Curio Reference Manual

This manual describes the basic concepts and functionality provided by curio.

Coroutines

Curio is solely concerned with the execution of coroutines. A coroutine is a function defined using async def. For example:

async def hello(name):
      return 'Hello ' + name

Coroutines call other coroutines using await. For example:

async def main():
      s = await hello('Guido')
      print(s)

Unlike a normal function, a coroutine can never run all on its own. It always has to execute under the supervision of a manager (e.g., an event-loop, a kernel, etc.). In curio, an initial coroutine is executed by a low-level kernel using the run() function. For example:

import curio
curio.run(main())

When executed by curio, a coroutine is considered to be a “Task.” Whenever the word “task” is used, it refers to the execution of a coroutine.

The Kernel

All coroutines in curio are executed by an underlying kernel. Normally, you would run a top-level coroutine using the following function:

run(coro, *, pdb=False, log_errors=True, selector=None, with_monitor=False)

Run the coroutine coro to completion and return its final return value. If pdb is True, pdb is launched if any task crashes. If log_errors is True, a traceback is written to the log on crash. If with_monitor is True, then the monitor debugging task executes in the background. If selector is given, it should be an instance of a selector from the selectors module.

If you are going to repeatedly run coroutines one after the other, it will be more efficient to create a Kernel instance and submit them using its run() method as described below:

class Kernel(pdb=False, log_errors=True, selector=None, with_monitor=False)

Create an instance of a curio kernel. The arguments are the same as described above for the run() function.

There is only one method that may be used on a Kernel outside of coroutines.

Kernel.run(coro=None, shutdown=False)

Runs the kernel until all non-daemonic tasks have finished execution. coro is a coroutine to run as a task. If shutdown is True, the kernel will cancel all daemonic tasks and perform a clean shutdown once all regular tasks have completed. Calling this method with no coroutine and shutdown set to True will make the kernel cancel all remaining tasks and perform a clean shut down.

Tasks

The following functions are defined to help manage the execution of tasks.

await spawn(coro, daemon=False)

Create a new task that runs the coroutine coro. Does not return to the caller until the new task has been scheduled and executed for at least one cycle. Returns a Task instance as a result. The daemon option, if supplied, specifies that the new task will run indefinitely in the background. Curio only runs as long as there are non-daemonic tasks to execute. Note: a daemonic task will still be cancelled if the underlying kernel is shut down.

await sleep(seconds)

Sleep for a specified number of seconds. If the number of seconds is 0, the kernel merely switches to the next task (if any).

await current_task()

Returns a reference to the Task instance corresponding to the caller. A coroutine can use this to get a self-reference to its current Task instance if needed.

The spawn() and current_task() both return a Task instance that serves as a kind of wrapper around the underlying coroutine that’s executing. Task instances are not created directly, but they have the following methods that can be used in coroutines:

await Task.join()

Wait for the task to terminate. Returns the value returned by the task or raises a TaskError exception if the task failed with an exception. This is a chained exception. The __cause__ attribute of this exception contains the actual exception raised by the task when it crashed. If called on a task that has been cancelled, the __cause__ attribute is set to CancelledError.

await Task.cancel()

Cancels the task. This raises a CancelledError exception in the task which may choose to handle it in order to perform cleanup actions. Does not return until the task actually terminates. Curio only allows a task to be cancelled once. If this method is somehow invoked more than once on a still running task, the second request will merely wait until the task is cancelled from the first request. If the task has already run to completion, this method does nothing and returns immediately. Returns True if the task was actually cancelled. False is returned if the task was already finished prior to the cancellation request.

The following public attributes are available of Task instances:

Task.id

The task’s integer id.

Task.coro

The underlying coroutine associated with the task.

Task.daemon

Boolean flag that indicates whether or not a task is daemonic.

Task.state

The name of the task’s current state. Printing it can be potentially useful for debugging.

Task.cycles

The number of scheduling cycles the task has completed. This might be useful if you’re trying to figure out if a task is running or not. Or if you’re trying to monitor a task’s progress.

