⚡️ Speed up function _get_scheduler by 166%

[codeflash-ai]

📄 166% (1.66x) speedup for _get_scheduler in xarray/backends/locks.py

⏱️ Runtime : 13.5 milliseconds → 5.08 milliseconds (best of 27 runs)

📝 Explanation and details

The optimization restructures the exception-heavy scheduler detection logic into a more efficient approach using getattr and early checks.

Key optimizations:

1. Eliminated redundant exception handling: The original code wrapped both distributed and multiprocessing checks in try/except blocks that caught AttributeError exceptions. The optimized version uses getattr with defaults to safely access attributes without exception overhead.

2. Pre-extracted __self__ attribute: Instead of accessing actual_get.__self__ inside the exception handler where it could raise AttributeError, the optimized code extracts it once with getattr(actual_get, '__self__', None) and checks if it's None before proceeding.

3. Reduced import overhead: For the distributed scheduler check, the import of dask.distributed.Client now only happens when actual_get_self is not None, avoiding unnecessary imports in many cases.

4. Safer multiprocessing access: Uses nested getattr calls (getattr(getattr(dask, 'multiprocessing', None), 'get', None)) to safely navigate the attribute chain without raising AttributeError.

Performance impact: The line profiler shows the expensive from dask.distributed import Client import (11.6% of total time in original) is now conditional and happens less frequently. Exception handling overhead is eliminated across multiple code paths.

Function context: This optimization is particularly valuable since _get_scheduler() is called from to_netcdf() in the data writing pipeline. The 165% speedup means faster netCDF file operations, especially important when processing large datasets or in batch operations where this function may be called repeatedly.

Test results show: The optimization excels with threaded/multiprocessing scenarios (400-500% faster) where the original code's exception handling was most expensive, while maintaining similar performance for distributed scenarios where the import was unavoidable.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3147 Passed
⏪ Replay Tests	✅ 255 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

from __future__ import annotations

import sys

# imports
import pytest
from xarray.backends.locks import _get_scheduler

# unit tests

# ------------- Basic Test Cases -------------


def test_returns_none_if_dask_not_installed(monkeypatch):
    """Should return None if dask is not installed (simulate ImportError)."""
    # Remove dask from sys.modules and block import
    monkeypatch.setitem(sys.modules, "dask", None)
    monkeypatch.setitem(sys.modules, "dask.base", None)
    # Patch __import__ to raise ImportError for dask
    orig_import = __import__

    def fake_import(name, *args, **kwargs):
        if name.startswith("dask"):
            raise ImportError("No module named 'dask'")
        return orig_import(name, *args, **kwargs)

    monkeypatch.setattr("builtins.__import__", fake_import)
    codeflash_output = _get_scheduler()  # 2.08μs -> 1.99μs (4.58% faster)


def test_returns_threaded_scheduler(monkeypatch):
    """Should return 'threaded' if dask is installed but not using multiprocessing/distributed."""

    # Patch dask and dask.base.get_scheduler to return a dummy function
    class DummyDask:
        class multiprocessing:
            get = object()

    class DummyGet:
        __self__ = None

    def fake_get_scheduler(get, collection):
        return DummyGet()

    monkeypatch.setitem(sys.modules, "dask", DummyDask)
    monkeypatch.setitem(
        sys.modules,
        "dask.base",
        type("base", (), {"get_scheduler": staticmethod(fake_get_scheduler)}),
    )
    # Patch dask.multiprocessing.get to not match
    codeflash_output = _get_scheduler()  # 21.5μs -> 3.74μs (475% faster)


def test_returns_multiprocessing_scheduler(monkeypatch):
    """Should return 'multiprocessing' if dask.multiprocessing.get is returned."""
    dummy_get = object()

    class DummyDask:
        class multiprocessing:
            get = dummy_get

    def fake_get_scheduler(get, collection):
        return dummy_get

    monkeypatch.setitem(sys.modules, "dask", DummyDask)
    monkeypatch.setitem(
        sys.modules,
        "dask.base",
        type("base", (), {"get_scheduler": staticmethod(fake_get_scheduler)}),
    )
    codeflash_output = _get_scheduler()  # 19.9μs -> 3.36μs (494% faster)


def test_returns_distributed_scheduler(monkeypatch):
    """Should return 'distributed' if actual_get.__self__ is an instance of dask.distributed.Client."""

