⚡️ Speed up function fetch_all_users by 868%

[codeflash-ai-dev]

📄 868% (8.68x) speedup for fetch_all_users in src/asynchrony/various.py

⏱️ Runtime : 200 milliseconds → 20.7 milliseconds (best of 250 runs)

📝 Explanation and details

The optimized code achieves an 868% speedup by replacing sequential async operations with concurrent execution using asyncio.gather().

Key optimization: The original code processes user fetches sequentially in a loop - each await fetch_user(user_id) blocks until that individual operation completes before starting the next one. This means for N users, the total time is roughly N × 0.0001 seconds (the sleep duration).

The optimized version creates all coroutines upfront with a list comprehension, then uses asyncio.gather(*coros) to execute them concurrently. All asyncio.sleep(0.0001) calls now run in parallel, so the total time becomes approximately 0.0001 seconds regardless of the number of users.

Performance impact:

• Runtime improvement: From 200ms to 20.7ms (868% faster)
• Throughput improvement: From 2,907 to 14,250 operations/second (390% increase)

Why this works: The line profiler shows the original code spent 96.8% of its time in the await fetch_user(user_id) line within the sequential loop. The optimized version eliminates this bottleneck by allowing all I/O operations to overlap.

Test case benefits: The optimization is most effective for larger user lists (the throughput tests with 50-500 users show the greatest gains). For single users or empty lists, the improvement is minimal since there's no concurrency benefit. The concurrent test cases demonstrate that the optimization maintains correctness while dramatically improving performance when processing multiple users simultaneously.

Behavioral preservation: The function maintains identical output ordering, error handling, and return types - only the execution strategy changes from sequential to concurrent.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 57 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import asyncio  # used to run async functions

import pytest  # used for our unit tests
from src.asynchrony.various import fetch_all_users

# unit tests

# ----------------------
# 1. Basic Test Cases
# ----------------------

@pytest.mark.asyncio
async def test_fetch_all_users_empty_list():
    """Test with an empty list: should return an empty list."""
    result = await fetch_all_users([])

@pytest.mark.asyncio
async def test_fetch_all_users_single_user():
    """Test with a single user ID."""
    result = await fetch_all_users([42])

@pytest.mark.asyncio
async def test_fetch_all_users_multiple_users():
    """Test with multiple user IDs."""
    user_ids = [1, 2, 3]
    result = await fetch_all_users(user_ids)
    expected = [
        {"id": 1, "name": "User1"},
        {"id": 2, "name": "User2"},
        {"id": 3, "name": "User3"},
    ]

@pytest.mark.asyncio
async def test_fetch_all_users_duplicate_ids():
    """Test with duplicate user IDs."""
    user_ids = [5, 5, 7]
    result = await fetch_all_users(user_ids)
    expected = [
        {"id": 5, "name": "User5"},
        {"id": 5, "name": "User5"},
        {"id": 7, "name": "User7"},
    ]

# ----------------------
# 2. Edge Test Cases
# ----------------------

@pytest.mark.asyncio
async def test_fetch_all_users_negative_and_zero_ids():
    """Test with zero and negative user IDs."""
    user_ids = [0, -1, -99]
    result = await fetch_all_users(user_ids)
    expected = [
        {"id": 0, "name": "User0"},
        {"id": -1, "name": "User-1"},
        {"id": -99, "name": "User-99"},
    ]

@pytest.mark.asyncio
async def test_fetch_all_users_non_integer_ids():
    """Test with non-integer IDs should raise TypeError from fetch_user."""
    # fetch_user expects int, so passing a string should raise TypeError when formatting
    user_ids = ["abc", 1.5, None]
    with pytest.raises((TypeError, ValueError)):
        await fetch_all_users(user_ids)

@pytest.mark.asyncio
async def test_fetch_all_users_concurrent_invocations():
    """Test concurrent execution of fetch_all_users (separate coroutines)."""
    # Each call should be isolated and return correct results
    user_ids1 = [1, 2]
    user_ids2 = [3, 4]
    results = await asyncio.gather(
        fetch_all_users(user_ids1),
        fetch_all_users(user_ids2)
    )

@pytest.mark.asyncio
async def test_fetch_all_users_large_id_values():
    """Test with very large user IDs."""
    user_ids = [999999999, 2**63-1]
    result = await fetch_all_users(user_ids)
    expected = [
        {"id": 999999999, "name": "User999999999"},
        {"id": 2**63-1, "name": f"User{2**63-1}"},
    ]

# ----------------------
# 3. Large Scale Test Cases
# ----------------------

@pytest.mark.asyncio
async def test_fetch_all_users_large_list():
    """Test with a large list of user IDs (performance and correctness)."""
    user_ids = list(range(100))  # 100 is large enough for a unit test
    result = await fetch_all_users(user_ids)
    for i, user in enumerate(result):
        pass

@pytest.mark.asyncio
async def test_fetch_all_users_concurrent_large_lists():
    """Test concurrent execution with multiple large lists."""
    user_ids1 = list(range(50))
    user_ids2 = list(range(50, 100))
    results = await asyncio.gather(
        fetch_all_users(user_ids1),
        fetch_all_users(user_ids2)
    )

# ----------------------
# 4. Throughput Test Cases
# ----------------------

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_small_load():
    """Throughput: Test performance with a small number of users."""
    user_ids = list(range(10))
    result = await fetch_all_users(user_ids)

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_medium_load():
    """Throughput: Test performance with a medium number of users."""
    user_ids = list(range(50))
    result = await fetch_all_users(user_ids)

