tinsel

Your data IS your schema

This tiny library helps to overcome excessive complexity in hand-written pyspark dataframe schemas.

How?

Shape your data as NamedTuple or dataclasses - they can freely mix:

from dataclasses import dataclass
from tinsel import struct, transform
from typing import NamedTuple, Optional, Dict, List
@struct
@dataclass
class UserInfo:
hobby: List[str]
last_seen: Optional[int]
pet_ages: Dict[str, int]
@struct
class User(NamedTuple):
login: str
age: int
active: bool
info: Optional[UserInfo]

Transform root node (User in our case) into schema:

schema = transform(User)

Create some data, if necessary:

data = [
User(
login="Ben",
age=18,
active=False,
info=None
),
User(
login="Tom",
age=32,
active=True,
info=UserInfo(
hobby=["pets", "flowers"],
last_seen=16,
pet_ages={"Jack": 2, "Sunshine": 6}
)
)
]

And… voilà!:

from pyspark.sql import SparkSession
sc = SparkSession.builder.master('local').getOrCreate()
df = sc.createDataFrame(data=data, schema=schema)
df.printSchema()
df.show(truncate=False)

This will output:

root
|-- login: string (nullable = false)
|-- age: integer (nullable = false)
|-- active: boolean (nullable = false)
|-- info: struct (nullable = true)
| |-- hobby: array (nullable = false)
| | |-- element: string (containsNull = false)
| |-- last_seen: integer (nullable = true)
| |-- pet_ages: map (nullable = false)
| | |-- key: string
| | |-- value: integer (valueContainsNull = false)
+-----+---+------+----------------------------------------------+
|login|age|active|info |
+-----+---+------+----------------------------------------------+
|Ben |18 |false |null |
|Tom |32 |true |[[pets, flowers],, [Jack -> 2, Sunshine -> 6]]|
+-----+---+------+----------------------------------------------+

Features

• use native python types; no extra DSL, no cryptic API — just plain Python;
• small and fast;
• provide type shims for some types absent in Python, like long or short;
• nullable fields naturally fits into schema definition;

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.