tinsel
PySpark schema generator
Decision gist · record as of 2026-08-14
No. The package is abandoned (last release 2018-09-01, no maintenance since) and targets Python 3.6–3.7. Compatibility with modern PySpark and Python versions is unverified. For new projects, consider modern alternatives or write schemas directly using PySpark's StructType API. Install only if you are maintaining legacy code already using tinsel and cannot migrate.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires pyspark to be installed and a SparkSession available; designed for Python 3.6–3.7 era, compatibility with modern Python versions unverified.
- Low install friction; depends only on pyspark.
- However, the package is abandoned—last release was 2018-09-01 and no commits or maintenance activity since.
License · maintenance · safety
MIT license (permissive) — MIT license (permissive); you may use, modify, and distribute freely with attribution.
last release 2018-09-01 (2904 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 186,783 downloads/mo, #9,976 on PyPI
Alternatives
Verify before relying
from dataclasses import dataclass
from tinsel import struct, transform
from typing import NamedTuple, Optional, List
@struct
@dataclass
class UserInfo:
hobby: List[str]
@struct
class User(NamedTuple):
login: str
age: int
info: Optional[UserInfo]
schema = transform(User)
df = spark.createDataFrame(data, schema=schema)- Whether tinsel works with modern PySpark versions (3.x+) and Python 3.8+
- Whether the package handles all complex nested types (e.g., deeply nested structs, union types) reliably
- Performance characteristics when dealing with very large or deeply nested schema definitions
What it is and what it does
tinsel is a lightweight PySpark schema generator that converts Python NamedTuple and dataclass definitions into PySpark StructType schemas. Instead of writing verbose, error-prone schema definitions by hand, you define your data structure as a standard Python class, decorate it with @struct, and call transform() to produce the corresponding Spark schema. It handles nested structures, optional fields, collections (lists, dicts), and type annotations natively.
The package was last released in September 2018 and is no longer maintained. It targets Python 3.6–3.7 and depends only on pyspark. While it solves a real problem—schema boilerplate reduction—its age and lack of maintenance mean it may not work reliably with modern PySpark or Python versions without testing.
Use it for
- Define PySpark DataFrame schemas using Python dataclasses or NamedTuples to avoid hand-written StructType boilerplate.
- Quickly prototype data pipelines where schema structure mirrors your application's domain objects.
- Generate schemas for nested, optional, and collection-heavy data structures with minimal code.
- Keep schema definitions in sync with Python type definitions during development.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No.
The package is abandoned (last release 2018-09-01, no maintenance since) and targets Python 3.6–3.7. Compatibility with modern PySpark and Python versions is unverified. For new projects, consider modern alternatives or write schemas directly using PySpark's StructType API. Install only if you are maintaining legacy code already using tinsel and cannot migrate.
Install
tinsel on PyPI
Before you install
Low install friction; depends only on pyspark. However, the package is abandoned—last release was 2018-09-01 and no commits or maintenance activity since. Use only if your PySpark and Python versions remain compatible with the 3.6–3.7 era.
Requires pyspark to be installed and a SparkSession available; designed for Python 3.6–3.7 era, compatibility with modern Python versions unverified.
License in practice
MIT license (permissive); you may use, modify, and distribute freely with attribution.
Quickstart
from dataclasses import dataclass
from tinsel import struct, transform
from typing import NamedTuple, Optional, List
@struct
@dataclass
class UserInfo:
hobby: List[str]
@struct
class User(NamedTuple):
login: str
age: int
info: Optional[UserInfo]
schema = transform(User)
df = spark.createDataFrame(data, schema=schema)Verify before relying
- Whether tinsel works with modern PySpark versions (3.x+) and Python 3.8+
- Whether the package handles all complex nested types (e.g., deeply nested structs, union types) reliably
- Performance characteristics when dealing with very large or deeply nested schema definitions
Package facts
| License | MIT license permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagepyspark |
| Maintenance | Abandoned 2,904 days since the last release |
| First released | |
| Downloads | 186,783 / month, #9,976 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishProgramming Language :: Python :: 3.6Programming Language :: Python :: 3.7 |
Evidence: tinsel-0.3.0-py2.py3-none-any.whl
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See also sparkdantic · sparkaid · quinn · dataclasses-avroschema · warchant_dc_schema · namedlist · desert · recordclass · pyspark-pandas · recordtype