From 996cd96a50519f2a75bc5c82c59a293e21b9361e Mon Sep 17 00:00:00 2001
From: "codeflash-ai-dev[bot]"
 <157075493+codeflash-ai-dev[bot]@users.noreply.github.com>
Date: Sat, 8 Nov 2025 11:16:28 +0000
Subject: [PATCH] Optimize fetch_all_users
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The optimization replaces sequential async execution with concurrent execution using `asyncio.gather()`, delivering a **53% runtime improvement** and **296% throughput increase**.

**Key Change**: The original code awaited each `fetch_user()` call sequentially in a loop, causing total execution time to be the sum of all individual fetch operations. The optimized version uses `asyncio.gather(*[fetch_user(user_id) for user_id in user_ids])` to execute all fetch operations concurrently.

**Why This Is Faster**: In the original implementation, each 0.0001-second `asyncio.sleep()` call must complete before the next one begins, creating cumulative delay. With `asyncio.gather()`, all coroutines start simultaneously and the total execution time becomes approximately equal to the longest single operation rather than the sum of all operations. The line profiler shows the optimized version eliminates the loop overhead entirely - the original had 3,265 loop iterations taking 96.3% of execution time, while the optimized version has a single gather operation.

**Concurrency Benefits**: For I/O-bound operations like database fetches, network requests, or any async operations with waiting periods, this pattern maximizes parallelism. When fetching N users, instead of N × 0.0001 seconds, execution takes roughly 0.0001 seconds total.

**Test Case Performance**: The optimization excels particularly with larger datasets - tests with 100+ user IDs show dramatic improvements since the benefit scales with the number of concurrent operations. Throughput tests demonstrate the optimization handles high-volume concurrent workloads much better, as evidenced by the 296% throughput increase from 5,472 to 21,660 operations per second.

The optimization maintains identical output ordering and handles all edge cases (empty lists, duplicates, negative IDs) while dramatically improving performance for any workload involving multiple async I/O operations.
---
 src/asynchrony/various.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/src/asynchrony/various.py b/src/asynchrony/various.py
index 95f9f7f..c6016cf 100644
--- a/src/asynchrony/various.py
+++ b/src/asynchrony/various.py
@@ -1,4 +1,5 @@
 import time
+import asyncio
 
 
 async def retry_with_backoff(func, max_retries=3):
@@ -13,3 +14,13 @@ async def retry_with_backoff(func, max_retries=3):
             if attempt < max_retries - 1:
                 time.sleep(0.0001 * attempt)
     raise last_exception
+
+
+async def fetch_user(user_id: int) -> dict:
+    """Simulates fetching a user from a database"""
+    await asyncio.sleep(0.0001)
+    return {"id": user_id, "name": f"User{user_id}"}
+
+
+async def fetch_all_users(user_ids: list[int]) -> list[dict]:
+    return await asyncio.gather(*[fetch_user(user_id) for user_id in user_ids])