⚡️ Speed up method JournalStorageReplayResult.get_study by 18%

[codeflash-ai]

📄 18% (0.18x) speedup for JournalStorageReplayResult.get_study in optuna/storages/journal/_storage.py

⏱️ Runtime : 425 microseconds → 359 microseconds (best of 256 runs)

📝 Explanation and details

The optimization replaces the explicit membership check (if study_id not in self._studies) with a try/except pattern around direct dictionary access. This eliminates a double dictionary lookup - the original code checks for key existence first, then accesses the value, while the optimized version directly accesses the value and handles the KeyError exception when the key is missing.

Key Changes:

• Removed if study_id not in self._studies: check
• Wrapped return self._studies[study_id] in a try block
• Added except KeyError: to handle missing keys

Why This is Faster:
In Python, dictionary lookups are expensive operations. The original code performs two lookups for existing keys: one for the membership test (in operator) and another for value retrieval. The optimized version performs only one lookup for the common case where the key exists. When the key is missing, the exception handling overhead is minimal compared to the saved lookup.

Performance Profile:
The line profiler shows the optimization is most effective for successful lookups (existing study_ids), where we see 18% speedup overall. For missing keys, there's slight overhead due to exception handling (tests show 14-25% slower for KeyError cases), but this is typically the less common path in real applications.

Best Use Cases:
This optimization is particularly effective when:

• Most get_study calls are for existing study_ids (high cache hit rate)
• The application frequently accesses the same studies multiple times
• Large numbers of studies exist, making the double-lookup cost more significant

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 2082 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	✅ 2 Passed
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import pytest
from optuna.storages.journal._storage import JournalStorageReplayResult


# Minimal stubs for FrozenStudy and FrozenTrial, since optuna is not available.
class FrozenStudy:
    def __init__(self, study_id, name):
        self.study_id = study_id
        self.name = name

class FrozenTrial:
    def __init__(self, trial_id, value):
        self.trial_id = trial_id
        self.value = value

# Function to test (copied as per instructions)
NOT_FOUND_MSG = "Record does not exist."
from optuna.storages.journal._storage import JournalStorageReplayResult

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

# 1. Basic Test Cases

def test_get_study_returns_correct_study():
    # Test that get_study returns the correct study object for a valid study_id
    jsr = JournalStorageReplayResult("worker")
    study = FrozenStudy(1, "test_study")
    jsr._studies[1] = study
    codeflash_output = jsr.get_study(1); result = codeflash_output # 585ns -> 439ns (33.3% faster)

def test_get_study_multiple_studies():
    # Test that get_study works with multiple studies
    jsr = JournalStorageReplayResult("worker")
    study1 = FrozenStudy(1, "study1")
    study2 = FrozenStudy(2, "study2")
    jsr._studies[1] = study1
    jsr._studies[2] = study2
    codeflash_output = jsr.get_study(1) # 532ns -> 410ns (29.8% faster)
    codeflash_output = jsr.get_study(2) # 259ns -> 229ns (13.1% faster)

def test_get_study_with_zero_study_id():
    # Test that get_study works with a study_id of 0
    jsr = JournalStorageReplayResult("worker")
    study = FrozenStudy(0, "zero_study")
    jsr._studies[0] = study
    codeflash_output = jsr.get_study(0) # 527ns -> 420ns (25.5% faster)
    codeflash_output = jsr.get_study(0).name # 250ns -> 231ns (8.23% faster)

