Merge branch 'main' into nanny_dont_kill_worker_restart

distributed/diagnostics/plugin.py

@@ -23,6 +23,7 @@
     # circular imports
     from distributed.scheduler import Scheduler
     from distributed.scheduler import TaskStateState as SchedulerTaskStateState
+    from distributed.scheduler import WorkerState
     from distributed.worker import Worker
     from distributed.worker_state_machine import TaskStateState as WorkerTaskStateState
 
@@ -205,6 +206,29 @@ def add_client(self, scheduler: Scheduler, client: str) -> None:
     def remove_client(self, scheduler: Scheduler, client: str) -> None:
         """Run when a client disconnects"""
 
+    def valid_workers_downscaling(
+        self, scheduler: Scheduler, workers: list[WorkerState]
+    ) -> list[WorkerState]:
+        """Determine which workers can be removed from the cluster
+
+        This method is called when the scheduler is about to downscale the cluster
+        by removing workers. The method should return a set of worker states that
+        can be removed from the cluster.
+
+        Parameters
+        ----------
+        workers : list
+            The list of worker states that are candidates for removal.
+        stimulus_id : str
+            ID of stimulus causing the downscaling.
+
+        Returns
+        -------
+        list
+            The list of worker states that can be removed from the cluster.
+        """
+        return workers
+
     def log_event(self, topic: str, msg: Any) -> None:
         """Run when an event is logged"""
 

distributed/scheduler.py

@@ -7151,6 +7151,9 @@ def workers_to_close(
         # running on, as it would cause them to restart from scratch
         # somewhere else.
         valid_workers = [ws for ws in self.workers.values() if not ws.long_running]
+        for plugin in list(self.plugins.values()):
+            valid_workers = plugin.valid_workers_downscaling(self, valid_workers)
+
         groups = groupby(key, valid_workers)
 
         limit_bytes = {k: sum(ws.memory_limit for ws in v) for k, v in groups.items()}

distributed/shuffle/_scheduler_plugin.py

@@ -410,6 +410,14 @@ def transition(
                     if not archived:
                         del self._archived_by_stimulus[shuffle._archived_by]
 
+    def valid_workers_downscaling(
+        self, scheduler: Scheduler, workers: list[WorkerState]
+    ) -> list[WorkerState]:
+        all_participating_workers = set()
+        for shuffle in self.active_shuffles.values():
+            all_participating_workers.update(shuffle.participating_workers)
+        return [w for w in workers if w.address not in all_participating_workers]
+
     def _fail_on_workers(self, shuffle: SchedulerShuffleState, message: str) -> None:
         worker_msgs = {
             worker: [

distributed/shuffle/tests/test_shuffle.py

@@ -18,6 +18,7 @@
 from packaging.version import parse as parse_version
 from tornado.ioloop import IOLoop
 
+import dask
 from dask.utils import key_split
 
 from distributed.shuffle._core import ShuffleId, ShuffleRun, barrier_key
@@ -28,7 +29,6 @@
 import numpy as np
 import pandas as pd
 
-import dask
 from dask.dataframe._compat import PANDAS_GE_150, PANDAS_GE_200
 from dask.typing import Key
 
@@ -139,9 +139,10 @@ async def test_minimal_version(c, s, a, b):
             dtypes={"x": float, "y": float},
             freq="10 s",
         )
-        with pytest.raises(
-            ModuleNotFoundError, match="requires pyarrow"
-        ), dask.config.set({"dataframe.shuffle.method": "p2p"}):
+        with (
+            pytest.raises(ModuleNotFoundError, match="requires pyarrow"),
+            dask.config.set({"dataframe.shuffle.method": "p2p"}),
+        ):
             await c.compute(df.shuffle("x"))
 
 
@@ -2795,3 +2796,43 @@ def data_gen():
         "meta",
     ):
         await c.gather(c.compute(ddf.shuffle(on="a")))
+
+
+@gen_cluster(client=True)
+async def test_dont_downscale_participating_workers(c, s, a, b):
+    df = dask.datasets.timeseries(
+        start="2000-01-01",
+        end="2000-01-10",
+        dtypes={"x": float, "y": float},
+        freq="10 s",
+    )
+    with dask.config.set({"dataframe.shuffle.method": "p2p"}):
+        shuffled = df.shuffle("x")
+
+    workers_to_close = s.workers_to_close(n=2)
+    assert len(workers_to_close) == 2
+    res = c.compute(shuffled)
+
+    shuffle_id = await wait_until_new_shuffle_is_initialized(s)
+    while not get_active_shuffle_runs(a):
+        await asyncio.sleep(0.01)
+    while not get_active_shuffle_runs(b):
+        await asyncio.sleep(0.01)
+
+    workers_to_close = s.workers_to_close(n=2)
+    assert len(workers_to_close) == 0
+
+    async with Worker(s.address) as w:
+        c.submit(lambda: None, workers=[w.address])
+
+        workers_to_close = s.workers_to_close(n=3)
+        assert len(workers_to_close) == 1
+
+    workers_to_close = s.workers_to_close(n=2)
+    assert len(workers_to_close) == 0
+
+    await c.gather(res)
+    del res
+
+    workers_to_close = s.workers_to_close(n=2)
+    assert len(workers_to_close) == 2

pyproject.toml

@@ -97,7 +97,7 @@ parentdir_prefix = "distributed-"
 
 [tool.pytest.ini_options]
 addopts = '''
--v -rsxfE
+-v -rsfE
 --durations=20
 --color=yes
 --ignore=continuous_integration