[data] introduce abstract interface for data autoscaling #45002

raulchen · 2024-04-26T22:00:02Z

Why are these changes needed?

Introduce an abstract interface for data autoscaling, making autoscaling behavior easier to customize and extend. Main components:
- Autoscaler: the abstract interface responsible for all autoscaling decisions, including cluster and actor pool autoscaling.
- AutoscalingActorPool: abstract interface that represents an actor pool that can autoscale.
- DefaultAutoscaler: default implementation.
No major code logic changes in this PR, except
- fixing a small bug of calculating actor pool util (should be num_active_actors/current_size instead of num_running_actors/current_size).
- ActorPoolMapOperator.incremental_resource_usage now doesn't consider autoscaling, as we are abstracting autoscaling out of the op. Previously the info wasn't useful either.
- Removed actor pool autoscaling logic for bulk executor.

Related issue number

Checks

I've signed off every commit(by using the -s flag, i.e., git commit -s) in this PR.
I've run scripts/format.sh to lint the changes in this PR.
I've included any doc changes needed for https://docs.ray.io/en/master/.
- I've added any new APIs to the API Reference. For example, if I added a
  method in Tune, I've added it in doc/source/tune/api/ under the
  corresponding .rst file.
I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
Testing Strategy
- Unit tests
- Release tests
- This PR is not tested :(

Signed-off-by: Hao Chen <chenh1024@gmail.com>

bveeramani

Overall LGTM

python/ray/data/_internal/execution/autoscaler/__init__.py

bveeramani · 2024-05-06T02:04:39Z

python/ray/data/_internal/execution/autoscaler/autoscaler.py

+
+
+@DeveloperAPI
+class Autoscaler(metaclass=ABCMeta):


Nit:

New class ABC has ABCMeta as its meta class. Using ABC as a base class has essentially the same effect as specifying metaclass=abc.ABCMeta, but is simpler to type and easier to read.

Suggested change

class Autoscaler(metaclass=ABCMeta):

class Autoscaler(ABC):

thanks. I forgot this again 😅

bveeramani · 2024-05-06T02:05:52Z

python/ray/data/_internal/execution/autoscaler/autoscaler.py

+        self._execution_id = execution_id
+
+    @abstractmethod
+    def try_trigger_scaling(self, scheduling_decision: "SchedulingDecision"):


This method is supposed to trigger autoscaling of both the cluster and actor pools, right? Could we add a note in the docstring? I don't think it's obvious what's getting autoscaled from the name

bveeramani · 2024-05-06T02:06:06Z

python/ray/data/_internal/execution/autoscaler/autoscaling_actor_pool.py

+
+
+@DeveloperAPI
+class AutoscalingActorPool(metaclass=ABCMeta):


Nit:

Suggested change

class AutoscalingActorPool(metaclass=ABCMeta):

class AutoscalingActorPool(ABC):

bveeramani · 2024-05-06T02:08:40Z

python/ray/data/_internal/execution/autoscaler/default_autoscaler.py

+        self._try_scale_up_cluster(scheduling_decision)
+        self._try_scale_up_or_down_actor_pool(scheduling_decision)
+
+    def _actor_pool_util(self, actor_pool: AutoscalingActorPool):


Nit: Could we rename this method to be more descriptive? I wasn't able to infer what the method does from the name _actor_pool_util

will update it to _calculate_actor_pool_util

Oh, does "util" refer to "utilization" in this context? Thought it meant "util" as in "utility"

yeah, I didn't realize "util" is also an common abbreviation for "utility"

python/ray/data/_internal/execution/autoscaler/default_autoscaler.py

bveeramani · 2024-05-06T02:17:46Z

python/ray/data/_internal/execution/interfaces/physical_operator.py

+    def get_autoscaling_actor_pools(self) -> List[AutoscalingActorPool]:
+        """Return a list of `AutoscalingActorPool`s managed by this operator."""
+        return []


Do we expect any operators other than ActorPoolMapOperator to override this method? Feel like it's not ideal to add a method to the PhysicalOperator interface just for one subclass, although I can't think of any alternatives off the top of my head.

