Restructure P2P code (#8098)

distributed/shuffle/_core.py

@@ -0,0 +1,277 @@
+from __future__ import annotations
+
+import abc
+import asyncio
+import contextlib
+import itertools
+import time
+from collections import defaultdict
+from collections.abc import Callable, Iterator
+from concurrent.futures import ThreadPoolExecutor
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import TYPE_CHECKING, Any, ClassVar, Generic, NewType, TypeVar
+
+from distributed.core import PooledRPCCall
+from distributed.exceptions import Reschedule
+from distributed.protocol import to_serialize
+from distributed.shuffle._comms import CommShardsBuffer
+from distributed.shuffle._disk import DiskShardsBuffer
+from distributed.shuffle._exceptions import ShuffleClosedError
+from distributed.shuffle._limiter import ResourceLimiter
+
+if TYPE_CHECKING:
+    import pandas as pd
+    from typing_extensions import TypeAlias
+
+    # avoid circular dependencies
+    from distributed.shuffle._worker_plugin import ShuffleWorkerPlugin
+
+_T_partition_id = TypeVar("_T_partition_id")
+_T_partition_type = TypeVar("_T_partition_type")
+_T = TypeVar("_T")
+
+NDIndex: TypeAlias = tuple[int, ...]
+
+ShuffleId = NewType("ShuffleId", str)
+
+
+class ShuffleRun(Generic[_T_partition_id, _T_partition_type]):
+    def __init__(
+        self,
+        id: ShuffleId,
+        run_id: int,
+        output_workers: set[str],
+        local_address: str,
+        directory: str,
+        executor: ThreadPoolExecutor,
+        rpc: Callable[[str], PooledRPCCall],
+        scheduler: PooledRPCCall,
+        memory_limiter_disk: ResourceLimiter,
+        memory_limiter_comms: ResourceLimiter,
+    ):
+        self.id = id
+        self.run_id = run_id
+        self.output_workers = output_workers
+        self.local_address = local_address
+        self.executor = executor
+        self.rpc = rpc
+        self.scheduler = scheduler
+        self.closed = False
+
+        self._disk_buffer = DiskShardsBuffer(
+            directory=directory,
+            memory_limiter=memory_limiter_disk,
+        )
+
+        self._comm_buffer = CommShardsBuffer(
+            send=self.send, memory_limiter=memory_limiter_comms
+        )
+        # TODO: reduce number of connections to number of workers
+        # MultiComm.max_connections = min(10, n_workers)
+
+        self.diagnostics: dict[str, float] = defaultdict(float)
+        self.transferred = False
+        self.received: set[_T_partition_id] = set()
+        self.total_recvd = 0
+        self.start_time = time.time()
+        self._exception: Exception | None = None
+        self._closed_event = asyncio.Event()
+
+    def __repr__(self) -> str:
+        return f"<{self.__class__.__name__}: id={self.id!r}, run_id={self.run_id!r}, local_address={self.local_address!r}, closed={self.closed!r}, transferred={self.transferred!r}>"
+
+    def __str__(self) -> str:
+        return f"{self.__class__.__name__}<{self.id}[{self.run_id}]> on {self.local_address}"
+
+    def __hash__(self) -> int:
+        return self.run_id
+
+    @contextlib.contextmanager
+    def time(self, name: str) -> Iterator[None]:
+        start = time.time()
+        yield
+        stop = time.time()
+        self.diagnostics[name] += stop - start
+
+    async def barrier(self) -> None:
+        self.raise_if_closed()
+        # TODO: Consider broadcast pinging once when the shuffle starts to warm
+        # up the comm pool on scheduler side
+        await self.scheduler.shuffle_barrier(id=self.id, run_id=self.run_id)
+
+    async def send(
+        self, address: str, shards: list[tuple[_T_partition_id, bytes]]
+    ) -> None:
+        self.raise_if_closed()
+        return await self.rpc(address).shuffle_receive(
+            data=to_serialize(shards),
+            shuffle_id=self.id,
+            run_id=self.run_id,
+        )
+
+    async def offload(self, func: Callable[..., _T], *args: Any) -> _T:
+        self.raise_if_closed()
+        with self.time("cpu"):
+            return await asyncio.get_running_loop().run_in_executor(
+                self.executor,
+                func,
+                *args,
+            )
+
+    def heartbeat(self) -> dict[str, Any]:
+        comm_heartbeat = self._comm_buffer.heartbeat()
+        comm_heartbeat["read"] = self.total_recvd
+        return {
+            "disk": self._disk_buffer.heartbeat(),
+            "comm": comm_heartbeat,
+            "diagnostics": self.diagnostics,
+            "start": self.start_time,
+        }
+
+    async def _write_to_comm(
+        self, data: dict[str, tuple[_T_partition_id, bytes]]
+    ) -> None:
+        self.raise_if_closed()
+        await self._comm_buffer.write(data)
+
+    async def _write_to_disk(self, data: dict[NDIndex, bytes]) -> None:
+        self.raise_if_closed()
+        await self._disk_buffer.write(
+            {"_".join(str(i) for i in k): v for k, v in data.items()}
+        )
+
+    def raise_if_closed(self) -> None:
+        if self.closed:
+            if self._exception:
+                raise self._exception
+            raise ShuffleClosedError(f"{self} has already been closed")
