⚡️ Speed up method TransformsContainer._get_next_transformations by 100%

[codeflash-ai]

📄 100% (1.00x) speedup for TransformsContainer._get_next_transformations in marimo/_plugins/ui/_impl/dataframes/transforms/apply.py

⏱️ Runtime : 568 microseconds → 284 microseconds (best of 250 runs)

📝 Explanation and details

The optimization replaces a manual element-by-element comparison loop with Python's built-in list comparison using slice notation.

Key Changes:

• Removed the explicit for loop that compared self._transforms[i] with transforms.transforms[i]
• Replaced it with a single comparison: self._transforms == transforms.transforms[:len(self._transforms)]

Why This Is Faster:
Python's native list equality comparison (==) is implemented in C and highly optimized for comparing sequences. The original code performed individual element comparisons in Python bytecode, which involves:

• Loop overhead with enumerate()
• Individual list indexing operations (transforms.transforms[i])
• Python-level equality checks for each element

The optimized version leverages:

• Fast slice creation (transforms.transforms[:len(self._transforms)])
• Native C-level list comparison that processes elements in batches
• Elimination of Python loop overhead

Performance Impact:
The line profiler shows the optimization eliminates the expensive loop (originally 76.3% of execution time) and reduces total _is_superset time from 3.16ms to 1.08ms - a 66% improvement. This translates to an overall 100% speedup (568μs → 284μs).

Test Case Benefits:
The optimization performs particularly well on:

• Large-scale comparisons (500-800 elements): 63-73% faster
• Identical transform lists: 2610% faster for 800 elements
• Prefix matching scenarios: 15-73% faster
• Early mismatch detection: 22-65% faster

The native comparison can short-circuit more efficiently when differences are found, making it especially effective for both matching and non-matching scenarios across all scales.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 68 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

from typing import Generic, TypeVar

# imports
import pytest
from marimo._plugins.ui._impl.dataframes.transforms.apply import \
    TransformsContainer


# Dummy Transform, TransformHandler, and Transformations for testing
class Transform:
    """A simple transform class for testing."""
    def __init__(self, name, params=None):
        self.name = name
        self.params = params or {}

    def __eq__(self, other):
        return (
            isinstance(other, Transform)
            and self.name == other.name
            and self.params == other.params
        )

    def __repr__(self):
        return f"Transform(name={self.name!r}, params={self.params!r})"

class TransformHandler:
    """Dummy handler, not used in tests."""
    pass

class Transformations:
    """Container for a list of transforms."""
    def __init__(self, transforms):
        self.transforms = transforms

    def __eq__(self, other):
        return (
            isinstance(other, Transformations)
            and self.transforms == other.transforms
        )

    def __repr__(self):
        return f"Transformations({self.transforms!r})"

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

# 1. Basic Test Cases

def test_empty_existing_and_new_transforms():
    # Both existing and new transforms are empty
    container = TransformsContainer(df=123, handler=TransformHandler())
    new_transforms = Transformations([])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 710ns -> 711ns (0.141% slower)

def test_empty_existing_nonempty_new_transforms():
    # Existing transforms empty, new transforms non-empty
    container = TransformsContainer(df=123, handler=TransformHandler())
    new_transforms = Transformations([Transform("A")])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 571ns -> 569ns (0.351% faster)

def test_existing_subset_of_new_transforms():
    # Existing transforms are a prefix of new transforms
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = [Transform("A"), Transform("B")]
    new_transforms = Transformations([Transform("A"), Transform("B"), Transform("C"), Transform("D")])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 3.23μs -> 2.76μs (17.2% faster)

def test_existing_equals_new_transforms():
    # Existing transforms are exactly the same as new transforms
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = [Transform("X"), Transform("Y")]
    new_transforms = Transformations([Transform("X"), Transform("Y")])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 2.57μs -> 2.23μs (15.3% faster)

def test_existing_not_prefix_of_new_transforms():
    # Existing transforms differ from new transforms (not a prefix)
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = [Transform("A"), Transform("B")]
    new_transforms = Transformations([Transform("A"), Transform("C"), Transform("D")])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 2.13μs -> 1.62μs (31.4% faster)