we could potentially use some top PhysicalOperator level attribute which describes if the operator relies on actors or tasks, then call this method only for operators for which that attribute is true

streaming aggregation also use actor pools and may implement this. also, I want to avoid other components (autoscaler and resource manager) depending on the ActorPoolMapOperator. This makes the dependency graph more complex.

use some top PhysicalOperator level attribute which describes if the operator relies on actors or tasks

I think this is redundant, because we can tell this by if get_autoscaling_actor_pools returns an empty list.

bveeramani · 2024-05-06T02:20:18Z

python/ray/data/_internal/execution/autoscaler/default_autoscaler.py

+                        actor_pool, op
+                    )
+                    if should_scale_up and not should_scale_down:
+                        if actor_pool.scale_up(1) == 0:


When would this evaluate to true? Looks like scale_up always returns input value?

if i understand correctly, looks like scale_up is intended to return the number of actors actually added, which could differ from the requested scaleup. but in the current default implementation, looks like we return the input as @bveeramani said

yes, the current implementation always returns true. But I wanted to the make the interface more flexible and also make it consistent with scale_down

python/ray/data/_internal/execution/autoscaler/default_autoscaler.py

scottjlee · 2024-05-06T17:35:20Z

python/ray/data/_internal/execution/autoscaler/autoscaler.py

+    def try_trigger_scaling(self, scheduling_decision: "SchedulingDecision"):
+        """Try trigger autoscaling.
+
+        This method will be called each time when StreamExecutor makes


nit: here and docstring of on_executor_shutdown, to make sure readers don't get confused by a different name

Suggested change

This method will be called each time when StreamExecutor makes

This method will be called each time when StreamingExecutor makes

scottjlee · 2024-05-06T17:53:16Z

python/ray/data/_internal/execution/autoscaler/default_autoscaler.py

+                        actor_pool, op
+                    )
+                    if should_scale_up and not should_scale_down:
+                        if actor_pool.scale_up(1) == 0:


if i understand correctly, looks like scale_up is intended to return the number of actors actually added, which could differ from the requested scaleup. but in the current default implementation, looks like we return the input as @bveeramani said

scottjlee · 2024-05-06T18:12:50Z

python/ray/data/_internal/execution/interfaces/physical_operator.py

+    def get_autoscaling_actor_pools(self) -> List[AutoscalingActorPool]:
+        """Return a list of `AutoscalingActorPool`s managed by this operator."""
+        return []


we could potentially use some top PhysicalOperator level attribute which describes if the operator relies on actors or tasks, then call this method only for operators for which that attribute is true

scottjlee · 2024-05-06T19:11:23Z

python/ray/data/_internal/execution/streaming_executor_state.py

+class OpSchedulingStatus:
+    """The scheduling status of an operator."""
+
+    # Whether the operator is runnable.


what exactly does "runnable" mean here? can we expand on this definition?
also, wondering if it would make sense to connect this with OpState, or combine the two classes together

select_op_to_run checks which ops are runnable, and choose the best op to run. "runnable" means this. Will add a comment.
also good suggestion to incorporate this class with OpState.

Signed-off-by: Hao Chen <chenh1024@gmail.com>

raulchen · 2024-05-06T23:24:16Z

@bveeramani @scottjlee thanks for your comments. all addressed.

…#45002) * Introduce an abstract interface for data autoscaling, making autoscaling behavior easier to customize and extend. Main components: * `Autoscaler`: the abstract interface responsible for all autoscaling decisions, including cluster and actor pool autoscaling. * `AutoscalingActorPool`: abstract interface that represents an actor pool that can autoscale. * `DefaultAutoscaler`: default implementation. * No major code logic changes in this PR, except * fixing a small bug of calculating actor pool util (should be `num_active_actors/current_size` instead of `num_running_actors/current_size`). * `ActorPoolMapOperator.incremental_resource_usage` now doesn't consider autoscaling, as we are abstracting autoscaling out of the op. Previously the info wasn't useful either. * Removed actor pool autoscaling logic for bulk executor. --------- Signed-off-by: Hao Chen <chenh1024@gmail.com>

can-anyscale · 2024-05-08T04:27:47Z

@raulchen this broke https://github.com/orgs/anyscale/projects/76/views/1?pane=issue&itemId=54699584, can you help investigate, thankks

