⚡️ Speed up function group_indexers_by_index by 291%

[codeflash-ai]

📄 291% (2.91x) speedup for group_indexers_by_index in xarray/core/indexing.py

⏱️ Runtime : 12.1 milliseconds → 3.11 milliseconds (best of 28 runs)

📝 Explanation and details

This optimization achieves a 290% speedup by reducing attribute lookups and optimizing membership checks in the core loop. The key improvements are:

What optimizations were applied:

1. Cached attribute lookups: Moved obj.xindexes, obj.coords, and obj.dims outside the loop to avoid repeated attribute access
2. Optimized membership checking: Pre-converted obj.dims to a set if it wasn't already one, enabling O(1) membership tests instead of potentially O(n) tuple lookups
3. Method lookup caching: Stored obj_xindexes.get in a local variable to avoid repeated method resolution

Why this leads to speedup:

• Python attribute access (dot notation) has overhead - each obj.xindexes lookup requires dictionary traversal in the object's __dict__
• The key not in obj.dims check was potentially O(n) if obj.dims was a tuple/list, now consistently O(1) with set conversion
• Method lookups like obj.xindexes.get involve method resolution overhead that's eliminated by caching the bound method

How this impacts workloads:
Based on the function reference, group_indexers_by_index is called from map_index_queries, which handles label-based indexing operations. This is a hot path in xarray's indexing system, so the optimization will significantly benefit:

• DataArray/Dataset selection operations with multiple indexers
• Operations that repeatedly access the same dimensions/coordinates

Test case performance patterns:
The optimization shows the most dramatic gains (734-1738% faster) on large-scale test cases with many non-indexed dimensions, where the set conversion for obj.dims pays off immediately. Smaller test cases show modest slowdowns (2-27%) likely due to the upfront cost of set conversion, but real workloads with larger dimension counts will see substantial benefits.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 44 Passed
🌀 Generated Regression Tests	✅ 33 Passed
⏪ Replay Tests	✅ 255 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_indexing.py::TestIndexers.test_group_indexers_by_index	32.1μs	27.4μs	17.2%✅

🌀 Generated Regression Tests and Runtime

from collections import defaultdict

# function to test (copied verbatim from prompt)
from typing import Any, Mapping

# imports
import pytest
from xarray.core.indexing import group_indexers_by_index

# --- Mocks for xarray objects and Index, since we can't import xarray here ---


class DummyIndex:
    """A simple stand-in for xarray.core.indexes.Index"""

    def __init__(self, name):
        self.name = name


class DummyObj:
    """
    Mock for xarray object with .xindexes, .coords, and .dims attributes.
    xindexes: dict mapping key -> Index
    coords: set of coordinate names
    dims: tuple of dimension names
    """

    def __init__(self, xindexes=None, coords=None, dims=None):
        self.xindexes = xindexes or {}
        self.coords = set(coords or [])
        self.dims = tuple(dims or [])


from xarray.core.indexing import group_indexers_by_index

# ---------------------- UNIT TESTS BELOW --------------------------

# Basic Test Cases


def test_single_indexer_with_index():
    # One indexer, key in xindexes, no options
    idx = DummyIndex("foo")
    obj = DummyObj(xindexes={"foo": idx}, coords={"foo"}, dims=("foo",))
    codeflash_output = group_indexers_by_index(obj, {"foo": 1}, {})
    result = codeflash_output  # 3.77μs -> 4.02μs (6.22% slower)


def test_multiple_indexers_with_shared_index():
    # Multiple indexers, all keys in xindexes, all share the same index
    idx = DummyIndex("a")
    obj = DummyObj(xindexes={"a": idx, "b": idx}, coords={"a", "b"}, dims=("a", "b"))
    codeflash_output = group_indexers_by_index(obj, {"a": 10, "b": 20}, {})
    result = codeflash_output  # 4.12μs -> 4.43μs (6.95% slower)


def test_multiple_indexers_with_different_indexes():
    # Multiple indexers, each with their own index
    idx1 = DummyIndex("x")
    idx2 = DummyIndex("y")
    obj = DummyObj(xindexes={"x": idx1, "y": idx2}, coords={"x", "y"}, dims=("x", "y"))
    codeflash_output = group_indexers_by_index(obj, {"x": 1, "y": 2}, {})
    result = codeflash_output  # 4.28μs -> 4.68μs (8.73% slower)
    found = {id(idx1): False, id(idx2): False}
    for index, d in result:
        if index is idx1:
            found[id(idx1)] = True
        elif index is idx2:
            found[id(idx2)] = True


def test_dimension_without_index_fallback():
    # Indexer key is in dims but not in xindexes or coords, options is empty
    obj = DummyObj(xindexes={}, coords=set(), dims=("foo",))
    codeflash_output = group_indexers_by_index(obj, {"foo": 42}, {})
    result = codeflash_output  # 3.98μs -> 4.04μs (1.53% slower)


def test_multiple_dims_without_index():
    # Multiple indexers, all keys are dims, none in xindexes or coords
    obj = DummyObj(xindexes={}, coords=set(), dims=("a", "b"))
    codeflash_output = group_indexers_by_index(obj, {"a": 1, "b": 2}, {})
    result = codeflash_output  # 4.45μs -> 4.57μs (2.65% slower)


# Edge Test Cases


def test_key_in_coords_but_not_xindexes_raises():
    # Key is in coords but not in xindexes
    obj = DummyObj(xindexes={}, coords={"foo"}, dims=("foo",))
    with pytest.raises(KeyError, match="no index found for coordinate 'foo'"):
        group_indexers_by_index(obj, {"foo": 1}, {})  # 2.74μs -> 3.26μs (16.1% slower)


def test_key_not_in_dims_or_coords_raises():
    # Key is not in dims or coords
    obj = DummyObj(xindexes={}, coords={"a"}, dims=("b",))
    with pytest.raises(KeyError, match="is not a valid dimension or coordinate"):
        group_indexers_by_index(obj, {"c": 1}, {})  # 4.46μs -> 4.99μs (10.7% slower)


def test_options_given_for_dim_without_index_raises():
    # Key is in dims, not in xindexes or coords, but options is not empty
    obj = DummyObj(xindexes={}, coords=set(), dims=("foo",))
    with pytest.raises(ValueError, match="cannot supply selection options"):
        group_indexers_by_index(
            obj, {"foo": 1}, {"some_option": True}
        )  # 4.50μs -> 4.89μs (8.06% slower)


def test_empty_indexers_returns_empty():
    # No indexers
    obj = DummyObj(xindexes={}, coords=set(), dims=())
    codeflash_output = group_indexers_by_index(obj, {}, {})
    result = codeflash_output  # 2.33μs -> 3.01μs (22.6% slower)


def test_indexers_with_mixed_index_and_dim():
    # One key with index, one key is just a dim
    idx = DummyIndex("x")
    obj = DummyObj(xindexes={"x": idx}, coords={"x"}, dims=("x", "y"))
    codeflash_output = group_indexers_by_index(obj, {"x": 5, "y": 7}, {})
    result = codeflash_output  # 4.76μs -> 5.00μs (4.74% slower)
    found = {id(idx): False, None: False}
    for index, d in result:
        if index is idx:
            found[id(idx)] = True
        elif index is None:
            found[None] = True


def test_indexers_with_duplicate_indexes():
    # Two keys, both point to the same index object
    idx = DummyIndex("foo")
    obj = DummyObj(xindexes={"a": idx, "b": idx}, coords={"a", "b"}, dims=("a", "b"))
    codeflash_output = group_indexers_by_index(obj, {"a": 1, "b": 2}, {})
    result = codeflash_output  # 4.15μs -> 4.35μs (4.64% slower)


def test_indexers_with_none_index_and_index():
    # One key is a dim (no index), one key has an index
    idx = DummyIndex("foo")
    obj = DummyObj(xindexes={"a": idx}, coords={"a"}, dims=("a", "b"))
    codeflash_output = group_indexers_by_index(obj, {"a": 1, "b": 2}, {})
    result = codeflash_output  # 4.67μs -> 4.79μs (2.51% slower)
    found = {id(idx): False, None: False}
    for index, d in result:
        if index is idx:
            found[id(idx)] = True
        elif index is None:
            found[None] = True


# Large Scale Test Cases


def test_large_number_of_indexers_with_indexes():
    # 1000 unique indexes, one indexer per index
    indexes = {f"dim{i}": DummyIndex(f"dim{i}") for i in range(1000)}
    obj = DummyObj(
        xindexes=indexes,
        coords={f"dim{i}" for i in range(1000)},
        dims=tuple(f"dim{i}" for i in range(1000)),
    )
    indexers = {f"dim{i}": i for i in range(1000)}
    codeflash_output = group_indexers_by_index(obj, indexers, {})
    result = codeflash_output  # 301μs -> 309μs (2.74% slower)
    seen = set()
    for index, d in result:
        for k, v in d.items():
            seen.add(k)


def test_large_number_of_dims_without_indexes():
    # 1000 dims, none in xindexes or coords
    dims = tuple(f"dim{i}" for i in range(1000))
    obj = DummyObj(xindexes={}, coords=set(), dims=dims)
    indexers = {f"dim{i}": i for i in range(1000)}
    codeflash_output = group_indexers_by_index(obj, indexers, {})
    result = codeflash_output  # 2.74ms -> 158μs (1629% faster)
    index, d = result[0]
    for i in range(1000):
        pass


def test_large_mixed_indexes_and_dims():
    # 500 with indexes, 500 as dims without indexes
    indexes = {f"idx{i}": DummyIndex(f"idx{i}") for i in range(500)}
    coords = set(indexes.keys())
    dims = tuple(list(indexes.keys()) + [f"dim{i}" for i in range(500)])
    obj = DummyObj(xindexes=indexes, coords=coords, dims=dims)
    indexers = {
        **{f"idx{i}": i for i in range(500)},
        **{f"dim{i}": i for i in range(500)},
    }
    codeflash_output = group_indexers_by_index(obj, indexers, {})
    result = codeflash_output  # 2.06ms -> 246μs (734% faster)
    found_indexes = set()
    found_none = False
    for index, d in result:
        if index is None:
            for i in range(500):
                pass
            found_none = True
        else:
            for k, v in d.items():
                pass
            found_indexes.add(index.name)


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

from collections import defaultdict

# function to test (copied verbatim from prompt)
from collections.abc import Mapping
from typing import Any

# imports
import pytest
from xarray.core.indexing import group_indexers_by_index


# Minimal stubs for Index and T_Xarray for testing purposes
class DummyIndex:
    def __init__(self, name):
        self.name = name


class DummyObj:
    """
    Dummy object to simulate xarray object for testing group_indexers_by_index.
    """

    def __init__(self, dims, coords=None, xindexes=None):
        self.dims = dims  # tuple or list of dimension names
        self.coords = coords if coords is not None else {}
        # xindexes: dict mapping dim/coord name to Index object
        self.xindexes = xindexes if xindexes is not None else {}


from xarray.core.indexing import group_indexers_by_index

# unit tests

# 1. Basic Test Cases


def test_single_indexed_dim():
    # One dim, one index, one indexer
    idx = DummyIndex("x")
    obj = DummyObj(dims=("x",), xindexes={"x": idx})
    indexers = {"x": 5}
    options = {}
    codeflash_output = group_indexers_by_index(obj, indexers, options)
    result = codeflash_output  # 3.83μs -> 4.07μs (5.92% slower)


def test_multiple_indexed_dims():
    # Two dims, each with an index, both used in indexers
    idx1 = DummyIndex("x")
    idx2 = DummyIndex("y")
    obj = DummyObj(dims=("x", "y"), xindexes={"x": idx1, "y": idx2})
    indexers = {"x": 1, "y": 2}
    options = {}
    codeflash_output = group_indexers_by_index(obj, indexers, options)
    result = codeflash_output  # 4.33μs -> 4.86μs (10.7% slower)
    # Should return two tuples, order not guaranteed
    found = {id(idx1): False, id(idx2): False}
    for index, group in result:
        if index is idx1:
            found[id(idx1)] = True
        elif index is idx2:
            found[id(idx2)] = True
        else:
            pass


def test_indexed_and_nonindexed_dim():
    # One dim with index, one without
    idx = DummyIndex("x")
    obj = DummyObj(dims=("x", "y"), xindexes={"x": idx})
    indexers = {"x": 10, "y": 20}
    options = {}
    codeflash_output = group_indexers_by_index(obj, indexers, options)
    result = codeflash_output  # 4.65μs -> 4.96μs (6.26% slower)
    # Should have two groups: one for idx, one for None
    found_idx = False
    found_none = False
    for index, group in result:
        if index is idx:
            found_idx = True
        elif index is None:
            found_none = True
        else:
            pass


def test_empty_indexers():
    # No indexers
    obj = DummyObj(dims=("x", "y"))
    indexers = {}
    options = {}
    codeflash_output = group_indexers_by_index(obj, indexers, options)
    result = codeflash_output  # 2.26μs -> 3.09μs (26.9% slower)


def test_indexers_with_non_dim_key():
    # Indexer key not in dims or coords should raise KeyError
    obj = DummyObj(dims=("x",))
    indexers = {"foo": 1}
    options = {}
    with pytest.raises(KeyError) as e:
        group_indexers_by_index(
            obj, indexers, options
        )  # 4.22μs -> 4.88μs (13.7% slower)


# 2. Edge Test Cases


def test_dim_with_coord_but_no_index():
    # Key is in coords but not in xindexes: should raise KeyError
    obj = DummyObj(dims=("x",), coords={"x": [1, 2, 3]}, xindexes={})
    indexers = {"x": 1}
    options = {}
    with pytest.raises(KeyError) as e:
        group_indexers_by_index(
            obj, indexers, options
        )  # 2.88μs -> 3.19μs (9.70% slower)


def test_dim_with_options_but_no_index_or_coord():
    # Key is a dim, not in coords, not in xindexes, but options is non-empty: ValueError
    obj = DummyObj(dims=("x",))
    indexers = {"x": 1}
    options = {"method": "nearest"}
    with pytest.raises(ValueError) as e:
        group_indexers_by_index(
            obj, indexers, options
        )  # 4.50μs -> 4.91μs (8.30% slower)


def test_dim_with_neither_index_nor_coord_no_options():
    # Key is a dim, not in coords, not in xindexes, options empty: fallback to None
    obj = DummyObj(dims=("x",))
    indexers = {"x": 2}
    options = {}
    codeflash_output = group_indexers_by_index(obj, indexers, options)
    result = codeflash_output  # 4.18μs -> 4.42μs (5.40% slower)


def test_multiple_indexers_share_same_index():
    # Multiple keys mapped to the same Index object
    idx = DummyIndex("shared")
    obj = DummyObj(dims=("x", "y"), xindexes={"x": idx, "y": idx})
    indexers = {"x": 1, "y": 2}
    options = {}
    codeflash_output = group_indexers_by_index(obj, indexers, options)
    result = codeflash_output  # 4.24μs -> 4.74μs (10.6% slower)


def test_indexer_with_non_string_keys():
    # Indexer keys can be non-strings
    idx = DummyIndex(42)
    obj = DummyObj(dims=(42,), xindexes={42: idx})
    indexers = {42: 7}
    options = {}
    codeflash_output = group_indexers_by_index(obj, indexers, options)
    result = codeflash_output  # 3.63μs -> 4.12μs (12.1% slower)


def test_indexer_with_int_and_str_keys():
    # Both int and str keys present
    idx1 = DummyIndex("x")
    idx2 = DummyIndex(1)
    obj = DummyObj(dims=("x", 1), xindexes={"x": idx1, 1: idx2})
    indexers = {"x": 11, 1: 22}
    options = {}
    codeflash_output = group_indexers_by_index(obj, indexers, options)
    result = codeflash_output  # 4.50μs -> 4.84μs (7.10% slower)
    found = {id(idx1): False, id(idx2): False}
    for index, group in result:
        if index is idx1:
            found[id(idx1)] = True
        elif index is idx2:
            found[id(idx2)] = True
        else:
            pass


def test_indexer_with_empty_dim_tuple():
    # No dims, but indexers provided: should raise KeyError
    obj = DummyObj(dims=())
    indexers = {"x": 1}
    options = {}
    with pytest.raises(KeyError):
        group_indexers_by_index(
            obj, indexers, options
        )  # 3.39μs -> 3.92μs (13.6% slower)


def test_indexer_with_none_key():
    # None as a key: not in dims, not in coords, should raise KeyError
    obj = DummyObj(dims=("x",))
    indexers = {None: 1}
    options = {}
    with pytest.raises(KeyError):
        group_indexers_by_index(
            obj, indexers, options
        )  # 4.47μs -> 4.94μs (9.55% slower)


# 3. Large Scale Test Cases


def test_large_number_of_indexed_dims():
    # 1000 dims, all with indexes, all used in indexers
    N = 1000
    dims = tuple(f"dim{i}" for i in range(N))
    xindexes = {d: DummyIndex(d) for d in dims}
    obj = DummyObj(dims=dims, xindexes=xindexes)
    indexers = {d: i for i, d in enumerate(dims)}
    options = {}
    codeflash_output = group_indexers_by_index(obj, indexers, options)
    result = codeflash_output  # 296μs -> 296μs (0.071% slower)
    # Check that each group is correct
    for index, group in result:
        for k, v in group.items():
            pass


def test_large_number_of_nonindexed_dims():
    # 1000 dims, none with indexes, all used in indexers
    N = 1000
    dims = tuple(f"dim{i}" for i in range(N))
    obj = DummyObj(dims=dims)
    indexers = {d: i for i, d in enumerate(dims)}
    options = {}
    codeflash_output = group_indexers_by_index(obj, indexers, options)
    result = codeflash_output  # 2.66ms -> 144μs (1738% faster)
    index, group = result[0]


def test_large_mixed_indexed_and_nonindexed_dims():
    # 500 indexed, 500 non-indexed
    N = 1000
    dims = tuple(f"dim{i}" for i in range(N))
    xindexes = {d: DummyIndex(d) for d in dims[:500]}
    obj = DummyObj(dims=dims, xindexes=xindexes)
    indexers = {d: i for i, d in enumerate(dims)}
    options = {}
    codeflash_output = group_indexers_by_index(obj, indexers, options)
    result = codeflash_output  # 2.15ms -> 236μs (806% faster)
    # Check that all indexed dims are present
    indexed_keys = set(dims[:500])
    nonindexed_keys = set(dims[500:])
    indexed_found = set()
    nonindexed_found = set()
    for index, group in result:
        if index is None:
            nonindexed_found.update(group.keys())
        else:
            k = next(iter(group))
            indexed_found.add(k)


def test_large_number_of_indexers_with_shared_index():
    # 1000 dims, all point to the same index object
    N = 1000
    dims = tuple(f"dim{i}" for i in range(N))
    idx = DummyIndex("shared")
    xindexes = {d: idx for d in dims}
    obj = DummyObj(dims=dims, xindexes=xindexes)
    indexers = {d: i for i, d in enumerate(dims)}
    options = {}
    codeflash_output = group_indexers_by_index(obj, indexers, options)
    result = codeflash_output  # 130μs -> 141μs (7.73% slower)


def test_large_number_of_indexers_with_options_and_missing_index():
    # 1000 dims, none with indexes, options is non-empty: should raise ValueError
    N = 1000
    dims = tuple(f"dim{i}" for i in range(N))
    obj = DummyObj(dims=dims)
    indexers = {d: i for i, d in enumerate(dims)}
    options = {"method": "nearest"}
    with pytest.raises(ValueError):
        group_indexers_by_index(
            obj, indexers, options
        )  # 4.59μs -> 20.5μs (77.7% slower)


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

Timer unit: 1e-09 s

⏪ 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_core_indexing_group_indexers_by_index	1.68ms	1.42ms	18.7%✅

To edit these changes git checkout codeflash/optimize-group_indexers_by_index-mja0vow9 and push.