majiayu000 name: concurrency description: Comprehensive concurrency and parallelism patterns for multi-threaded and async programming. Use when implementing async/await, parallel processing, thread safety, worker pools, or debugging race conditions and deadlocks. Triggers: async, await, concurrent, parallel, threads, race condition, deadlock, mutex, semaphore, worker pool, queue.
Concurrency
Overview
Concurrency enables programs to handle multiple tasks efficiently. This skill covers async/await patterns, parallelism vs concurrency distinctions, race condition prevention, deadlock handling, thread safety patterns, and work queue implementations.
Instructions
1. Async/Await Patterns
Python Async Patterns
import asyncio
from typing import List, TypeVar, Coroutine, Any
T = TypeVar('T')
# Basic async function
async def fetch_data(url: str) -> dict:
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
return await response.json()
# Concurrent execution with gather
async def fetch_all(urls: List[str]) -> List[dict]:
tasks = [fetch_data(url) for url in urls]
return await asyncio.gather(*tasks, return_exceptions=True)
# Timeout handling
async def fetch_with_timeout(url: str, timeout: float = 5.0) -> dict:
try:
return await asyncio.wait_for(fetch_data(url), timeout=timeout)
except asyncio.TimeoutError:
raise TimeoutError(f"Request to {url} timed out after {timeout}s")
# Semaphore for rate limiting
async def fetch_with_rate_limit(urls: List[str], max_concurrent: int = 10) -> List[dict]:
semaphore = asyncio.Semaphore(max_concurrent)
async def limited_fetch(url: str) -> dict:
async with semaphore:
return await fetch_data(url)
return await asyncio.gather(*[limited_fetch(url) for url in urls])
# Async context manager
class AsyncDatabaseConnection:
async def __aenter__(self):
self.conn = await asyncpg.connect(...)
return self.conn
async def __aexit__(self, exc_type, exc_val, exc_tb):
await self.conn.close()
# Async iterator
class AsyncPaginator:
def __init__(self, fetch_page):
self.fetch_page = fetch_page
self.page = 0
self.done = False
def __aiter__(self):
return self
async def __anext__(self):
if self.done:
raise StopAsyncIteration
result = await self.fetch_page(self.page)
if not result:
self.done = True
raise StopAsyncIteration
self.page += 1
return result
TypeScript Async Patterns
// Promise.all for concurrent execution
async function fetchAll<T>(urls: string[]): Promise<T[]> {
return Promise.all(urls.map((url) => fetch(url).then((r) => r.json())));
}
// Promise.allSettled for fault tolerance
async function fetchAllSafe<T>(urls: string[]): Promise<Array<T | Error>> {
const results = await Promise.allSettled(
urls.map((url) => fetch(url).then((r) => r.json())),
);
return results.map((result) =>
result.status === "fulfilled" ? result.value : new Error(result.reason),
);
}
// Rate-limited concurrent execution
async function fetchWithConcurrencyLimit<T>(
items: string[],
fn: (item: string) => Promise<T>,
limit: number,
): Promise<T[]> {
const results: T[] = [];
const executing: Promise<void>[] = [];
for (const item of items) {
const p = fn(item).then((result) => {
results.push(result);
});
executing.push(p);
if (executing.length >= limit) {
await Promise.race(executing);
executing.splice(
executing.findIndex((e) => e === p),
1,
);
}
}
await Promise.all(executing);
return results;
}
// Async queue
class AsyncQueue<T> {
private queue: T[] = [];
private resolvers: Array<(value: T) => void> = [];
async enqueue(item: T): Promise<void> {
if (this.resolvers.length > 0) {
const resolve = this.resolvers.shift()!;
resolve(item);
} else {
this.queue.push(item);
}
}
async dequeue(): Promise<T> {
if (this.queue.length > 0) {
return this.queue.shift()!;
}
return new Promise((resolve) => this.resolvers.push(resolve));
}
}
2. Parallelism vs Concurrency
import asyncio
import multiprocessing
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
# Concurrency: I/O-bound tasks (use async or threads)
async def io_bound_concurrent():
"""Use for network calls, file I/O, database queries."""
