⚡️ Speed up function fetch_all_users by 786%

[codeflash-ai-dev]

📄 786% (7.86x) speedup for fetch_all_users in src/asynchrony/various.py

⏱️ Runtime : 302 milliseconds → 34.1 milliseconds (best of 250 runs)

📝 Explanation and details

The optimization replaces sequential async execution with concurrent execution using asyncio.gather().

Key Change:

• Original: Fetches users one-by-one in a loop with await fetch_user(user_id), blocking until each completes
• Optimized: Uses asyncio.gather(*(fetch_user(user_id) for user_id in user_ids)) to launch all fetch operations concurrently

Why This Creates a Speedup:
The original code suffers from "false serialization" - it waits for each 0.0001-second database call to complete before starting the next one. With N users, total time is roughly N × 0.0001 seconds. The optimized version launches all fetch operations simultaneously, so total time becomes approximately max(fetch_times) ≈ 0.0001 seconds regardless of list size.

Performance Impact:

• Runtime improvement: 785% speedup (302ms → 34.1ms)
• Throughput improvement: 558% increase (1,862 → 12,250 operations/second)

The line profiler shows the optimization eliminates the expensive sequential loop overhead - the original fetch_all_users spent 96.3% of time waiting on individual fetch calls, while the optimized version completes in a single concurrent operation.

Test Case Performance:
The optimization excels with larger datasets - test cases with 100-500 users show the most dramatic improvements since they maximize the concurrency benefit. Small lists (1-10 users) still benefit but see smaller gains due to the fixed asyncio.sleep overhead.

This pattern is particularly valuable for I/O-bound operations like database queries, API calls, or file operations where the underlying operations can run independently.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 49 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 user_ids 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_basic_async_behavior():
    # Test that the function returns a coroutine and can be awaited
    codeflash_output = fetch_all_users([7, 8]); coroutine = codeflash_output
    result = await coroutine

# 2. Edge Test Cases

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

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

@pytest.mark.asyncio
async def test_fetch_all_users_non_integer_ids():
    # Test with non-integer user_ids (should raise TypeError)
    with pytest.raises(TypeError):
        await fetch_all_users(["a", 2.5, None])

@pytest.mark.asyncio
async def test_fetch_all_users_concurrent_execution():
    # Test concurrent calls to fetch_all_users with different inputs
    user_ids_1 = [10, 11]
    user_ids_2 = [20, 21]
    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_empty_string_id():
    # Test with an empty string as user_id (should raise TypeError)
    with pytest.raises(TypeError):
        await fetch_all_users([""])

# 3. Large Scale Test Cases

@pytest.mark.asyncio
async def test_fetch_all_users_large_list():
    # Test with a large list of user_ids (up to 500)
    user_ids = list(range(500))
    result = await fetch_all_users(user_ids)
    expected = [{"id": i, "name": f"User{i}"} for i in user_ids]

@pytest.mark.asyncio
async def test_fetch_all_users_concurrent_large_lists():
    # Test concurrent execution with large lists
    user_ids_1 = list(range(100, 200))
    user_ids_2 = list(range(200, 300))
    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 (10 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: medium load (100 users)
    user_ids = list(range(100))
    result = await fetch_all_users(user_ids)

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_high_volume():
    # Throughput test: high volume (500 users)
    user_ids = list(range(500))
    result = await fetch_all_users(user_ids)

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_sustained_concurrent_load():
    # Throughput test: sustained concurrent execution with multiple small batches
    batches = [list(range(i, i+10)) for i in range(0, 100, 10)]  # 10 batches of 10 users
    results = await asyncio.gather(*(fetch_all_users(batch) for batch in batches))
    for idx, batch_result in enumerate(results):
        expected = [{"id": i, "name": f"User{i}"} for i in range(idx*10, idx*10+10)]
# 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 fetch_all_users with an empty list returns an empty list."""
    result = await fetch_all_users([])

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

@pytest.mark.asyncio
async def test_fetch_all_users_multiple_users():
    """Test fetch_all_users 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)

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

# 2. Edge Test Cases

@pytest.mark.asyncio
async def test_fetch_all_users_non_sequential_ids():
    """Test fetch_all_users with non-sequential and negative user_ids."""
    user_ids = [10, -1, 999]
    expected = [
        {"id": 10, "name": "User10"},
        {"id": -1, "name": "User-1"},
        {"id": 999, "name": "User999"},
    ]
    result = await fetch_all_users(user_ids)

@pytest.mark.asyncio
async def test_fetch_all_users_concurrent_calls():
    """Test concurrent execution of fetch_all_users with different inputs."""
    ids1 = [1, 2]
    ids2 = [3, 4]
    # Run two fetch_all_users concurrently
    results = await asyncio.gather(
        fetch_all_users(ids1),
        fetch_all_users(ids2)
    )

@pytest.mark.asyncio
async def test_fetch_all_users_large_id_values():
    """Test fetch_all_users with very large user_ids."""
    user_ids = [2**30, 2**31-1]
    expected = [
        {"id": 2**30, "name": f"User{2**30}"},
        {"id": 2**31-1, "name": f"User{2**31-1}"},
    ]
    result = await fetch_all_users(user_ids)

@pytest.mark.asyncio
async def test_fetch_all_users_empty_dict_structure():
    """Test that fetch_all_users always returns a list of dicts, even for empty input."""
    result = await fetch_all_users([])

# 3. Large Scale Test Cases

@pytest.mark.asyncio
async def test_fetch_all_users_large_list():
    """Test fetch_all_users with a large list of user_ids (but <1000 for speed)."""
    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 of fetch_all_users with large lists."""
    ids1 = list(range(50))
    ids2 = list(range(50, 100))
    results = await asyncio.gather(
        fetch_all_users(ids1),
        fetch_all_users(ids2)
    )
    expected1 = [{"id": i, "name": f"User{i}"} for i in ids1]
    expected2 = [{"id": i, "name": f"User{i}"} for i in ids2]

# 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)

@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)

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_high_load():
    """Throughput test: high load, but bounded for speed."""
    user_ids = list(range(300))
    result = await fetch_all_users(user_ids)
    # Ensure all users are unique and correct
    for i, user in enumerate(result):
        pass

@pytest.mark.asyncio
async def test_fetch_all_users_throughput_sustained_concurrent():
    """Throughput test: sustained concurrent execution pattern."""
    # Simulate 10 concurrent requests, each for 20 users
    batches = [list(range(i*20, (i+1)*20)) for i in range(10)]
    results = await asyncio.gather(*(fetch_all_users(batch) for batch in batches))
    # Check each batch result
    for batch, result in zip(batches, results):
        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-mhq8hacp and push.