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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.
In [1]:
from dask.distributed import Client, progress
client = Client(n_workers=4, threads_per_worker=1)
client
Out[1]:
Client
|
Cluster
|
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.
In [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
Out[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.
In [3]:
!head data/0.json -n 2
{"age": 36, "name": ["Boris", "May"], "occupation": "Travel Courier", "telephone": "(967) 473-1076", "address": {"address": "659 Minna Boulevard", "city": "Ontario"}, "credit-card": {"number": "4358 0881 1653 6165", "expiration-date": "02/18"}}
{"age": 29, "name": ["Shawana", "Ramos"], "occupation": "Milklady", "telephone": "+1-(446)-472-1888", "address": {"address": "1081 Clearview Close", "city": "Macomb"}, "credit-card": {"number": "3441 390740 87769", "expiration-date": "10/22"}}
In [4]:
import dask.bag as db
import json
b = db.read_text('data/*.json').map(json.loads)
b
Out[4]:
dask.bag<map-loa..., npartitions=10>
In [5]:
b.take(2)
Out[5]:
({'age': 36,
'name': ['Boris', 'May'],
'occupation': 'Travel Courier',
'telephone': '(967) 473-1076',
'address': {'address': '659 Minna Boulevard', 'city': 'Ontario'},
'credit-card': {'number': '4358 0881 1653 6165',
'expiration-date': '02/18'}},
{'age': 29,
'name': ['Shawana', 'Ramos'],
'occupation': 'Milklady',
'telephone': '+1-(446)-472-1888',
'address': {'address': '1081 Clearview Close', 'city': 'Macomb'},
'credit-card': {'number': '3441 390740 87769', 'expiration-date': '10/22'}})
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.
In [6]:
b.filter(lambda record: record['age'] > 30).take(2) # Select only people over 30
Out[6]:
({'age': 36,
'name': ['Boris', 'May'],
'occupation': 'Travel Courier',
'telephone': '(967) 473-1076',
'address': {'address': '659 Minna Boulevard', 'city': 'Ontario'},
'credit-card': {'number': '4358 0881 1653 6165',
'expiration-date': '02/18'}},
{'age': 43,
'name': ['Daryl', 'Blevins'],
'occupation': 'Housing Assistant',
'telephone': '(684) 161-0146',
'address': {'address': '1383 Mirabel Highway', 'city': 'Lenoir'},
'credit-card': {'number': '5245 9269 7219 2314',
'expiration-date': '10/22'}})
In [7]:
b.map(lambda record: record['occupation']).take(2) # Select the occupation field
Out[7]:
('Travel Courier', 'Milklady')
In [8]:
b.count().compute() # Count total number of records
Out[8]:
10000
Chain computations¶
It is common to do many of these steps in one pipeline, only calling
compute or take at the end.
In [9]:
result = (b.filter(lambda record: record['age'] > 30)
.map(lambda record: record['occupation'])
.frequencies(sort=True)
.topk(10, key=1))
result
Out[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.
In [10]:
result.compute()
Out[10]:
[('Magistrates Clerk', 16),
('Furniture Remover', 16),
('Riding Instructor', 15),
('Surgeon', 14),
('Meat Wholesaler', 14),
('Welder', 14),
('Care Assistant', 14),
('Health Nurse', 14),
('Tutor', 13),
('Tree Feller', 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.
In [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
Out[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.
In [12]:
b.take(1)
Out[12]:
({'age': 36,
'name': ['Boris', 'May'],
'occupation': 'Travel Courier',
'telephone': '(967) 473-1076',
'address': {'address': '659 Minna Boulevard', 'city': 'Ontario'},
'credit-card': {'number': '4358 0881 1653 6165',
'expiration-date': '02/18'}},)
In [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)
Out[13]:
({'age': 36,
'occupation': 'Travel Courier',
'telephone': '(967) 473-1076',
'credit-card-number': '4358 0881 1653 6165',
'credit-card-expiration': '02/18',
'name': 'Boris May',
'street-address': '659 Minna Boulevard',
'city': 'Ontario'},)
In [14]:
df = b.map(flatten).to_dataframe()
df.head()
Out[14]:
| age | city | credit-card-expiration | credit-card-number | name | occupation | street-address | telephone | |
|---|---|---|---|---|---|---|---|---|
| 0 | 36 | Ontario | 02/18 | 4358 0881 1653 6165 | Boris May | Travel Courier | 659 Minna Boulevard | (967) 473-1076 |
| 1 | 29 | Macomb | 10/22 | 3441 390740 87769 | Shawana Ramos | Milklady | 1081 Clearview Close | +1-(446)-472-1888 |
| 2 | 43 | Lenoir | 10/22 | 5245 9269 7219 2314 | Daryl Blevins | Housing Assistant | 1383 Mirabel Highway | (684) 161-0146 |
| 3 | 42 | Bowie | 01/22 | 4418 2528 1924 7862 | Nelia Craft | Beautician | 1384 Constanso Promenade | 906-597-6730 |
| 4 | 30 | Upper Arlington | 10/19 | 2246 1119 9192 6614 | Kristofer Rojas | Cellarman | 335 Zoo Close | 859.122.0468 |
We can now perform the same computation as before, but now using Pandas and Dask dataframe.
In [15]:
df[df.age > 30].occupation.value_counts().nlargest(10).compute()
Out[15]:
Magistrates Clerk 16
Furniture Remover 16
Riding Instructor 15
Health Nurse 14
Care Assistant 14
Meat Wholesaler 14
Surgeon 14
Welder 14
Tunneller 13
Tutor 13
Name: occupation, dtype: int64