    class DummyClient:
        pass

    class DummyGet:
        def __init__(self):
            self.__self__ = DummyClient()

    def fake_get_scheduler(get, collection):
        return DummyGet()

    class DummyDistributed:
        Client = DummyClient

    monkeypatch.setitem(sys.modules, "dask", type("dask", (), {}))
    monkeypatch.setitem(
        sys.modules,
        "dask.base",
        type("base", (), {"get_scheduler": staticmethod(fake_get_scheduler)}),
    )
    monkeypatch.setitem(sys.modules, "dask.distributed", DummyDistributed)
    codeflash_output = _get_scheduler()  # 4.84μs -> 4.94μs (1.98% slower)


# ------------- Edge Test Cases -------------


def test_distributed_importerror(monkeypatch):
    """Should not fail if dask.distributed is not installed (ImportError raised)."""

    class DummyGet:
        __self__ = object()

    def fake_get_scheduler(get, collection):
        return DummyGet()

    monkeypatch.setitem(sys.modules, "dask", type("dask", (), {}))
    monkeypatch.setitem(
        sys.modules,
        "dask.base",
        type("base", (), {"get_scheduler": staticmethod(fake_get_scheduler)}),
    )
    # Remove dask.distributed from sys.modules and patch import to raise ImportError
    monkeypatch.setitem(sys.modules, "dask.distributed", None)
    orig_import = __import__

    def fake_import(name, *args, **kwargs):
        if name == "dask.distributed":
            raise ImportError("No module named 'dask.distributed'")
        return orig_import(name, *args, **kwargs)

    monkeypatch.setattr("builtins.__import__", fake_import)
    codeflash_output = _get_scheduler()  # 6.42μs -> 6.19μs (3.62% faster)


def test_multiprocessing_attributeerror(monkeypatch):
    """Should not fail if dask.multiprocessing is missing (AttributeError)."""

    class DummyGet:
        __self__ = None

    def fake_get_scheduler(get, collection):
        return DummyGet()

    # Patch dask so that multiprocessing attribute does not exist
    class DummyDask:
        pass

    monkeypatch.setitem(sys.modules, "dask", DummyDask)
    monkeypatch.setitem(
        sys.modules,
        "dask.base",
        type("base", (), {"get_scheduler": staticmethod(fake_get_scheduler)}),
    )
    codeflash_output = _get_scheduler()  # 22.3μs -> 4.01μs (455% faster)


def test_actual_get_has_no_self(monkeypatch):
    """Should not fail if actual_get has no __self__ attribute (AttributeError)."""

    class DummyGet:
        pass

    def fake_get_scheduler(get, collection):
        return DummyGet()

    monkeypatch.setitem(sys.modules, "dask", type("dask", (), {}))
    monkeypatch.setitem(
        sys.modules,
        "dask.base",
        type("base", (), {"get_scheduler": staticmethod(fake_get_scheduler)}),
    )
    codeflash_output = _get_scheduler()  # 20.5μs -> 3.76μs (445% faster)


def test_get_scheduler_raises(monkeypatch):
    """Should return None if dask.base.get_scheduler raises ImportError."""
    # Patch dask.base.get_scheduler to raise ImportError
    monkeypatch.setitem(sys.modules, "dask", type("dask", (), {}))

    def fake_get_scheduler(get, collection):
        raise ImportError("No scheduler")

    monkeypatch.setitem(
        sys.modules,
        "dask.base",
        type("base", (), {"get_scheduler": staticmethod(fake_get_scheduler)}),
    )
    codeflash_output = _get_scheduler()  # 3.45μs -> 3.65μs (5.43% slower)


# ------------- Large Scale Test Cases -------------


def test_many_calls_threaded(monkeypatch):
    """Test that repeated calls (hundreds) to _get_scheduler are consistent and efficient."""