# 2. Edge Test Cases

def test_get_study_missing_study_raises_keyerror():
    # Test that get_study raises KeyError if study_id not present
    jsr = JournalStorageReplayResult("worker")
    with pytest.raises(KeyError) as excinfo:
        jsr.get_study(999) # 897ns -> 1.16μs (22.8% slower)

def test_get_study_with_negative_study_id():
    # Test that get_study works with negative study_id
    jsr = JournalStorageReplayResult("worker")
    study = FrozenStudy(-1, "negative_study")
    jsr._studies[-1] = study
    codeflash_output = jsr.get_study(-1) # 554ns -> 469ns (18.1% faster)
    codeflash_output = jsr.get_study(-1).name # 304ns -> 245ns (24.1% faster)


def test_get_study_with_large_integer_study_id():
    # Test with a very large integer study_id
    large_id = 2**62
    jsr = JournalStorageReplayResult("worker")
    study = FrozenStudy(large_id, "large_study")
    jsr._studies[large_id] = study
    codeflash_output = jsr.get_study(large_id) # 654ns -> 604ns (8.28% faster)
    codeflash_output = jsr.get_study(large_id).name # 238ns -> 209ns (13.9% faster)

def test_get_study_with_minimum_integer_study_id():
    # Test with minimum integer value (simulate 64-bit signed int)
    min_id = -2**63
    jsr = JournalStorageReplayResult("worker")
    study = FrozenStudy(min_id, "min_study")
    jsr._studies[min_id] = study
    codeflash_output = jsr.get_study(min_id) # 615ns -> 502ns (22.5% faster)
    codeflash_output = jsr.get_study(min_id).name # 236ns -> 200ns (18.0% faster)

def test_get_study_empty_studies_dict():
    # Test behavior when _studies dict is empty
    jsr = JournalStorageReplayResult("worker")
    with pytest.raises(KeyError) as excinfo:
        jsr.get_study(0) # 932ns -> 1.16μs (19.7% slower)

def test_get_study_mutation_safety():
    # Test that get_study does not mutate _studies dict
    jsr = JournalStorageReplayResult("worker")
    study = FrozenStudy(1, "study1")
    jsr._studies[1] = study
    before = dict(jsr._studies)
    jsr.get_study(1) # 558ns -> 494ns (13.0% faster)
    after = dict(jsr._studies)

# 3. Large Scale Test Cases

def test_get_study_large_number_of_studies():
    # Test with a large number of studies (1000)
    jsr = JournalStorageReplayResult("worker")
    for i in range(1000):
        jsr._studies[i] = FrozenStudy(i, f"study_{i}")
    # Check random sample
    codeflash_output = jsr.get_study(0).name # 661ns -> 512ns (29.1% faster)
    codeflash_output = jsr.get_study(999).name # 470ns -> 366ns (28.4% faster)
    codeflash_output = jsr.get_study(500).name # 222ns -> 180ns (23.3% faster)

def test_get_study_performance_on_large_dict():
    # Test that get_study is performant on large dicts
    jsr = JournalStorageReplayResult("worker")
    for i in range(1000):
        jsr._studies[i] = FrozenStudy(i, f"study_{i}")
    # Try to access all studies, should not raise
    for i in range(1000):
        codeflash_output = jsr.get_study(i); result = codeflash_output # 201μs -> 168μs (19.3% faster)

def test_get_study_missing_from_large_dict():
    # Test that missing study_id in large dict raises KeyError
    jsr = JournalStorageReplayResult("worker")
    for i in range(1000):
        jsr._studies[i] = FrozenStudy(i, f"study_{i}")
    with pytest.raises(KeyError) as excinfo:
        jsr.get_study(1001) # 1.13μs -> 1.39μs (18.7% slower)

def test_get_study_after_removal():
    # Test that get_study raises after study is removed
    jsr = JournalStorageReplayResult("worker")
    study = FrozenStudy(1, "study1")
    jsr._studies[1] = study
    codeflash_output = jsr.get_study(1) # 567ns -> 469ns (20.9% faster)
    del jsr._studies[1]
    with pytest.raises(KeyError) as excinfo:
        jsr.get_study(1) # 830ns -> 1.09μs (23.9% slower)

def test_get_study_with_sparse_ids():
    # Test with sparse study IDs (not sequential)
    jsr = JournalStorageReplayResult("worker")
    ids = [0, 10, 100, 999]
    for i in ids:
        jsr._studies[i] = FrozenStudy(i, f"sparse_{i}")
    for i in ids:
        codeflash_output = jsr.get_study(i).name # 1.18μs -> 956ns (23.6% faster)
    # Try an ID not present
    with pytest.raises(KeyError) as excinfo:
        jsr.get_study(5) # 686ns -> 911ns (24.7% 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  # used for our unit tests
from optuna.storages.journal._storage import JournalStorageReplayResult