Task.exc_info

A tuple of exception information obtained from sys.exc_info() if the task crashes for some reason. Potentially useful for debugging.

Task.cancelled

A boolean flag that indicates whether or not the task was cancelled.

Task.terminated

A boolean flag that indicates whether or not the task has run to completion.

Timeouts

Any blocking operation in curio can be cancelled after a timeout. The following functions can be used for this purpose:

await timeout_after(seconds, coro=None)

Execute the specified coroutine and return its result. However, issue a cancellation request to the calling task after seconds have elapsed. When this happens, a TaskTimeout exception is raised. If coro is None, the result of this function serves as an asynchronous context manager that applies a timeout to a block of statements.

await ignore_after(seconds, coro=None, *, timeout_result=None)

Execute the specified coroutine and return its result. Issue a cancellation request after seconds have elapsed. When a timeout occurs, no exception is raised. Instead, None or the value of timeout_result is returned. If coro is None, the result is an asynchronous context manager that applies a timeout to a block of statements. For the context manager case, result attribute of the manager is set to None or the value of timeout_result if the block was cancelled.

Here is an example that shows how these functions can be used:

# Execute coro(args) with a 5 second timeout
try:
    result = await timeout_after(5, coro(args))
except TaskTimeout as e:
    result = None

# Execute multiple statements with a 5 second timeout
try:
    async with timeout_after(5):
         await coro1(args)
         await coro2(args)
         ...
except TaskTimeout as e:
    # Handle the timeout
    ...

The difference between timeout_after() and ignore_after() concerns the exception handling behavior when time expires. The latter function returns None instead of raising an exception which might be more convenient in certain cases. For example:

result = await ignore_after(5, coro(args))
if result is None:
    # Timeout occurred (if you care)
    ...

# Execute multiple statements with a 5 second timeout
async with ignore_after(5) as s:
    await coro1(args)
    await coro2(args)
    ...
    s.result = successful_result

if s.result is None:
    # Timeout occurred

It’s important to note that every curio operation can be cancelled by timeout. Rather than having every possible call take an explicit timeout argument, you should wrap the call using timeout_after() or ignore_after() as appropriate.

Performing External Work

Sometimes you need to perform work outside the kernel. This includes CPU-intensive calculations and blocking operations. Use the following functions to do that:

await run_in_process(callable, *args, **kwargs)

Run callable(*args, **kwargs) in a separate process and returns the result. If the calling task is cancelled, the underlying worker process (if started) is immediately cancelled by a SIGTERM signal.

await run_in_thread(callable, *args, **kwargs)

Run callable(*args, **kwargs) in a separate thread and return the result. If the calling task is cancelled, the underlying worker thread (if started) is set aside and sent a termination request. However, since there is no underlying mechanism to forcefully kill threads, the thread won’t recognize the termination request until it runs the requested work to completion. It’s important to note that a cancellation won’t block other tasks from using threads. Instead, cancellation produces a kind of “zombie thread” that executes the requested work, discards the result, and then disappears. For reliability, work submitted to threads should have a timeout or some other mechanism that puts a bound on execution time.

await run_in_executor(exc, callable, *args, **kwargs)

Run callable(*args, **kwargs) callable in a user-supplied executor and returns the result. exc is an executor from the concurrent.futures module in the standard library. This executor is expected to implement a submit() method that executes the given callable and returns a Future instance for collecting its result.

When performing external work, it’s almost always better to use the run_in_process() and run_in_thread() functions instead of run_in_executor(). These functions have no external library dependencies, have substantially less communication overhead, and more predictable cancellation semantics.

The following values in curio.workers define how many worker threads and processes are used. If you are going to change these values, do it before any tasks are executed.

MAX_WORKER_THREADS

Specifies the maximum number of threads used by a single kernel using the run_in_thread() function. Default value is 64.

MAX_WORKER_PROCESSES

Specifies the maximum number of processes used by a single kernel using the run_in_process() function. Default value is the number of CPUs on the host system.