+
+    async def inputs_done(self) -> None:
+        self.raise_if_closed()
+        self.transferred = True
+        await self._flush_comm()
+        try:
+            self._comm_buffer.raise_on_exception()
+        except Exception as e:
+            self._exception = e
+            raise
+
+    async def _flush_comm(self) -> None:
+        self.raise_if_closed()
+        await self._comm_buffer.flush()
+
+    async def flush_receive(self) -> None:
+        self.raise_if_closed()
+        await self._disk_buffer.flush()
+
+    async def close(self) -> None:
+        if self.closed:  # pragma: no cover
+            await self._closed_event.wait()
+            return
+
+        self.closed = True
+        await self._comm_buffer.close()
+        await self._disk_buffer.close()
+        self._closed_event.set()
+
+    def fail(self, exception: Exception) -> None:
+        if not self.closed:
+            self._exception = exception
+
+    def _read_from_disk(self, id: NDIndex) -> bytes:
+        self.raise_if_closed()
+        data: bytes = self._disk_buffer.read("_".join(str(i) for i in id))
+        return data
+
+    async def receive(self, data: list[tuple[_T_partition_id, bytes]]) -> None:
+        await self._receive(data)
+
+    async def _ensure_output_worker(self, i: _T_partition_id, key: str) -> None:
+        assigned_worker = self._get_assigned_worker(i)
+
+        if assigned_worker != self.local_address:
+            result = await self.scheduler.shuffle_restrict_task(
+                id=self.id, run_id=self.run_id, key=key, worker=assigned_worker
+            )
+            if result["status"] == "error":
+                raise RuntimeError(result["message"])
+            assert result["status"] == "OK"
+            raise Reschedule()
+
+    @abc.abstractmethod
+    def _get_assigned_worker(self, i: _T_partition_id) -> str:
+        """Get the address of the worker assigned to the output partition"""
+
+    @abc.abstractmethod
+    async def _receive(self, data: list[tuple[_T_partition_id, bytes]]) -> None:
+        """Receive shards belonging to output partitions of this shuffle run"""
+
+    @abc.abstractmethod
+    async def add_partition(
+        self, data: _T_partition_type, partition_id: _T_partition_id
+    ) -> int:
+        """Add an input partition to the shuffle run"""
+
+    @abc.abstractmethod
+    async def get_output_partition(
+        self, partition_id: _T_partition_id, key: str, meta: pd.DataFrame | None = None
+    ) -> _T_partition_type:
+        """Get an output partition to the shuffle run"""
+
+
+def get_worker_plugin() -> ShuffleWorkerPlugin:
+    from distributed import get_worker
+
+    try:
+        worker = get_worker()
+    except ValueError as e:
+        raise RuntimeError(
+            "`shuffle='p2p'` requires Dask's distributed scheduler. This task is not running on a Worker; "
+            "please confirm that you've created a distributed Client and are submitting this computation through it."
+        ) from e
+    plugin: ShuffleWorkerPlugin | None = worker.plugins.get("shuffle")  # type: ignore
+    if plugin is None:
+        raise RuntimeError(
+            f"The worker {worker.address} does not have a ShuffleExtension. "
+            "Is pandas installed on the worker?"
+        )
+    return plugin
+
+
+_BARRIER_PREFIX = "shuffle-barrier-"
+
+
+def barrier_key(shuffle_id: ShuffleId) -> str:
+    return _BARRIER_PREFIX + shuffle_id
+
+
+def id_from_key(key: str) -> ShuffleId:
+    assert key.startswith(_BARRIER_PREFIX)
+    return ShuffleId(key.replace(_BARRIER_PREFIX, ""))
+
+
+class ShuffleType(Enum):
+    DATAFRAME = "DataFrameShuffle"
+    ARRAY_RECHUNK = "ArrayRechunk"
+
+
+@dataclass(eq=False)
+class ShuffleState(abc.ABC):
+    _run_id_iterator: ClassVar[itertools.count] = itertools.count(1)
+
+    id: ShuffleId
+    run_id: int
+    output_workers: set[str]
+    participating_workers: set[str]
+    _archived_by: str | None = field(default=None, init=False)
+
+    @abc.abstractmethod
+    def to_msg(self) -> dict[str, Any]:
+        """Transform the shuffle state into a JSON-serializable message"""
+
+    def __str__(self) -> str:
+        return f"{self.__class__.__name__}<{self.id}[{self.run_id}]>"
+
+    def __hash__(self) -> int:
+        return hash(self.run_id)

distributed/shuffle/_merge.py

@@ -9,13 +9,8 @@
 from dask.highlevelgraph import HighLevelGraph
 from dask.layers import Layer
 
-from distributed.shuffle._shuffle import (
-    ShuffleId,
-    _get_worker_plugin,
-    barrier_key,
-    shuffle_barrier,
-    shuffle_transfer,
-)
+from distributed.shuffle._core import ShuffleId, barrier_key, get_worker_plugin
+from distributed.shuffle._shuffle import shuffle_barrier, shuffle_transfer
 
 if TYPE_CHECKING:
     import pandas as pd
@@ -167,7 +162,7 @@ def merge_unpack(
 ):
     from dask.dataframe.multi import merge_chunk
 
-    ext = _get_worker_plugin()
+    ext = get_worker_plugin()
     # If the partition is empty, it doesn't contain the hash column name
     left = ext.get_output_partition(
         shuffle_id_left, barrier_left, output_partition, meta=meta_left