# 2. Edge Test Cases

def test_existing_longer_than_new_transforms():
    # Existing transforms longer than new transforms
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = [Transform("A"), Transform("B"), Transform("C")]
    new_transforms = Transformations([Transform("A"), Transform("B")])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 835ns -> 808ns (3.34% faster)

def test_existing_empty_new_empty():
    # Both lists empty, edge case
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = []
    new_transforms = Transformations([])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 558ns -> 543ns (2.76% faster)

def test_existing_nonempty_new_empty():
    # Existing non-empty, new empty
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = [Transform("A")]
    new_transforms = Transformations([])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 765ns -> 807ns (5.20% slower)

def test_existing_and_new_transform_with_params():
    # Transforms with parameters, equality should check params too
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = [Transform("A", {"x": 1}), Transform("B", {"y": 2})]
    # Same names, different params
    new_transforms = Transformations([Transform("A", {"x": 1}), Transform("B", {"y": 3}), Transform("C", {"z": 4})])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 2.54μs -> 1.90μs (33.9% faster)

def test_existing_and_new_transform_with_params_equal():
    # Transforms with parameters, exactly equal
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = [Transform("A", {"x": 1}), Transform("B", {"y": 2})]
    new_transforms = Transformations([Transform("A", {"x": 1}), Transform("B", {"y": 2}), Transform("C", {"z": 4})])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 2.91μs -> 2.47μs (17.6% faster)

def test_existing_and_new_transform_with_different_types():
    # Transforms with same name but different types in params
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = [Transform("A", {"x": 1})]
    new_transforms = Transformations([Transform("A", {"x": "1"}), Transform("B")])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 1.98μs -> 1.52μs (29.6% faster)

def test_existing_and_new_transform_with_missing_params():
    # Transforms with missing params
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = [Transform("A")]
    new_transforms = Transformations([Transform("A", {"x": 1}), Transform("B")])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 1.82μs -> 1.47μs (23.9% faster)

# 3. Large Scale Test Cases

def test_large_scale_prefix():
    # Large number of transforms, existing is a prefix
    N = 500
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = [Transform(str(i)) for i in range(N)]
    new_transforms = Transformations([Transform(str(i)) for i in range(N+10)])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 75.6μs -> 44.0μs (71.7% faster)

def test_large_scale_exact():
    # Large number of transforms, existing equals new
    N = 800
    container = TransformsContainer(df=123, handler=TransformHandler())
    transforms_list = [Transform(str(i)) for i in range(N)]
    container._transforms = transforms_list[:]
    new_transforms = Transformations(transforms_list[:])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 114μs -> 4.24μs (2610% faster)

def test_large_scale_not_superset():
    # Large number of transforms, but not a superset (one mismatch in prefix)
    N = 700
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = [Transform(str(i)) for i in range(N)]
    # Introduce a mismatch at position 200
    new_transforms_list = [Transform(str(i)) for i in range(N+5)]
    new_transforms_list[200] = Transform("mismatch")
    new_transforms = Transformations(new_transforms_list)
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 31.3μs -> 19.9μs (57.8% faster)

def test_large_scale_existing_longer():
    # Existing transforms longer than new transforms
    N = 300
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = [Transform(str(i)) for i in range(N+10)]
    new_transforms = Transformations([Transform(str(i)) for i in range(N)])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 991ns -> 984ns (0.711% faster)

def test_large_scale_params():
    # Large scale with params
    N = 400
    container = TransformsContainer(df=123, handler=TransformHandler())
    container._transforms = [Transform(str(i), {"x": i}) for i in range(N)]
    new_transforms = Transformations([Transform(str(i), {"x": i}) for i in range(N+5)])
    codeflash_output = container._get_next_transformations(new_transforms); result = codeflash_output # 63.3μs -> 38.9μs (63.0% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from typing import Generic, TypeVar

# imports
import pytest
from marimo._plugins.ui._impl.dataframes.transforms.apply import \
    TransformsContainer


# Minimal stubs for required types
class Transform:
    def __init__(self, name, params=None):
        self.name = name
        self.params = params or {}

    def __eq__(self, other):
        return (
            isinstance(other, Transform)
            and self.name == other.name
            and self.params == other.params
        )

    def __repr__(self):
        return f"Transform({self.name!r}, {self.params!r})"

class Transformations:
    def __init__(self, transforms):
        self.transforms = transforms

    def __eq__(self, other):
        return (
            isinstance(other, Transformations)
            and self.transforms == other.transforms
        )

    def __repr__(self):
        return f"Transformations({self.transforms!r})"