I/O Layer

I/O in curio is performed by classes in curio.io that wrap around existing sockets and streams. These classes manage the blocking behavior and delegate their methods to an existing socket or file.

Socket

The Socket class is used to wrap existing an socket. It is compatible with sockets from the built-in socket module as well as SSL-wrapped sockets created by functions by the built-in ssl module. Sockets in curio should be fully compatible most common socket features.

class curio.io.Socket(sockobj)

Creates a wrapper the around an existing socket sockobj. This socket is set in non-blocking mode when wrapped. sockobj is not closed unless the created instance is explicitly closed or used as a context manager.

The following methods are redefined on Socket objects to be compatible with coroutines. Any socket method not listed here will be delegated directly to the underlying socket. Be aware that not all methods have been wrapped and that using a method not listed here might block the kernel or raise a BlockingIOError exception.

await Socket.recv(maxbytes, flags=0)

Receive up to maxbytes of data.

await Socket.recv_into(buffer, nbytes=0, flags=0)

Receive up to nbytes of data into a buffer object.

await Socket.recvfrom(maxsize, flags=0)

Receive up to maxbytes of data. Returns a tuple (data, client_address).

await Socket.recvfrom_into(buffer, nbytes=0, flags=0)

Receive up to nbytes of data into a buffer object.

await Socket.recvmsg(bufsize, ancbufsize=0, flags=0)

Receive normal and ancillary data.

await Socket.recvmsg_into(buffers, ancbufsize=0, flags=0)

Receive normal and ancillary data.

await Socket.send(data, flags=0)

Send data. Returns the number of bytes of data actually sent (which may be less than provided in data).

await Socket.sendall(data, flags=0)

Send all of the data in data.

await Socket.sendto(data, address)

await Socket.sendto(data, flags, address)

Send data to the specified address.

await Socket.sendmsg(buffers, ancdata=(), flags=0, address=None)

Send normal and ancillary data to the socket.

await Socket.accept()

Wait for a new connection. Returns a tuple (sock, address).

await Socket.connect(address)

Make a connection.

await Socket.connect_ex(address)

Make a connection and return an error code instead of raising an exception.

await Socket.close()

Close the connection. This method is not called on garbage collection.

await curio.io.do_handshake()

Perform an SSL client handshake. The underlying socket must have already be wrapped by SSL using the curio.ssl module.

Socket.makefile(mode, buffering=0)

Make a file-like object that wraps the socket. The resulting file object is a curio.io.FileStream instance that supports non-blocking I/O. mode specifies the file mode which must be one of 'rb' or 'wb'. buffering specifies the buffering behavior. By default unbuffered I/O is used. Note: It is not currently possible to create a stream with Unicode text encoding/decoding applied to it so those options are not available. If you are trying to put a file-like interface on a socket, it is usually better to use the Socket.as_stream() method below.

Socket.as_stream()

Wrap the socket as a stream using curio.io.SocketStream. The result is a file-like object that can be used for both reading and writing on the socket.

Socket.blocking()

A context manager that temporarily places the socket into blocking mode and returns the raw socket object used internally. This can be used if you need to pass the socket to existing synchronous code.

Socket objects may be used as an asynchronous context manager which cause the underlying socket to be closed when done. For example:

async with sock:
    # Use the socket
    ...
# socket closed here

FileStream

The FileStream class puts a non-blocking wrapper around an existing file-like object. Certain other functions in curio use this (e.g., the Socket.makefile() method).

class curio.io.FileStream(fileobj)

Create a file-like wrapper around an existing file. fileobj must be in in binary mode. The file is placed into non-blocking mode using os.set_blocking(fileobj.fileno()). fileobj is not closed unless the resulting instance is explicitly closed or used as a context manager.

The following methods are available on instances of FileStream:

await FileStream.read(maxbytes=- 1)

Read up to maxbytes of data on the file. If omitted, reads as much data as is currently available and returns it.

await FileStream.readall()

Return all of the data that’s available on a file up until an EOF is read.

await FileStream.readline()

Read a single line of data from a file.

await FileStream.write(bytes)

Write all of the data in bytes to the file.

await FileStream.writelines(lines)

Writes all of the lines in lines to the file.

await FileStream.flush()

Flush any unwritten data from buffers to the file.

await FileStream.close()

Flush any unwritten data and close the file. This method is not called on garbage collection.

FileStream.blocking()

A context manager that temporarily places the stream into blocking mode and returns the raw file object used internally. This can be used if you need to pass the file to existing synchronous code.

Other file methods (e.g., tell(), seek(), etc.) are available if the supplied fileobj also has them.

A FileStream may be used as an asynchronous context manager. For example:

async with stream:
    #  Use the stream object
    ...
# stream closed here

SocketStream

The SocketStream class puts a non-blocking file-like interface around a socket. This is normally created by the Socket.as_stream() method.

class curio.io.SocketStream(sock)

Create a file-like wrapper around an existing socket. sock must be a socket instance from Python’s built-in socket module. The socket is placed into non-blocking mode. sock is not closed unless the resulting instance is explicitly closed or used as a context manager.

A SocketStream instance supports the same methods as FileStream above. One subtle issue concerns the blocking() method below.

SocketStream.blocking()

A context manager that temporarily places the stream into blocking mode and returns a raw file object that wraps the underlying socket. It is important to note that the return value of this operation is a file created open(sock.fileno(), 'rb+', closefd=False). You can pass this object to code that is expecting to work with a file. The file is not closed when garbage collected.

socket wrapper module

The curio.socket module provides a wrapper around the built-in socket module–allowing it to be used as a stand-in in curio-related code. The module provides exactly the same functionality except that certain operations have been replaced by coroutine equivalents.

curio.socket.socket(family=AF_INET, type=SOCK_STREAM, proto=0, fileno=None)

Creates a curio.io.Socket wrapper the around socket objects created in the built-in socket module. The arguments for construction are identical and have the same meaning. The resulting socket instance is set in non-blocking mode.

The following module-level functions have been modified so that the returned socket objects are compatible with curio:

curio.socket.socketpair(family=AF_UNIX, type=SOCK_STREAM, proto=0)

curio.socket.fromfd(fd, family, type, proto=0)

curio.socket.create_connection(address, source_address)

The following module-level functions have been redefined as coroutines so that they don’t block the kernel when interacting with DNS:

await curio.socket.getaddrinfo(host, port, family=0, type=0, proto=0, flags=0)

await curio.socket.getfqdn(name)

await curio.socket.gethostbyname(hostname)

await curio.socket.gethostbyname_ex(hostname)

await curio.socket.gethostname()

await curio.socket.gethostbyaddr(ip_address)

await curio.socket.getnameinfo(sockaddr, flags)

ssl wrapper module

The curio.ssl module provides curio-compatible functions for creating an SSL layer around curio sockets. The following functions are redefined (and have the same calling signature as their counterparts in the standard ssl module:

curio.ssl.wrap_socket(*args, **kwargs)

await curio.ssl.get_server_certificate(*args, **kwargs)

curio.ssl.create_default_context(*args, **kwargs)

The SSLContext class is also redefined and modified so that the wrap_socket() method returns a socket compatible with curio.

Don’t attempt to use the curio.ssl module without a careful read of Python’s official documentation at https://docs.python.org/3/library/ssl.html.

For the purposes of curio, it is usually easier to apply SSL to a connection using some of the high level network functions described in the next section. For example, here’s how you make an outgoing SSL connection:

sock = await curio.open_connection('www.python.org', 443,
                                   ssl=True,
                                   server_hostname='www.python.org')

Here’s how you might define a server that uses SSL:

import curio
from curio import ssl

KEYFILE = "privkey_rsa"       # Private key
CERTFILE = "certificate.crt"  # Server certificat

async def handler(client, addr):
    ...

if __name__ == '__main__':
    kernel = curio.Kernel()
    ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
    ssl_context.load_cert_chain(certfile=CERTFILE, keyfile=KEYFILE)
    kernel.run(curio.tcp_server('', 10000, handler, ssl=ssl_context))

High Level Networking

The following functions are provided to simplify common tasks related to making network connections and writing servers.

await curio.open_connection(host, port, *, ssl=None, source_addr=None, server_hostname=None)

Creates an outgoing connection to a server at host and port. This connection is made using the socket.create_connection() function and might be IPv4 or IPv6 depending on the network configuration (although you’re not supposed to worry about it). ssl specifies whether or not SSL should be used. ssl can be True or an instance of curio.ssl.SSLContext. source_addr specifies the source address to use on the socket. server_hostname specifies the hostname to check against when making SSL connections. It is highly advised that this be supplied to avoid man-in-the-middle attacks.

await curio.open_unix_connection(path, *, ssl=None, server_hostname=None)

Creates a connection to a Unix domain socket with optional SSL applied.

await curio.tcp_server(host, port, client_connected_task, *, family=AF_INET, backlog=100, ssl=None, reuse_address=True)

Creates a server for receiving TCP connections on a given host and port. client_connected_task is a coroutine that is to be called to handle each connection. Family specifies the address family and is either socket.AF_INET or socket.AF_INET6. backlog is the argument to the socket.socket.listen() method. ssl specifies an curio.ssl.SSLContext instance to use. reuse_address specifies whether to reuse a previously used port.

await curio.unix_server(path, client_connected_task, *, backlog=100, ssl=None)

Creates a Unix domain server on a given path. client_connected_task is a coroutine to execute on each connection. backlog is the argument given to the socket.socket.listen() method. ssl is an optional curio.ssl.SSLContext to use if setting up an SSL connection.

subprocess wrapper module

The curio.subprocess module provides a wrapper around the built-in subprocess module.

class curio.subprocess.Popen(*args, **kwargs)

A wrapper around the subprocess.Popen class. The same arguments are accepted. On the resulting Popen instance, the stdin, stdout, and stderr file attributes have been wrapped by the curio.io.FileStream class. You can use these in an asynchronous context.

Here is an example of using Popen to read streaming output off of a subprocess with curio:

import curio
from curio import subprocess

async def main():
    p = subprocess.Popen(['ping', 'www.python.org'], stdout=subprocess.PIPE)
    async for line in p.stdout:
        print('Got:', line.decode('ascii'), end='')

if __name__ == '__main__':
    kernel = curio.Kernel()
    kernel.add_task(main())
    kernel.run()

The following methods of Popen have been replaced by asynchronous equivalents:

await Popen.wait(timeout=None)

Wait for a subprocess to exit.

await Popen.communicate(input=b'', timeout=None)

Communicate with the subprocess, sending the specified input on standard input. Returns a tuple (stdout, stderr) with the resulting output of standard output and standard error.

The following functions are also available. They accept the same arguments as their equivalents in the subprocess module:

await curio.subprocess.run(args, stdin=None, input=None, stdout=None, stderr=None, shell=False, timeout=None, check=False)

Run a command in a subprocess. Returns a subprocess.CompletedProcess instance.

await curio.subprocess.check_output(args, stdout=None, stderr=None, shell=False, timeout=None)

Run a command in a subprocess and return the resulting output. Raises a subprocess.CalledProcessError exception if an error occurred.

Synchronization Primitives

The following synchronization primitives are available. Their behavior is similar to their equivalents in the threading module. None of these primitives are safe to use with threads created by the built-in threading module.

class Event

An event object.

Event instances support the following methods:

Event.is_set()

Return True if the event is set.

Event.clear()

Clear the event.

await Event.wait()

Wait for the event.

await Event.set()

Set the event. Wake all waiting tasks (if any).