async with aiohttp.ClientSession() as session:
tasks = [session.get(url) for url in urls]
return await asyncio.gather(*tasks)
def io_bound_threaded(urls: List[str]):
"""Threads for I/O when async isn't available."""
with ThreadPoolExecutor(max_workers=10) as executor:
return list(executor.map(requests.get, urls))
# Parallelism: CPU-bound tasks (use processes)
def cpu_bound_parallel(data: List[int]) -> List[int]:
"""Use for heavy computation - bypasses GIL."""
with ProcessPoolExecutor() as executor:
return list(executor.map(heavy_computation, data))
# Hybrid: CPU work with I/O
async def hybrid_processing(items: List[dict]):
"""Combine async I/O with parallel CPU processing."""
loop = asyncio.get_event_loop()
# Fetch data concurrently
raw_data = await asyncio.gather(*[fetch(item) for item in items])
# Process CPU-bound work in parallel
with ProcessPoolExecutor() as executor:
processed = await loop.run_in_executor(
executor,
process_batch,
raw_data
)
return processed
3. Race Conditions and Prevention
import threading
import asyncio
from contextlib import contextmanager
# Thread-safe counter with lock
class ThreadSafeCounter:
def __init__(self):
self._value = 0
self._lock = threading.Lock()
def increment(self):
with self._lock:
self._value += 1
return self._value
@property
def value(self):
with self._lock:
return self._value
# Read-write lock for optimized concurrent access
class ReadWriteLock:
def __init__(self):
self._read_ready = threading.Condition(threading.Lock())
self._readers = 0
@contextmanager
def read_lock(self):
with self._read_ready:
self._readers += 1
try:
yield
finally:
with self._read_ready:
self._readers -= 1
if self._readers == 0:
self._read_ready.notify_all()
@contextmanager
def write_lock(self):
with self._read_ready:
while self._readers > 0:
self._read_ready.wait()
yield
# Async lock for async code
class AsyncSafeCache:
def __init__(self):
self._cache = {}
self._lock = asyncio.Lock()
async def get_or_set(self, key: str, factory):
async with self._lock:
if key not in self._cache:
self._cache[key] = await factory()
return self._cache[key]
# Compare-and-swap for lock-free operations
import atomics # or use threading primitives
class LockFreeCounter:
def __init__(self):
self._value = atomics.atomic(width=8, atype=atomics.INT)
def increment(self):
while True:
current = self._value.load()
if self._value.cmpxchg_weak(current, current + 1):
return current + 1
4. Deadlock Detection and Prevention
import threading
from collections import defaultdict
from typing import Dict, Set
import time
# Deadlock prevention with lock ordering
class OrderedLockManager:
"""Prevents deadlocks by enforcing lock acquisition order."""
def __init__(self):
self._lock_order: Dict[str, int] = {}
self._next_order = 0
self._thread_locks: Dict[int, Set[str]] = defaultdict(set)
self._meta_lock = threading.Lock()
def register_lock(self, name: str) -> threading.Lock:
with self._meta_lock:
if name not in self._lock_order:
self._lock_order[name] = self._next_order
self._next_order += 1
return threading.Lock()
@contextmanager
def acquire(self, lock: threading.Lock, name: str):
thread_id = threading.current_thread().ident
# Check lock ordering
held_locks = self._thread_locks[thread_id]
for held_name in held_locks:
if self._lock_order[name] < self._lock_order[held_name]:
raise RuntimeError(
f"Lock ordering violation: {name} < {held_name}"
)
lock.acquire()
self._thread_locks[thread_id].add(name)
try:
yield
finally:
self._thread_locks[thread_id].discard(name)
lock.release()
# Timeout-based deadlock detection
class TimeoutLock:
def __init__(self, timeout: float = 5.0):
self._lock = threading.Lock()
self._timeout = timeout
def acquire(self):
acquired = self._lock.acquire(timeout=self._timeout)
if not acquired:
raise DeadlockError(
