Enable queuing by default

.github/workflows/tests.yaml

@@ -24,7 +24,7 @@ jobs:
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
         python-version: ["3.8", "3.9", "3.10"]
-        queuing: [no_queue]
+        queuing: [queue]
         # Cherry-pick test modules to split the overall runtime roughly in half
         partition: [ci1, not ci1]
         exclude:
@@ -33,11 +33,11 @@ jobs:
         include:
           - os: ubuntu-latest
             python-version: 3.9
-            queuing: queue
+            queuing: no_queue
             partition: "ci1"
           - os: ubuntu-latest
             python-version: 3.9
-            queuing: queue
+            queuing: no_queue
             partition: "not ci1"
 
         # Uncomment to stress-test the test suite for random failures.
@@ -144,8 +144,8 @@ jobs:
 
       - name: Set up dask env for job queuing
         shell: bash -l {0}
-        if: ${{ matrix.queuing == 'queue' }}
-        run: echo "DASK_DISTRIBUTED__SCHEDULER__WORKER_SATURATION=1.0" >> $GITHUB_ENV
+        if: ${{ matrix.queuing == 'no_queue' }}
+        run: echo "DASK_DISTRIBUTED__SCHEDULER__WORKER_SATURATION=inf" >> $GITHUB_ENV
 
       - name: Print host info
         shell: bash -l {0}

distributed/distributed.yaml

@@ -22,7 +22,7 @@ distributed:
     events-log-length: 100000
     work-stealing: True     # workers should steal tasks from each other
     work-stealing-interval: 100ms  # Callback time for work stealing
-    worker-saturation: .inf # Send this fraction of nthreads root tasks to workers
+    worker-saturation: 1.1 # Send this fraction of nthreads root tasks to workers
     worker-ttl: "5 minutes" # like '60s'. Time to live for workers.  They must heartbeat faster than this
     pickle: True            # Is the scheduler allowed to deserialize arbitrary bytestrings
     preload: []             # Run custom modules with Scheduler
@@ -152,7 +152,7 @@ distributed:
       # Fractions of worker process memory at which we take action to avoid memory
       # blowup. Set any of the values to False to turn off the behavior entirely.
       # All fractions are relative to each worker's memory_limit.
-      transfer: 0.10  # fractional size of incoming data transfers where we start 
+      transfer: 0.10  # fractional size of incoming data transfers where we start
                        # throttling incoming data transfers
       target: 0.60     # fraction of managed memory where we start spilling to disk
       spill: 0.70      # fraction of process memory where we start spilling to disk

distributed/tests/test_scheduler.py

@@ -257,7 +257,7 @@ def random(**kwargs):
 
 
 @pytest.mark.skipif(
-    math.isfinite(dask.config.get("distributed.scheduler.worker-saturation")),
+    math.isfinite(float(dask.config.get("distributed.scheduler.worker-saturation"))),
     reason="Not relevant with queuing on; see https://github.com/dask/distributed/issues/7204",
 )
 @gen_cluster(
@@ -915,8 +915,10 @@ def f(x, y=2):
     assert set(d) == {"function", "args"}
 
 
+@pytest.mark.parametrize("worker_saturation", [1.0, float("inf")])
 @gen_cluster()
-async def test_ready_remove_worker(s, a, b):
+async def test_ready_remove_worker(s, a, b, worker_saturation):
+    s.WORKER_SATURATION = worker_saturation
     s.update_graph(
         tasks={"x-%d" % i: dumps_task((inc, i)) for i in range(20)},
         keys=["x-%d" % i for i in range(20)],
@@ -1552,7 +1554,10 @@ async def test_balance_many_workers(c, s, *workers):
 # FIXME test is very timing-based; if some threads are consistently slower than others,
 # they'll receive fewer tasks from the queue (a good thing).
 @pytest.mark.skipif(
-    MACOS and math.isfinite(dask.config.get("distributed.scheduler.worker-saturation")),
+    MACOS
+    and math.isfinite(
+        float(dask.config.get("distributed.scheduler.worker-saturation"))
+    ),
     reason="flaky on macOS with queuing active",
 )
 @nodebug