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[DNM] Use @dataclass(slots=True) (needs Python >=3.14) #9254

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[DNM] Use @dataclass(slots=True) (needs Python >=3.14) #9254

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6 changes: 3 additions & 3 deletions distributed/shuffle/_core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -444,7 +444,7 @@ class ShuffleType(Enum):
ARRAY_RECHUNK = "ArrayRechunk"


@dataclass(frozen=True)
@dataclass(frozen=True, slots=True)
class ShuffleRunSpec(Generic[_T_partition_id]):
run_id: int = field(init=False, default_factory=partial(next, itertools.count(1)))
spec: ShuffleSpec
Expand All @@ -456,7 +456,7 @@ def id(self) -> ShuffleId:
return self.spec.id


@dataclass(frozen=True)
@dataclass(frozen=True, slots=True)
class ShuffleSpec(abc.ABC, Generic[_T_partition_id]):
id: ShuffleId
disk: bool
Expand Down Expand Up @@ -491,7 +491,7 @@ def create_run_on_worker(
"""Create the new shuffle run on the worker."""


@dataclass(eq=False)
@dataclass(eq=False, slots=True)
class SchedulerShuffleState(Generic[_T_partition_id]):
run_spec: ShuffleRunSpec
participating_workers: set[str]
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2 changes: 1 addition & 1 deletion distributed/shuffle/_rechunk.py
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Expand Up @@ -1107,7 +1107,7 @@ def _get_assigned_worker(self, id: NDIndex) -> str:
return self.worker_for[id]


@dataclass(frozen=True)
@dataclass(frozen=True, slots=True)
class ArrayRechunkSpec(ShuffleSpec[NDIndex]):
new: ChunkedAxes
old: ChunkedAxes
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2 changes: 1 addition & 1 deletion distributed/shuffle/_shuffle.py
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Expand Up @@ -327,7 +327,7 @@ def validate_data(self, data: pd.DataFrame) -> None:
raise ValueError(f"Expected {self.meta.columns=} to match {data.columns=}.")


@dataclass(frozen=True)
@dataclass(frozen=True, slots=True)
class DataFrameShuffleSpec(ShuffleSpec[int]):
npartitions: int
column: str
Expand Down
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