    class DummyDask:
        class multiprocessing:
            get = object()

    class DummyGet:
        __self__ = None

    def fake_get_scheduler(get, collection):
        return DummyGet()

    monkeypatch.setitem(sys.modules, "dask", DummyDask)
    monkeypatch.setitem(
        sys.modules,
        "dask.base",
        type("base", (), {"get_scheduler": staticmethod(fake_get_scheduler)}),
    )
    results = []
    for _ in range(500):  # large number but <1000
        results.append(_get_scheduler())  # 2.26ms -> 432μs (422% faster)


def test_many_calls_multiprocessing(monkeypatch):
    """Test that repeated calls to _get_scheduler with multiprocessing are consistent."""
    dummy_get = object()

    class DummyDask:
        class multiprocessing:
            get = dummy_get

    def fake_get_scheduler(get, collection):
        return dummy_get

    monkeypatch.setitem(sys.modules, "dask", DummyDask)
    monkeypatch.setitem(
        sys.modules,
        "dask.base",
        type("base", (), {"get_scheduler": staticmethod(fake_get_scheduler)}),
    )
    results = []
    for _ in range(500):
        results.append(_get_scheduler())  # 2.21ms -> 414μs (435% faster)


def test_many_calls_distributed(monkeypatch):
    """Test that repeated calls to _get_scheduler with distributed are consistent."""

    class DummyClient:
        pass

    class DummyGet:
        def __init__(self):
            self.__self__ = DummyClient()

    def fake_get_scheduler(get, collection):
        return DummyGet()

    class DummyDistributed:
        Client = DummyClient

    monkeypatch.setitem(sys.modules, "dask", type("dask", (), {}))
    monkeypatch.setitem(
        sys.modules,
        "dask.base",
        type("base", (), {"get_scheduler": staticmethod(fake_get_scheduler)}),
    )
    monkeypatch.setitem(sys.modules, "dask.distributed", DummyDistributed)
    results = []
    for _ in range(500):
        results.append(_get_scheduler())  # 646μs -> 657μs (1.68% slower)


# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from __future__ import annotations

# imports
import pytest
from xarray.backends.locks import _get_scheduler

# unit tests

# Basic Test Cases


def test_no_dask_installed(monkeypatch):
    """Test behavior when dask is not installed (ImportError should be raised)."""
    # Simulate dask not being installed
    monkeypatch.setitem(__import__("sys").modules, "dask", None)
    monkeypatch.setitem(__import__("sys").modules, "dask.base", None)
    codeflash_output = _get_scheduler()
    result = codeflash_output  # 14.4μs -> 14.2μs (1.59% faster)


def test_threaded_scheduler_default(monkeypatch):
    """Test default behavior when dask is installed and no distributed/multiprocessing."""

    # Simulate dask installed, but not distributed or multiprocessing
    class FakeDask:
        class multiprocessing:
            get = object()

    class FakeGetScheduler:
        def __call__(self, get, collection):
            return lambda x: x

    monkeypatch.setitem(__import__("sys").modules, "dask", FakeDask)
    monkeypatch.setitem(
        __import__("sys").modules,
        "dask.base",
        type("FakeBase", (), {"get_scheduler": FakeGetScheduler()}),
    )
    codeflash_output = _get_scheduler()
    result = codeflash_output  # 7.30μs -> 3.46μs (111% faster)


def test_multiprocessing_scheduler(monkeypatch):
    """Test when actual_get is dask.multiprocessing.get."""

    class FakeDask:
        class multiprocessing:
            get = object()

    class FakeGetScheduler:
        def __call__(self, get, collection):
            return FakeDask.multiprocessing.get

    monkeypatch.setitem(__import__("sys").modules, "dask", FakeDask)
    monkeypatch.setitem(
        __import__("sys").modules,
        "dask.base",
        type("FakeBase", (), {"get_scheduler": FakeGetScheduler()}),
    )
    codeflash_output = _get_scheduler()
    result = codeflash_output  # 6.72μs -> 3.31μs (103% faster)


def test_distributed_scheduler(monkeypatch):
    """Test when actual_get.__self__ is an instance of dask.distributed.Client."""