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_high_load():
    """Throughput: Test performance with a high number of users (up to 200)."""
    user_ids = list(range(200))
    result = await fetch_all_users(user_ids)

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_many_concurrent_small_loads():
    """Throughput: Test many concurrent small loads."""
    all_ids = [list(range(i, i+5)) for i in range(0, 50, 5)]  # 10 batches of 5
    results = await asyncio.gather(*(fetch_all_users(ids) for ids in all_ids))
    for idx, batch in enumerate(results):
        for j, user in enumerate(batch):
            expected_id = idx * 5 + j

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_sustained_execution():
    """Throughput: Test repeated sustained execution patterns."""
    user_ids = [10, 20, 30]
    for _ in range(10):  # run 10 times in a row
        result = await fetch_all_users(user_ids)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import asyncio  # used to run async functions

import pytest  # used for our unit tests
from src.asynchrony.various import fetch_all_users

# unit tests

# 1. Basic Test Cases

@pytest.mark.asyncio
async def test_fetch_all_users_empty_list():
    # Test with an empty list; should return an empty list
    result = await fetch_all_users([])

@pytest.mark.asyncio
async def test_fetch_all_users_single_user():
    # Test with a single user_id
    result = await fetch_all_users([42])

@pytest.mark.asyncio
async def test_fetch_all_users_multiple_users():
    # Test with multiple user_ids
    user_ids = [1, 2, 3]
    expected = [
        {"id": 1, "name": "User1"},
        {"id": 2, "name": "User2"},
        {"id": 3, "name": "User3"}
    ]
    result = await fetch_all_users(user_ids)

# 2. Edge Test Cases

@pytest.mark.asyncio
async def test_fetch_all_users_duplicate_user_ids():
    # Test with duplicate user_ids
    user_ids = [5, 5, 5]
    expected = [
        {"id": 5, "name": "User5"},
        {"id": 5, "name": "User5"},
        {"id": 5, "name": "User5"}
    ]
    result = await fetch_all_users(user_ids)

@pytest.mark.asyncio
async def test_fetch_all_users_negative_and_zero_user_ids():
    # Test with negative and zero user_ids
    user_ids = [-1, 0, 1]
    expected = [
        {"id": -1, "name": "User-1"},
        {"id": 0, "name": "User0"},
        {"id": 1, "name": "User1"}
    ]
    result = await fetch_all_users(user_ids)

@pytest.mark.asyncio
async def test_fetch_all_users_large_user_id():
    # Test with a very large user_id value
    user_ids = [999999999]
    expected = [{"id": 999999999, "name": "User999999999"}]
    result = await fetch_all_users(user_ids)

@pytest.mark.asyncio
async def test_fetch_all_users_concurrent_invocation():
    # Test concurrent invocation of fetch_all_users with different user_ids
    user_ids_1 = [1, 2]
    user_ids_2 = [3, 4]
    results = await asyncio.gather(
        fetch_all_users(user_ids_1),
        fetch_all_users(user_ids_2)
    )

@pytest.mark.asyncio
async def test_fetch_all_users_non_integer_user_ids():
    # Test with non-integer user_ids; should raise a TypeError from fetch_user
    user_ids = ["a", None, 3.14]
    try:
        await fetch_all_users(user_ids)
    except TypeError:
        pass  # Expected behavior

# 3. Large Scale Test Cases

@pytest.mark.asyncio
async def test_fetch_all_users_large_list():
    # Test with a large list of user_ids (but <1000 for performance)
    user_ids = list(range(100))
    expected = [{"id": i, "name": f"User{i}"} for i in user_ids]
    result = await fetch_all_users(user_ids)

@pytest.mark.asyncio
async def test_fetch_all_users_concurrent_large_lists():
    # Test concurrent execution with multiple large lists
    user_ids_1 = list(range(50))
    user_ids_2 = list(range(50, 100))
    results = await asyncio.gather(
        fetch_all_users(user_ids_1),
        fetch_all_users(user_ids_2)
    )
    expected_1 = [{"id": i, "name": f"User{i}"} for i in user_ids_1]
    expected_2 = [{"id": i, "name": f"User{i}"} for i in user_ids_2]

# 4. Throughput Test Cases

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_small_load():
    # Throughput test: small load
    user_ids = list(range(10))
    result = await fetch_all_users(user_ids)
    for i, user in enumerate(result):
        pass

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_medium_load():
    # Throughput test: medium load
    user_ids = list(range(100))
    result = await fetch_all_users(user_ids)
    for i, user in enumerate(result):
        pass

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_concurrent_medium_load():
    # Throughput test: concurrent medium load
    user_ids_list = [list(range(20)), list(range(20, 40)), list(range(40, 60))]
    results = await asyncio.gather(
        *(fetch_all_users(ids) for ids in user_ids_list)
    )
    for idx, user_ids in enumerate(user_ids_list):
        expected = [{"id": i, "name": f"User{i}"} for i in user_ids]

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_high_load():
    # Throughput test: high load (but <1000 for performance)
    user_ids = list(range(500))
    result = await fetch_all_users(user_ids)
    for i, user in enumerate(result):
        pass

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_sustained_execution():
    # Throughput test: sustained execution pattern (multiple sequential calls)
    user_ids_batches = [list(range(i, i+10)) for i in range(0, 50, 10)]
    for batch in user_ids_batches:
        result = await fetch_all_users(batch)
        expected = [{"id": i, "name": f"User{i}"} for i in batch]
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from src.asynchrony.various import fetch_all_users

To edit these changes git checkout codeflash/optimize-fetch_all_users-mhq7qd2y and push.