f"Failed to acquire lock within {self._timeout}s - possible deadlock"
)
return True
def release(self):
self._lock.release()
def __enter__(self):
self.acquire()
return self
def __exit__(self, *args):
self.release()
# Deadlock detection with wait-for graph
class DeadlockDetector:
def __init__(self):
self._wait_for: Dict[int, int] = {} # thread -> thread it's waiting for
self._lock = threading.Lock()
def register_wait(self, waiting_thread: int, holding_thread: int):
with self._lock:
self._wait_for[waiting_thread] = holding_thread
if self._has_cycle():
raise DeadlockError("Deadlock detected in wait-for graph")
def unregister_wait(self, thread: int):
with self._lock:
self._wait_for.pop(thread, None)
def _has_cycle(self) -> bool:
visited = set()
rec_stack = set()
def dfs(node):
visited.add(node)
rec_stack.add(node)
next_node = self._wait_for.get(node)
if next_node:
if next_node not in visited:
if dfs(next_node):
return True
elif next_node in rec_stack:
return True
rec_stack.remove(node)
return False
for node in self._wait_for:
if node not in visited:
if dfs(node):
return True
return False
5. Thread Safety Patterns
import threading
from functools import wraps
from typing import TypeVar, Generic
T = TypeVar('T')
# Thread-local storage
class RequestContext:
_local = threading.local()
@classmethod
def set_user(cls, user_id: str):
cls._local.user_id = user_id
@classmethod
def get_user(cls) -> str:
return getattr(cls._local, 'user_id', None)
# Immutable data for thread safety
from dataclasses import dataclass
from typing import Tuple
@dataclass(frozen=True)
class ImmutableConfig:
host: str
port: int
options: Tuple[str, ...] # Use tuple instead of list
# Copy-on-write pattern
class CopyOnWriteList(Generic[T]):
def __init__(self):
self._data: Tuple[T, ...] = ()
self._lock = threading.Lock()
def append(self, item: T):
with self._lock:
self._data = (*self._data, item)
def __iter__(self):
# Snapshot iteration - safe without lock
return iter(self._data)
# Thread-safe singleton
class Singleton:
_instance = None
_lock = threading.Lock()
def __new__(cls):
if cls._instance is None:
with cls._lock:
if cls._instance is None:
cls._instance = super().__new__(cls)
return cls._instance
# Synchronized decorator
def synchronized(lock: threading.Lock = None):
def decorator(func):
nonlocal lock
if lock is None:
lock = threading.Lock()
@wraps(func)
def wrapper(*args, **kwargs):
with lock:
return func(*args, **kwargs)
return wrapper
return decorator
6. Work Queues and Worker Pools
import asyncio
import queue
import threading
from typing import Callable, Any, List
from dataclasses import dataclass
from concurrent.futures import Future
@dataclass
class Job:
func: Callable
args: tuple
kwargs: dict
future: Future
# Thread-based worker pool
class ThreadWorkerPool:
def __init__(self, num_workers: int = 4):
self._queue = queue.Queue()
self._workers: List[threading.Thread] = []
self._shutdown = False
for _ in range(num_workers):
worker = threading.Thread(target=self._worker_loop, daemon=True)
worker.start()
self._workers.append(worker)
def _worker_loop(self):
while not self._shutdown:
try:
job = self._queue.get(timeout=1)
try:
result = job.func(*job.args, **job.kwargs)
job.future.set_result(result)
except Exception as e:
job.future.set_exception(e)
except queue.Empty:
continue
def submit(self, func: Callable, *args, **kwargs) -> Future:
future = Future()
job = Job(func, args, kwargs, future)
self._queue.put(job)
return future
def shutdown(self, wait: bool = True):
self._shutdown = True
if wait:
for worker in self._workers:
worker.join()
# Async worker pool
class AsyncWorkerPool:
def __init__(self, num_workers: int = 10):
self._queue: asyncio.Queue = asyncio.Queue()
self._num_workers = num_workers
self._workers: List[asyncio.Task] = []
async def start(self):
for _ in range(self._num_workers):
