planframe-sparkless 0.2.0

sparkless backend adapter for PlanFrame (SparkFrame UI + sparkless engine).

planframe-sparkless

Sparkless adapter package for PlanFrame. Import as planframe_sparkless.

This package:

• uses the PySpark-like UI from planframe.spark (SparkFrame)
• executes plans using the sparkless engine (no JVM)

Documentation (ReadTheDocs):

• Sparkless track (end users): https://planframe.readthedocs.io/en/latest/planframe_sparkless/

Install

pip install planframe-sparkless

Quickstart

from planframe.expr import add, col, lit
from planframe_sparkless import SparklessFrame


class User(SparklessFrame):
    id: int
    x: int


pf = User([{"id": 1, "x": 2}, {"id": 2, "x": 3}])

out = (
    pf.select("id", "x")
    .withColumn("x2", add(col("x"), lit(1)))
    .where(pf["x"] > lit(2))
    .select("id", "x2")
)

print(out.to_dicts())

Execution model (PlanFrame)

• PlanFrame is always lazy: chaining does not execute backend work.
• Materialization boundaries:
  • collect() returns list[pydantic.BaseModel]
  • collect_backend() returns the sparkless backend dataframe object
  • to_dicts() / to_dict() export rows/columns
  • Async: acollect() / ato_dicts() / ato_dict() (aliases: collect_async, to_dicts_async, to_dict_async)
• Core v1.2+: execute_plan_async, planframe.materialize, Expr == / & / … as IR—see Migrating since v1.1.0.

Notes / limitations

• This adapter aims to support a practical subset of Spark-like operations using sparkless.
• Row streaming: stream_dicts() currently materializes via to_dicts() (sparkless does not expose an efficient local iterator API yet).
• For backend-agnostic semantics and supported transforms, see the core docs: https://planframe.readthedocs.io/en/latest/planframe/