NOT_FOUND_MSG = "Record does not exist."

class FrozenStudy:
    """Minimal stub of optuna.study._frozen.FrozenStudy for testing."""
    def __init__(self, study_id, name):
        self.study_id = study_id
        self.name = name

    def __eq__(self, other):
        # Equality based on study_id and name
        if not isinstance(other, FrozenStudy):
            return False
        return self.study_id == other.study_id and self.name == other.name

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

class FrozenTrial:
    """Minimal stub for optuna.trial.FrozenTrial for completeness."""
    pass
from optuna.storages.journal._storage import JournalStorageReplayResult

# unit tests

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

def test_get_study_returns_correct_study_for_existing_id():
    # Basic test: single study present
    storage = JournalStorageReplayResult("worker1")
    study = FrozenStudy(1, "studyA")
    storage._studies[1] = study
    codeflash_output = storage.get_study(1); result = codeflash_output # 611ns -> 499ns (22.4% faster)

def test_get_study_multiple_studies():
    # Multiple studies: verify correct selection
    storage = JournalStorageReplayResult("worker2")
    studies = {i: FrozenStudy(i, f"study{i}") for i in range(3)}
    storage._studies.update(studies)
    for i in range(3):
        codeflash_output = storage.get_study(i) # 1.08μs -> 900ns (20.4% faster)

def test_get_study_returns_object_identity():
    # Should return the exact same object stored
    storage = JournalStorageReplayResult("worker3")
    study = FrozenStudy(42, "main")
    storage._studies[42] = study
    codeflash_output = storage.get_study(42) # 524ns -> 440ns (19.1% faster)

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

def test_get_study_raises_keyerror_for_missing_id():
    # Edge: study_id not present
    storage = JournalStorageReplayResult("worker4")
    with pytest.raises(KeyError) as excinfo:
        storage.get_study(999) # 938ns -> 1.16μs (18.8% slower)

def test_get_study_empty_storage():
    # Edge: _studies dict is empty
    storage = JournalStorageReplayResult("worker5")
    with pytest.raises(KeyError) as excinfo:
        storage.get_study(0) # 919ns -> 1.15μs (20.1% slower)

def test_get_study_with_negative_id():
    # Edge: negative study_id
    storage = JournalStorageReplayResult("worker6")
    storage._studies[-1] = FrozenStudy(-1, "negstudy")
    codeflash_output = storage.get_study(-1).name # 574ns -> 486ns (18.1% faster)

def test_get_study_with_zero_id():
    # Edge: zero study_id
    storage = JournalStorageReplayResult("worker7")
    storage._studies[0] = FrozenStudy(0, "zerostudy")
    codeflash_output = storage.get_study(0).name # 550ns -> 443ns (24.2% faster)

def test_get_study_with_large_integer_id():
    # Edge: very large study_id
    large_id = 10**18
    storage = JournalStorageReplayResult("worker8")
    storage._studies[large_id] = FrozenStudy(large_id, "bigstudy")
    codeflash_output = storage.get_study(large_id).name # 535ns -> 471ns (13.6% faster)

def test_get_study_with_non_integer_id():
    # Edge: non-integer study_id (should not be present, so KeyError)
    storage = JournalStorageReplayResult("worker9")
    storage._studies[1] = FrozenStudy(1, "studyA")
    with pytest.raises(KeyError) as excinfo:
        storage.get_study("not_an_int") # 931ns -> 1.09μs (14.4% slower)