class TransformHandler:
    pass  # Not used in the tested method

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

# Basic Test Cases

def test_empty_existing_and_empty_new():
    # Both existing and new transformations are empty
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = []
    new = Transformations([])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 586ns -> 607ns (3.46% slower)

def test_empty_existing_and_nonempty_new():
    # No existing transforms, new has some
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = []
    new = Transformations([Transform("A")])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 547ns -> 553ns (1.08% slower)

def test_existing_subset_of_new():
    # Existing is a prefix of new
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A"), Transform("B")]
    new = Transformations([Transform("A"), Transform("B"), Transform("C"), Transform("D")])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 3.02μs -> 2.61μs (15.6% faster)

def test_existing_equal_to_new():
    # Existing and new are exactly the same
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A"), Transform("B")]
    new = Transformations([Transform("A"), Transform("B")])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 2.60μs -> 2.13μs (22.0% faster)

def test_existing_not_prefix_of_new():
    # Existing and new differ at some index
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A"), Transform("B")]
    new = Transformations([Transform("A"), Transform("C"), Transform("D")])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 2.18μs -> 1.58μs (37.5% faster)

def test_existing_longer_than_new():
    # Existing is longer than new
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A"), Transform("B"), Transform("C")]
    new = Transformations([Transform("A"), Transform("B")])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 833ns -> 794ns (4.91% faster)

def test_existing_empty_new_nonempty():
    # Existing empty, new nonempty
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = []
    new = Transformations([Transform("X")])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 523ns -> 520ns (0.577% faster)

# Edge Test Cases

def test_transform_params_affect_equality():
    # Transforms with same name but different params are not equal
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A", {"x": 1})]
    new = Transformations([Transform("A", {"x": 2}), Transform("B")])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 2.05μs -> 1.68μs (22.5% faster)

def test_transformations_with_none_params():
    # Transforms with None params are equal to empty dict params
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A", None)]
    new = Transformations([Transform("A", {})])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 2.56μs -> 2.16μs (18.7% faster)

def test_transformations_with_different_types():
    # Transforms with same name but different param types
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A", {"x": 1})]
    new = Transformations([Transform("A", {"x": "1"})])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 1.99μs -> 1.58μs (26.1% faster)

def test_transformations_with_duplicate_transforms():
    # Duplicated transforms in new after prefix
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A")]
    new = Transformations([Transform("A"), Transform("A"), Transform("B")])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 2.37μs -> 2.06μs (15.1% faster)

def test_transformations_with_non_hashable_params():
    # Params with lists (non-hashable but eq works)
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform("A", {"x": [1,2]})]
    new = Transformations([Transform("A", {"x": [1,2]}), Transform("B")])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 2.54μs -> 2.10μs (21.2% faster)

# Large Scale Test Cases

def test_large_scale_prefix():
    # Existing is a large prefix of new
    N = 500
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform(str(i)) for i in range(N)]
    new = Transformations([Transform(str(i)) for i in range(N+10)])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 75.4μs -> 43.5μs (73.2% faster)

def test_large_scale_identical():
    # Both lists are large and identical
    N = 800
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform(str(i)) for i in range(N)]
    new = Transformations([Transform(str(i)) for i in range(N)])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 117μs -> 67.9μs (72.8% faster)

def test_large_scale_mismatch_in_middle():
    # Mismatch at some index in a large list
    N = 600
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform(str(i)) for i in range(N)]
    # Change one transform in the middle
    new_transforms = [Transform(str(i)) for i in range(N)]
    new_transforms[N//2] = Transform("DIFFERENT")
    new = Transformations(new_transforms)
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 45.2μs -> 27.4μs (65.2% faster)

def test_large_scale_existing_longer():
    # Existing longer than new, should not be superset
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = [Transform(str(i)) for i in range(300)]
    new = Transformations([Transform(str(i)) for i in range(200)])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 934ns -> 893ns (4.59% faster)

def test_large_scale_all_new():
    # Existing is empty, new is large
    tc = TransformsContainer(df=None, handler=TransformHandler())
    tc._transforms = []
    new = Transformations([Transform(str(i)) for i in range(999)])
    codeflash_output = tc._get_next_transformations(new); result = codeflash_output # 606ns -> 611ns (0.818% slower)
# 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-TransformsContainer._get_next_transformations-mhwvaq0e and push.