephone': '184.459.9042',
  'address': {'address': '805 Pelton Townline', 'city': 'Lake Charles'},
  'credit-card': {'number': '4602 6867 3210 7701',
   'expiration-date': '09/20'}},)
[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': 41,
  'occupation': 'Records Supervisor',
  'telephone': '184.459.9042',
  'credit-card-number': '4602 6867 3210 7701',
  'credit-card-expiration': '09/20',
  'name': 'Sol Thompson',
  'street-address': '805 Pelton Townline',
  'city': 'Lake Charles'},)
[14]:
df = b.map(flatten).to_dataframe()
df.head()
[14]:
age city credit-card-expiration credit-card-number name occupation street-address telephone
0 41 Lake Charles 09/20 4602 6867 3210 7701 Sol Thompson Records Supervisor 805 Pelton Townline 184.459.9042
1 63 Caldwell 08/20 3407 146562 58915 Myles Perkins Hypnotherapist 1016 Flower Freeway +1-(606)-949-0826
2 58 Alameda 03/23 4633 0072 4497 0572 Branden Puckett Payroll Clerk 85 Second Terrace 1-840-714-3753
3 43 Ashtabula 02/25 4051 7227 5695 6297 Delena Richards Physician 1055 Lexington Avenue 1-311-021-0530
4 36 Goodyear 05/17 4356 2532 0301 1488 Dimple Ayers Architect 1344 San Lorenzo Close 1-749-684-5079

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]:
Aerobic Instructor    17
Marine Geologist      14
Laundry Staff         14
Payroll Manager       14
Bakery Manager        14
Literary Agent        13
Youth Worker          13
Technician            13
Sound Artist          13
Airport Manager       12
Name: occupation, dtype: int64

Learn More

You may be interested in the following links: