⚡️ Speed up function group_lookup by 16%

[codeflash-ai]

📄 16% (0.16x) speedup for group_lookup in marimo/_utils/cell_matching.py

⏱️ Runtime : 1.03 milliseconds → 886 microseconds (best of 250 runs)

📝 Explanation and details

The optimized code achieves a 16% speedup by eliminating the overhead of Python's setdefault() method and replacing enumerate(zip()) with direct indexing.

Key optimizations:

1. Eliminated setdefault() overhead: The original code used lookup.setdefault(code, []).append((idx, cell_id)) which performs internal function calls and dictionary lookups even when the key exists. The optimized version uses explicit if code in lookup checks with direct assignment, reducing function call overhead.

2. Replaced enumerate(zip()) with range-based indexing: Instead of creating intermediate tuples through zip() and enumerate(), the optimization uses range(length) with direct sequence indexing. This avoids tuple creation overhead and leverages the efficient indexing that Sequence types provide.

3. Precomputed length calculation: Using min(len(ids), len(codes)) upfront maintains the original truncation behavior while avoiding repeated length checks during iteration.

Performance characteristics from tests:

• Large datasets see the biggest gains: Tests with 1000+ elements show 17-20% improvements, indicating the optimization scales well
• Small datasets have mixed results: Some small test cases show slight regressions due to the additional length calculation overhead
• Best for scenarios with many unique codes: The explicit key checking approach works particularly well when dictionary insertions are frequent

Impact on workloads:
Based on the function reference, group_lookup is called from _match_cell_ids_by_similarity, which appears to be part of a cell matching algorithm that likely runs during notebook operations. Since it's called twice per matching operation (for previous and next lookups), the 16% improvement could provide noticeable performance benefits in interactive notebook environments where cell matching occurs frequently.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 39 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

from __future__ import annotations

from collections.abc import Sequence

# imports
import pytest  # used for our unit tests
from marimo._utils.cell_matching import group_lookup

# unit tests

# 1. Basic Test Cases

def test_empty_inputs():
    # Both inputs empty: should return empty dict
    codeflash_output = group_lookup([], []) # 1.05μs -> 1.14μs (7.88% slower)

def test_single_element():
    # Single pair: should return dict with one key, one value
    codeflash_output = group_lookup([42], ["alpha"]) # 1.58μs -> 1.53μs (3.53% faster)

def test_multiple_unique_codes():
    # Each code is unique
    ids = [1, 2, 3]
    codes = ["a", "b", "c"]
    expected = {
        "a": [(0, 1)],
        "b": [(1, 2)],
        "c": [(2, 3)]
    }
    codeflash_output = group_lookup(ids, codes) # 1.97μs -> 1.71μs (15.8% faster)

def test_multiple_same_codes():
    # Multiple entries with same code
    ids = [10, 20, 30, 40]
    codes = ["x", "y", "x", "x"]
    expected = {
        "x": [(0, 10), (2, 30), (3, 40)],
        "y": [(1, 20)]
    }
    codeflash_output = group_lookup(ids, codes) # 1.97μs -> 2.02μs (2.13% slower)

def test_order_preserved():
    # Ensure order of appearance is preserved in output lists
    ids = [5, 6, 7, 8]
    codes = ["foo", "bar", "foo", "foo"]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 1.95μs -> 1.97μs (1.12% slower)

# 2. Edge Test Cases

def test_ids_longer_than_codes():
    # ids longer than codes: extra ids ignored
    ids = [1, 2, 3, 4]
    codes = ["a", "b"]
    expected = {
        "a": [(0, 1)],
        "b": [(1, 2)]
    }
    codeflash_output = group_lookup(ids, codes) # 1.65μs -> 1.63μs (1.23% faster)

def test_codes_longer_than_ids():
    # codes longer than ids: extra codes ignored
    ids = [10, 20]
    codes = ["x", "y", "z"]
    expected = {
        "x": [(0, 10)],
        "y": [(1, 20)]
    }
    codeflash_output = group_lookup(ids, codes) # 1.66μs -> 1.56μs (6.39% faster)


def test_empty_strings_as_codes():
    # Empty string as a code is valid
    ids = [100, 101]
    codes = ["", ""]
    expected = {"": [(0, 100), (1, 101)]}
    codeflash_output = group_lookup(ids, codes) # 2.00μs -> 2.14μs (6.55% slower)

def test_duplicate_ids_with_different_codes():
    # Same id appears with different codes
    ids = [1, 1, 2]
    codes = ["a", "b", "a"]
    expected = {
        "a": [(0, 1), (2, 2)],
        "b": [(1, 1)]
    }
    codeflash_output = group_lookup(ids, codes) # 2.12μs -> 2.00μs (5.59% faster)

def test_non_hashable_code():
    # Non-hashable code (e.g. list) should raise TypeError
    ids = [1]
    codes = [["not_hashable"]]
    with pytest.raises(TypeError):
        group_lookup(ids, codes) # 2.00μs -> 2.06μs (2.48% slower)

def test_ids_are_strings():
    # ids are strings (should work as CellId_t is generic)
    ids = ["cellA", "cellB"]
    codes = ["foo", "bar"]
    expected = {
        "foo": [(0, "cellA")],
        "bar": [(1, "cellB")]
    }
    codeflash_output = group_lookup(ids, codes) # 1.86μs -> 1.63μs (13.9% faster)

def test_all_codes_identical():
    # All codes are the same
    ids = [1, 2, 3, 4]
    codes = ["same"] * 4
    expected = {"same": [(0, 1), (1, 2), (2, 3), (3, 4)]}
    codeflash_output = group_lookup(ids, codes) # 2.01μs -> 2.06μs (2.47% slower)

def test_ids_are_none():
    # ids contains None
    ids = [None, 2]
    codes = ["a", "b"]
    expected = {
        "a": [(0, None)],
        "b": [(1, 2)]
    }
    codeflash_output = group_lookup(ids, codes) # 1.64μs -> 1.54μs (6.45% faster)


def test_large_unique_codes():
    # Large number of unique codes (1000)
    ids = list(range(1000))
    codes = [f"code_{i}" for i in range(1000)]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 132μs -> 110μs (20.1% faster)
    # Each code should map to exactly one tuple
    for i in range(1000):
        key = f"code_{i}"

def test_large_single_code():
    # Large number of ids with same code
    ids = list(range(1000))
    codes = ["x"] * 1000
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 81.8μs -> 68.6μs (19.2% faster)

def test_large_mixed_codes():
    # Large number of ids, alternating codes
    ids = list(range(1000))
    codes = ["even" if i % 2 == 0 else "odd" for i in range(1000)]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 82.3μs -> 68.6μs (19.9% faster)
    # Check even code group
    even_expected = [(i, i) for i in range(0, 1000, 2)]
    odd_expected = [(i, i) for i in range(1, 1000, 2)]

def test_large_ids_shorter_than_codes():
    # ids shorter than codes (999 vs 1000)
    ids = list(range(999))
    codes = ["x"] * 1000
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 80.1μs -> 68.4μs (17.2% faster)

def test_large_codes_shorter_than_ids():
    # codes shorter than ids (999 vs 1000)
    ids = list(range(1000))
    codes = ["y"] * 999
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 79.0μs -> 67.2μs (17.4% faster)
# 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

from collections.abc import Sequence
from typing import Any

# imports
import pytest  # used for our unit tests
from marimo._utils.cell_matching import group_lookup

# unit tests

# 1. Basic Test Cases

def test_basic_single_group():
    # All codes are the same, all ids are grouped together
    ids = [1, 2, 3]
    codes = ["a", "a", "a"]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 2.12μs -> 2.14μs (0.841% slower)

def test_basic_multiple_groups():
    # Codes split into two groups
    ids = [1, 2, 3, 4]
    codes = ["x", "y", "x", "y"]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 2.12μs -> 2.11μs (0.284% faster)

def test_basic_empty_inputs():
    # Both sequences empty
    ids = []
    codes = []
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 1.03μs -> 1.17μs (11.7% slower)

def test_basic_one_element():
    # Single element in both sequences
    ids = [42]
    codes = ["foo"]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 1.56μs -> 1.60μs (2.50% slower)

def test_basic_different_types_of_ids():
    # ids can be any type, not just int
    ids = ["cellA", "cellB", "cellC"]
    codes = ["alpha", "beta", "alpha"]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 1.99μs -> 1.83μs (8.62% faster)