    class FakeClient:
        pass

    class FakeActualGet:
        __self__ = FakeClient()

    class FakeGetScheduler:
        def __call__(self, get, collection):
            return FakeActualGet

    class FakeDistributed:
        Client = FakeClient

    monkeypatch.setitem(__import__("sys").modules, "dask", object())
    monkeypatch.setitem(
        __import__("sys").modules,
        "dask.base",
        type("FakeBase", (), {"get_scheduler": FakeGetScheduler()}),
    )
    monkeypatch.setitem(__import__("sys").modules, "dask.distributed", FakeDistributed)
    codeflash_output = _get_scheduler()
    result = codeflash_output  # 4.01μs -> 3.96μs (1.13% faster)


# Edge Test Cases


def test_actual_get_has_no_self(monkeypatch):
    """Test when actual_get has no __self__ attribute (should not raise)."""

    class FakeGetScheduler:
        def __call__(self, get, collection):
            return lambda x: x

    monkeypatch.setitem(__import__("sys").modules, "dask", object())
    monkeypatch.setitem(
        __import__("sys").modules,
        "dask.base",
        type("FakeBase", (), {"get_scheduler": FakeGetScheduler()}),
    )
    # Remove dask.distributed from sys.modules to simulate ImportError
    if "dask.distributed" in __import__("sys").modules:
        del __import__("sys").modules["dask.distributed"]
    codeflash_output = _get_scheduler()
    result = codeflash_output  # 21.4μs -> 3.34μs (540% faster)


def test_actual_get_is_none(monkeypatch):
    """Test when get_scheduler returns None (should default to threaded)."""

    class FakeGetScheduler:
        def __call__(self, get, collection):
            return None

    monkeypatch.setitem(__import__("sys").modules, "dask", object())
    monkeypatch.setitem(
        __import__("sys").modules,
        "dask.base",
        type("FakeBase", (), {"get_scheduler": FakeGetScheduler()}),
    )
    codeflash_output = _get_scheduler()
    result = codeflash_output  # 20.6μs -> 3.23μs (539% faster)


def test_multiprocessing_attribute_error(monkeypatch):
    """Test when dask.multiprocessing is missing (should not raise)."""

    class FakeGetScheduler:
        def __call__(self, get, collection):
            return object()

    class FakeDask:
        pass  # No multiprocessing attribute

    monkeypatch.setitem(__import__("sys").modules, "dask", FakeDask)
    monkeypatch.setitem(
        __import__("sys").modules,
        "dask.base",
        type("FakeBase", (), {"get_scheduler": FakeGetScheduler()}),
    )
    codeflash_output = _get_scheduler()
    result = codeflash_output  # 20.7μs -> 3.64μs (469% faster)


def test_distributed_import_error(monkeypatch):
    """Test when dask.distributed import fails (should not raise)."""

    class FakeGetScheduler:
        def __call__(self, get, collection):
            return object()

    monkeypatch.setitem(__import__("sys").modules, "dask", object())
    monkeypatch.setitem(
        __import__("sys").modules,
        "dask.base",
        type("FakeBase", (), {"get_scheduler": FakeGetScheduler()}),
    )
    # Remove dask.distributed from sys.modules to simulate ImportError
    if "dask.distributed" in __import__("sys").modules:
        del __import__("sys").modules["dask.distributed"]
    codeflash_output = _get_scheduler()
    result = codeflash_output  # 20.3μs -> 3.29μs (518% faster)


def test_get_scheduler_raises(monkeypatch):
    """Test if get_scheduler itself raises an exception (should return None)."""

    class FakeGetScheduler:
        def __call__(self, get, collection):
            raise ImportError("dask.base.get_scheduler not available")

    monkeypatch.setitem(__import__("sys").modules, "dask", object())
    monkeypatch.setitem(
        __import__("sys").modules,
        "dask.base",
        type("FakeBase", (), {"get_scheduler": FakeGetScheduler()}),
    )
    codeflash_output = _get_scheduler()
    result = codeflash_output  # 3.65μs -> 3.36μs (8.44% faster)


# Large Scale Test Cases


def test_many_calls_threaded(monkeypatch):
    """Test performance and determinism with many calls returning threaded."""