task = asyncio.create_task(self._worker_loop())
self._workers.append(task)
async def _worker_loop(self):
while True:
job = await self._queue.get()
if job is None: # Shutdown signal
break
func, args, kwargs, future = job
try:
if asyncio.iscoroutinefunction(func):
result = await func(*args, **kwargs)
else:
result = func(*args, **kwargs)
future.set_result(result)
except Exception as e:
future.set_exception(e)
finally:
self._queue.task_done()
async def submit(self, func: Callable, *args, **kwargs) -> Any:
future = asyncio.Future()
await self._queue.put((func, args, kwargs, future))
return await future
async def shutdown(self):
for _ in self._workers:
await self._queue.put(None)
await asyncio.gather(*self._workers)
# Priority queue worker
class PriorityWorkerPool:
def __init__(self, num_workers: int = 4):
self._queue = queue.PriorityQueue()
self._workers: List[threading.Thread] = []
self._shutdown = False
for _ in range(num_workers):
worker = threading.Thread(target=self._worker_loop, daemon=True)
worker.start()
self._workers.append(worker)
def _worker_loop(self):
while not self._shutdown:
try:
priority, job = self._queue.get(timeout=1)
try:
result = job.func(*job.args, **job.kwargs)
job.future.set_result(result)
except Exception as e:
job.future.set_exception(e)
except queue.Empty:
continue
def submit(self, func: Callable, *args, priority: int = 0, **kwargs) -> Future:
future = Future()
job = Job(func, args, kwargs, future)
self._queue.put((priority, job))
return future
Best Practices
Prefer Async for I/O: Use async/await for network and file I/O operations.
Use Processes for CPU Work: Bypass GIL with ProcessPoolExecutor for CPU-bound tasks.
Minimize Shared State: Prefer message passing over shared memory.
Lock Ordering: Always acquire locks in a consistent order to prevent deadlocks.
Keep Critical Sections Small: Hold locks for the minimum time necessary.
Use Higher-Level Abstractions: Prefer queues, futures, and async patterns over raw locks.
Test for Race Conditions: Use tools like ThreadSanitizer and stress testing.
Document Thread Safety: Clearly document which methods are thread-safe.
Examples
Complete Async Web Scraper with Rate Limiting
import asyncio
import aiohttp
from dataclasses import dataclass
from typing import List, Optional
@dataclass
class ScrapeResult:
url: str
status: int
content: Optional[str]
error: Optional[str] = None
class AsyncScraper:
def __init__(
self,
max_concurrent: int = 10,
requests_per_second: float = 5.0
):
self.semaphore = asyncio.Semaphore(max_concurrent)
self.rate_limit = 1.0 / requests_per_second
self.last_request_time = 0
self._lock = asyncio.Lock()
async def _rate_limit(self):
async with self._lock:
now = asyncio.get_event_loop().time()
wait_time = self.last_request_time + self.rate_limit - now
if wait_time > 0:
await asyncio.sleep(wait_time)
self.last_request_time = asyncio.get_event_loop().time()
async def scrape_url(
self,
session: aiohttp.ClientSession,
url: str
) -> ScrapeResult:
async with self.semaphore:
await self._rate_limit()
try:
async with session.get(url, timeout=10) as response:
content = await response.text()
return ScrapeResult(
url=url,
status=response.status,
content=content
)
except Exception as e:
return ScrapeResult(
url=url,
status=0,
content=None,
error=str(e)
)
async def scrape_all(self, urls: List[str]) -> List[ScrapeResult]:
async with aiohttp.ClientSession() as session:
tasks = [self.scrape_url(session, url) for url in urls]
return await asyncio.gather(*tasks)
# Usage
async def main():
scraper = AsyncScraper(max_concurrent=5, requests_per_second=2.0)
urls = ["https://example.com"] * 20
results = await scraper.scrape_all(urls)
for result in results:
if result.error:
print(f"Failed: {result.url} - {result.error}")
else:
print(f"Success: {result.url} - {result.status}")
asyncio.run(main())