# 2. Edge Test Cases

def test_edge_codes_with_empty_strings():
    # Codes contain empty string
    ids = [1, 2, 3]
    codes = ["", "", "nonempty"]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 2.00μs -> 1.98μs (0.707% faster)

def test_edge_ids_and_codes_length_mismatch():
    # Only the length of the shortest sequence is used
    ids = [1, 2, 3, 4, 5]
    codes = ["a", "b"]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 1.71μs -> 1.66μs (3.38% faster)

def test_edge_codes_with_duplicates_and_order():
    # Codes appear in non-contiguous order
    ids = [10, 20, 30, 40, 50]
    codes = ["foo", "bar", "foo", "bar", "foo"]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 2.14μs -> 2.07μs (3.24% faster)

def test_edge_codes_with_special_characters():
    # Codes contain special characters
    ids = [1, 2, 3]
    codes = ["$", "%", "$"]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 1.87μs -> 1.87μs (0.000% faster)

def test_edge_ids_are_none():
    # ids can be None
    ids = [None, None, 3]
    codes = ["a", "b", "a"]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 1.90μs -> 1.82μs (4.34% faster)

def test_edge_codes_are_empty_and_ids_are_empty():
    # Both sequences are empty
    ids = []
    codes = []
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 984ns -> 1.12μs (12.2% slower)

def test_edge_codes_are_all_unique():
    # Each code is unique, each group has one element
    ids = [1, 2, 3]
    codes = ["a", "b", "c"]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 2.00μs -> 1.77μs (12.4% faster)

def test_edge_codes_are_all_identical():
    # All codes identical, all ids in one group
    ids = [1, 2, 3, 4]
    codes = ["same", "same", "same", "same"]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 1.99μs -> 2.06μs (3.55% slower)

def test_edge_ids_and_codes_are_tuples():
    # ids and codes can be tuples (as long as codes are strings)
    ids = [(1, "a"), (2, "b"), (3, "c")]
    codes = ["group1", "group2", "group1"]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 1.92μs -> 1.82μs (5.45% faster)

def test_edge_codes_are_empty_strings_only():
    # All codes are empty string
    ids = [1, 2, 3]
    codes = ["", "", ""]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 1.80μs -> 1.92μs (6.09% slower)

# 3. Large Scale Test Cases

def test_large_scale_many_groups():
    # 1000 ids, 10 groups, codes cycle through group names
    n = 1000
    ids = list(range(n))
    group_names = [f"group{i}" for i in range(10)]
    codes = [group_names[i % 10] for i in range(n)]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 95.2μs -> 83.0μs (14.7% faster)
    for i, name in enumerate(group_names):
        # Each group should have 100 members
        group = result[name]
        # Check correct indices and ids
        for idx, (index, cell_id) in enumerate(group):
            pass

def test_large_scale_all_unique_codes():
    # 1000 ids, each code is unique
    n = 1000
    ids = list(range(n))
    codes = [str(i) for i in range(n)]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 123μs -> 104μs (18.1% faster)
    for i in range(n):
        pass

def test_large_scale_all_same_code():
    # 1000 ids, all codes the same
    n = 1000
    ids = list(range(n))
    codes = ["group"] * n
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 81.3μs -> 68.3μs (19.1% faster)
    for idx, (index, cell_id) in enumerate(result["group"]):
        pass

def test_large_scale_length_mismatch():
    # ids longer than codes, only shortest length used
    ids = list(range(1000))
    codes = ["a"] * 500
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 40.6μs -> 34.4μs (18.0% faster)

def test_large_scale_ids_are_strings():
    # ids are strings, codes cycle through 5 groups
    n = 1000
    ids = [f"id_{i}" for i in range(n)]
    codes = [f"group_{i%5}" for i in range(n)]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 99.4μs -> 93.2μs (6.59% faster)
    for i in range(5):
        group = result[f"group_{i}"]
        for idx, (index, cell_id) in enumerate(group):
            pass

def test_large_scale_codes_with_special_characters():
    # Codes contain special characters and are repeated
    n = 1000
    ids = list(range(n))
    codes = ["@!#" if i % 2 == 0 else "&*%" for i in range(n)]
    codeflash_output = group_lookup(ids, codes); result = codeflash_output # 82.4μs -> 70.0μs (17.6% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-group_lookup-mhwqeb78 and push.