## Why are these changes needed? Fix following bugs introduced by #45002: * `autoscaler.try_trigger_scaling` not called when `select_op_to_run` returns None. * scaling up condition on `under_resource_limits`. `python/ray/data/tests/test_streaming_integration.py::test_e2e_autoscaling_up` should pass after this fix. ## Related issue number Closes #43481 ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( Signed-off-by: Hao Chen <chenh1024@gmail.com>

## Why are these changes needed? Fix following bugs introduced by ray-project#45002: * `autoscaler.try_trigger_scaling` not called when `select_op_to_run` returns None. * scaling up condition on `under_resource_limits`. `python/ray/data/tests/test_streaming_integration.py::test_e2e_autoscaling_up` should pass after this fix. ## Related issue number Closes ray-project#43481 ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( Signed-off-by: Hao Chen <chenh1024@gmail.com>

…#45002) * Introduce an abstract interface for data autoscaling, making autoscaling behavior easier to customize and extend. Main components: * `Autoscaler`: the abstract interface responsible for all autoscaling decisions, including cluster and actor pool autoscaling. * `AutoscalingActorPool`: abstract interface that represents an actor pool that can autoscale. * `DefaultAutoscaler`: default implementation. * No major code logic changes in this PR, except * fixing a small bug of calculating actor pool util (should be `num_active_actors/current_size` instead of `num_running_actors/current_size`). * `ActorPoolMapOperator.incremental_resource_usage` now doesn't consider autoscaling, as we are abstracting autoscaling out of the op. Previously the info wasn't useful either. * Removed actor pool autoscaling logic for bulk executor. --------- Signed-off-by: Hao Chen <chenh1024@gmail.com>

## Why are these changes needed? Fix following bugs introduced by ray-project#45002: * `autoscaler.try_trigger_scaling` not called when `select_op_to_run` returns None. * scaling up condition on `under_resource_limits`. `python/ray/data/tests/test_streaming_integration.py::test_e2e_autoscaling_up` should pass after this fix. ## Related issue number Closes ray-project#43481 ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( Signed-off-by: Hao Chen <chenh1024@gmail.com>

…#45002) * Introduce an abstract interface for data autoscaling, making autoscaling behavior easier to customize and extend. Main components: * `Autoscaler`: the abstract interface responsible for all autoscaling decisions, including cluster and actor pool autoscaling. * `AutoscalingActorPool`: abstract interface that represents an actor pool that can autoscale. * `DefaultAutoscaler`: default implementation. * No major code logic changes in this PR, except * fixing a small bug of calculating actor pool util (should be `num_active_actors/current_size` instead of `num_running_actors/current_size`). * `ActorPoolMapOperator.incremental_resource_usage` now doesn't consider autoscaling, as we are abstracting autoscaling out of the op. Previously the info wasn't useful either. * Removed actor pool autoscaling logic for bulk executor. --------- Signed-off-by: Hao Chen <chenh1024@gmail.com>

## Why are these changes needed? Fix following bugs introduced by ray-project#45002: * `autoscaler.try_trigger_scaling` not called when `select_op_to_run` returns None. * scaling up condition on `under_resource_limits`. `python/ray/data/tests/test_streaming_integration.py::test_e2e_autoscaling_up` should pass after this fix. ## Related issue number Closes ray-project#43481 ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( Signed-off-by: Hao Chen <chenh1024@gmail.com>

…#45002) * Introduce an abstract interface for data autoscaling, making autoscaling behavior easier to customize and extend. Main components: * `Autoscaler`: the abstract interface responsible for all autoscaling decisions, including cluster and actor pool autoscaling. * `AutoscalingActorPool`: abstract interface that represents an actor pool that can autoscale. * `DefaultAutoscaler`: default implementation. * No major code logic changes in this PR, except * fixing a small bug of calculating actor pool util (should be `num_active_actors/current_size` instead of `num_running_actors/current_size`). * `ActorPoolMapOperator.incremental_resource_usage` now doesn't consider autoscaling, as we are abstracting autoscaling out of the op. Previously the info wasn't useful either. * Removed actor pool autoscaling logic for bulk executor. --------- Signed-off-by: Hao Chen <chenh1024@gmail.com> Signed-off-by: Ryan O'Leary <ryanaoleary@google.com>

