Address comments

Signed-off-by: Cheng Su <[email protected]>

python/ray/data/_internal/execution/backpressure_policy/concurrency_cap_backpressure_policy.py

@@ -18,14 +18,11 @@
 class ConcurrencyCapBackpressurePolicy(BackpressurePolicy):
     """A backpressure policy that caps the concurrency of each operator.
 
-    The concurrency cap limits the number of concurrently running tasks.
-
-    The concrete stategy is as follows:
-    - Each PhysicalOperator is assigned a concurrency cap.
-    - An PhysicalOperator can run new tasks if the number of running tasks is less
-      than the cap.
+    The policy will limit the number of concurrently running tasks based on its
+    concurrency cap parameter.
 
     NOTE: Only support setting concurrency cap for `TaskPoolMapOperator` for now.
+    TODO(chengsu): Consolidate with actor scaling logic of `ActorPoolMapOperator`.
     """
 
     def __init__(self, topology: "Topology"):

python/ray/data/tests/test_backpressure_policies.py

@@ -125,8 +125,8 @@ def test_e2e_normal(self):
         N = self.__class__._cluster_cpus
         ds = ray.data.range(N, parallelism=N)
         # Use different `num_cpus` to make sure they don't fuse.
-        ds = ds.map_batches(map_func1, batch_size=None, num_cpus=1)
-        ds = ds.map_batches(map_func2, batch_size=None, num_cpus=1.1)
+        ds = ds.map_batches(map_func1, batch_size=None, num_cpus=1, concurrency=1)
+        ds = ds.map_batches(map_func2, batch_size=None, num_cpus=1.1, concurrency=1)
         res = ds.take_all()
         self.assertEqual(len(res), N)