    class FakeGetScheduler:
        def __call__(self, get, collection):
            return lambda x: x

    monkeypatch.setitem(__import__("sys").modules, "dask", object())
    monkeypatch.setitem(
        __import__("sys").modules,
        "dask.base",
        type("FakeBase", (), {"get_scheduler": FakeGetScheduler()}),
    )
    results = []
    for _ in range(500):  # Reasonable scale
        results.append(_get_scheduler())  # 2.37ms -> 440μs (438% faster)


def test_many_calls_multiprocessing(monkeypatch):
    """Test performance and determinism with many calls returning multiprocessing."""

    class FakeDask:
        class multiprocessing:
            get = object()

    class FakeGetScheduler:
        def __call__(self, get, collection):
            return FakeDask.multiprocessing.get

    monkeypatch.setitem(__import__("sys").modules, "dask", FakeDask)
    monkeypatch.setitem(
        __import__("sys").modules,
        "dask.base",
        type("FakeBase", (), {"get_scheduler": FakeGetScheduler()}),
    )
    results = []
    for _ in range(500):  # Reasonable scale
        results.append(_get_scheduler())  # 2.26ms -> 450μs (402% faster)


def test_many_calls_distributed(monkeypatch):
    """Test performance and determinism with many calls returning distributed."""

    class FakeClient:
        pass

    class FakeActualGet:
        __self__ = FakeClient()

    class FakeGetScheduler:
        def __call__(self, get, collection):
            return FakeActualGet

    class FakeDistributed:
        Client = FakeClient

    monkeypatch.setitem(__import__("sys").modules, "dask", object())
    monkeypatch.setitem(
        __import__("sys").modules,
        "dask.base",
        type("FakeBase", (), {"get_scheduler": FakeGetScheduler()}),
    )
    monkeypatch.setitem(__import__("sys").modules, "dask.distributed", FakeDistributed)
    results = []
    for _ in range(500):  # Reasonable scale
        results.append(_get_scheduler())  # 573μs -> 585μs (1.99% slower)


def test_large_variety_of_inputs(monkeypatch):
    """Test with a variety of get/collection values to ensure robustness."""

    class FakeGetScheduler:
        def __call__(self, get, collection):
            return lambda x: x

    monkeypatch.setitem(__import__("sys").modules, "dask", object())
    monkeypatch.setitem(
        __import__("sys").modules,
        "dask.base",
        type("FakeBase", (), {"get_scheduler": FakeGetScheduler()}),
    )
    for i in range(100):
        codeflash_output = _get_scheduler(get=i, collection=[i])
        result = codeflash_output  # 506μs -> 98.9μs (413% faster)


def test_scheduler_switching(monkeypatch):
    """Test switching between scheduler types in sequence."""

    class FakeDask:
        class multiprocessing:
            get = object()

    class FakeClient:
        pass

    class FakeActualGetDist:
        __self__ = FakeClient()

    class FakeGetScheduler:
        def __init__(self):
            self.call_count = 0

        def __call__(self, get, collection):
            self.call_count += 1
            if self.call_count % 3 == 1:
                return FakeDask.multiprocessing.get
            elif self.call_count % 3 == 2:
                return FakeActualGetDist
            else:
                return lambda x: x

    class FakeDistributed:
        Client = FakeClient

    monkeypatch.setitem(__import__("sys").modules, "dask", FakeDask)
    monkeypatch.setitem(
        __import__("sys").modules,
        "dask.base",
        type("FakeBase", (), {"get_scheduler": FakeGetScheduler()}),
    )
    monkeypatch.setitem(__import__("sys").modules, "dask.distributed", FakeDistributed)
    results = []
    for _ in range(30):
        results.append(_get_scheduler())  # 52.8μs -> 39.4μs (33.8% faster)
    # Should cycle through: multiprocessing, distributed, threaded
    for idx, r in enumerate(results):
        if idx % 3 == 0:
            pass
        elif idx % 3 == 1:
            pass
        else:
            pass


# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

⏪ Replay Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_pytest_xarrayteststest_concat_py_xarrayteststest_computation_py_xarrayteststest_formatting_py_xarray__replay_test_0.py::test_xarray_backends_locks__get_scheduler	2.40ms	1.89ms	27.2%✅

To edit these changes git checkout codeflash/optimize-_get_scheduler-miymp5ye and push.