## Why are these changes needed? Fix following bugs introduced by ray-project#45002: * `autoscaler.try_trigger_scaling` not called when `select_op_to_run` returns None. * scaling up condition on `under_resource_limits`. `python/ray/data/tests/test_streaming_integration.py::test_e2e_autoscaling_up` should pass after this fix. ## Related issue number Closes ray-project#43481 ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( Signed-off-by: Hao Chen <chenh1024@gmail.com> Signed-off-by: Ryan O'Leary <ryanaoleary@google.com>

…#45002) * Introduce an abstract interface for data autoscaling, making autoscaling behavior easier to customize and extend. Main components: * `Autoscaler`: the abstract interface responsible for all autoscaling decisions, including cluster and actor pool autoscaling. * `AutoscalingActorPool`: abstract interface that represents an actor pool that can autoscale. * `DefaultAutoscaler`: default implementation. * No major code logic changes in this PR, except * fixing a small bug of calculating actor pool util (should be `num_active_actors/current_size` instead of `num_running_actors/current_size`). * `ActorPoolMapOperator.incremental_resource_usage` now doesn't consider autoscaling, as we are abstracting autoscaling out of the op. Previously the info wasn't useful either. * Removed actor pool autoscaling logic for bulk executor. --------- Signed-off-by: Hao Chen <chenh1024@gmail.com>

## Why are these changes needed? Fix following bugs introduced by ray-project#45002: * `autoscaler.try_trigger_scaling` not called when `select_op_to_run` returns None. * scaling up condition on `under_resource_limits`. `python/ray/data/tests/test_streaming_integration.py::test_e2e_autoscaling_up` should pass after this fix. ## Related issue number Closes ray-project#43481 ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( Signed-off-by: Hao Chen <chenh1024@gmail.com>

## Why are these changes needed? Fix following bugs introduced by ray-project#45002: * `autoscaler.try_trigger_scaling` not called when `select_op_to_run` returns None. * scaling up condition on `under_resource_limits`. `python/ray/data/tests/test_streaming_integration.py::test_e2e_autoscaling_up` should pass after this fix. ## Related issue number Closes ray-project#43481 ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( Signed-off-by: Hao Chen <chenh1024@gmail.com> Signed-off-by: gchurch <gabe1church@gmail.com>

raulchen requested review from ericl, scv119, c21, amogkam, scottjlee, bveeramani, stephanie-wang and omatthew98 as code owners April 26, 2024 22:00

raulchen marked this pull request as draft April 26, 2024 22:00

raulchen added 15 commits April 29, 2024 16:47

autoscaler interface

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integrate

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separate file

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increment_resource_usage

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actor pool map

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fix test_actor_pool_map_oprator

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lint

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fix test_streaming_executor

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autoscaling actor pool

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format

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minor

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SchedulingDecision

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fix

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comments

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refine

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raulchen force-pushed the autoscaler-interface branch from 43b1e07 to 11f96e0 Compare April 29, 2024 23:48

raulchen added 3 commits April 30, 2024 17:01

fix test_actor_pool

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test actor pool scaling

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test cluster scaling

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raulchen changed the title ~~[data] Autoscaling interface~~ [data] introduce abstract interface for data autoscaling May 1, 2024

raulchen marked this pull request as ready for review May 1, 2024 03:05

raulchen added 4 commits May 1, 2024 11:43

fix test_executor_resource_management.py

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fix resource_manager

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remove bulk test

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lint

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minor

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refine scheduling status

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fix

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Merge branch 'master' into autoscaler-interface

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[data] fix bugs introduced by autoscaler refactor #45200

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[data] introduce abstract interface for data autoscaling #45002

[data] introduce abstract interface for data autoscaling #45002

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can-anyscale commented May 8, 2024

	class AutoscalingActorPool(metaclass=ABCMeta):
	class AutoscalingActorPool(ABC):

	This method will be called each time when StreamExecutor makes
	This method will be called each time when StreamingExecutor makes

[data] introduce abstract interface for data autoscaling #45002

[data] introduce abstract interface for data autoscaling #45002

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raulchen commented Apr 26, 2024 • edited

Why are these changes needed?

Related issue number

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raulchen May 6, 2024 • edited

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raulchen commented May 6, 2024

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