def test_get_study_with_none_id():
    # Edge: None as study_id
    storage = JournalStorageReplayResult("worker10")
    with pytest.raises(KeyError) as excinfo:
        storage.get_study(None) # 891ns -> 1.11μs (19.7% slower)


def test_get_study_many_studies():
    # Large scale: many studies
    storage = JournalStorageReplayResult("worker12")
    num_studies = 1000
    for i in range(num_studies):
        storage._studies[i] = FrozenStudy(i, f"study_{i}")
    # Check a few random IDs
    codeflash_output = storage.get_study(0).name # 761ns -> 658ns (15.7% faster)
    codeflash_output = storage.get_study(num_studies//2).name # 474ns -> 393ns (20.6% faster)
    codeflash_output = storage.get_study(num_studies-1).name # 217ns -> 178ns (21.9% faster)

def test_get_study_missing_id_among_many():
    # Large scale: missing ID among many
    storage = JournalStorageReplayResult("worker13")
    num_studies = 500
    for i in range(num_studies):
        storage._studies[i] = FrozenStudy(i, f"study_{i}")
    missing_id = num_studies + 1
    with pytest.raises(KeyError) as excinfo:
        storage.get_study(missing_id) # 1.02μs -> 1.24μs (17.7% slower)

def test_get_study_performance_large():
    # Large scale: access performance (no timing, just correctness)
    storage = JournalStorageReplayResult("worker14")
    num_studies = 999
    for i in range(num_studies):
        storage._studies[i] = FrozenStudy(i, f"study_{i}")
    # Access every study to ensure all are retrievable
    for i in range(num_studies):
        codeflash_output = storage.get_study(i).name # 195μs -> 161μs (20.9% faster)

def test_get_study_with_sparse_ids():
    # Large scale: sparse, non-contiguous IDs
    storage = JournalStorageReplayResult("worker15")
    ids = [0, 10, 100, 500, 999]
    for i in ids:
        storage._studies[i] = FrozenStudy(i, f"study_{i}")
    for i in ids:
        codeflash_output = storage.get_study(i).name # 1.47μs -> 1.27μs (15.4% faster)
    # Try a missing sparse ID
    with pytest.raises(KeyError):
        storage.get_study(101) # 783ns -> 1.04μs (24.4% slower)

# ----------- Additional Robustness Test Cases ------------

def test_get_study_mutation_safety():
    # If studies dict is mutated after storing, get_study should reflect changes
    storage = JournalStorageReplayResult("worker16")
    study = FrozenStudy(1, "original")
    storage._studies[1] = study
    codeflash_output = storage.get_study(1).name # 526ns -> 440ns (19.5% faster)
    # Update study in dict
    new_study = FrozenStudy(1, "updated")
    storage._studies[1] = new_study
    codeflash_output = storage.get_study(1).name # 274ns -> 252ns (8.73% faster)

def test_get_study_with_custom_object_id():
    # Edge: custom object as study_id (should not be present, KeyError)
    storage = JournalStorageReplayResult("worker17")
    class CustomID:
        pass
    cid = CustomID()
    with pytest.raises(KeyError):
        storage.get_study(cid) # 909ns -> 1.17μs (22.2% slower)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
from optuna.storages.journal._storage import JournalStorageReplayResult
import pytest

def test_JournalStorageReplayResult_get_study():
    with pytest.raises(KeyError, match="'Record\\ does\\ not\\ exist\\.'"):
        JournalStorageReplayResult.get_study(JournalStorageReplayResult(''), 0)

🔎 Concolic Coverage Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
codeflash_concolic_wou29s7s/tmp9ih4g_9m/test_concolic_coverage.py::test_JournalStorageReplayResult_get_study	1.06μs	1.23μs	-13.6%⚠️

To edit these changes git checkout codeflash/optimize-JournalStorageReplayResult.get_study-mhawk4tg and push.