Here is an Event example:

import curio

async def waiter(evt):
    print('Waiting')
    await evt.wait()
    print('Running')

async def main():
    evt = curio.Event()
    # Create a few waiters
    await curio.spawn(waiter(evt))
    await curio.spawn(waiter(evt))
    await curio.spawn(waiter(evt))

    await curio.sleep(5)

    # Set the event. All waiters should wake up
    await evt.set()

curio.run(main)

class Lock

This class provides a mutex lock. It can only be used in tasks. It is not thread safe.

Lock instances support the following methods:

await Lock.acquire()

Acquire the lock.

await Lock.release()

Release the lock.

Lock.locked()

Return True if the lock is currently held.

The preferred way to use a Lock is as an asynchronous context manager. For example:

import curio

async def child(lck):
    async with lck:
        print('Child has the lock')

async def main():
    lck = curio.Lock()
    async with lck:
        print('Parent has the lock')
        await curio.spawn(child(lck))
        await curio.sleep(5)

curio.run(main())

class Semaphore(value=1)

Create a semaphore. Semaphores are based on a counter. If the count is greater than 0, it is decremented and the semaphore is acquired. Otherwise, the task has to wait until the count is incremented by another task.

class BoundedSemaphore(value=1)

This class is the same as Semaphore except that the semaphore value is not allowed to exceed the initial value.

Semaphores support the following methods:

await Semaphore.acquire()

Acquire the semaphore, decrementing its count. Blocks if the count is 0.

await Semaphore.release()

Release the semaphore, incrementing its count. Never blocks.

Semaphore.locked()

Return True if the Semaphore is locked.

Like locks, semaphores support the async-with statement. A common use of semaphores is to limit the number of tasks performing an operation. For example:

import curio

async def worker(sema):
    async with sema:
        print('Working')
        await curio.sleep(5)

async def main():
     sema = curio.Semaphore(2)     # Allow two tasks at a time

     # Launch a bunch of tasks
     for n in range(10):
         await curio.spawn(worker(sema))

     # After this point, you should see two tasks at a time run. Every 5 seconds.

curio.run(main())

class Condition(lock=None)

Condition variable. lock is the underlying lock to use. If none is provided, then a Lock object is used.

Condition objects support the following methods:

Condition.locked()

Return True if the condition variable is locked.

await Condition.acquire()

Acquire the condition variable lock.

await Condition.release()

Release the condition variable lock.

await Condition.wait()

Wait on the condition variable. This releases the underlying lock.

await Condition.wait_for(predicate)

Wait on the condition variable until a supplied predicate function returns True. predicate is a callable that takes no arguments.

await notify(n=1)

Notify one or more tasks, causing them to wake from the Condition.wait() method.

await notify_all()

Notify all tasks waiting on the condition.

Condition variables are often used to signal between tasks. For example, here is a simple producer-consumer scenario:

import curio
from collections import deque

items = deque()
async def consumer(cond):
    while True:
        async with cond:
            while not items:
                await cond.wait()    # Wait for items
            item = items.popleft()
        print('Got', item)

 async def producer(cond):
     for n in range(10):
          async with cond:
              items.append(n)
              await cond.notify()
          await curio.sleep(1)

 async def main():
     cond = curio.Condition()
     await curio.spawn(producer(cond))
     await curio.spawn(consumer(cond))

 curio.run(main())

Queues

If you want to communicate between tasks, it’s usually much easier to use a Queue instead.

class Queue(maxsize=0)

Creates a queue with a maximum number of elements in maxsize. If not specified, the queue can hold an unlimited number of items.

A Queue instance supports the following methods:

Queue.empty()

Returns True if the queue is empty.

Queue.full()

Returns True if the queue is full.

Queue.qsize()

Return the number of items currently in the queue.

await Queue.get()

Returns an item from the queue.

await Queue.put(item)

Puts an item on the queue.

await Queue.join()

Wait for all of the elements put onto a queue to be processed. Consumers must call Queue.task_done() to indicate completion.

await Queue.task_done()

Indicate that processing has finished for an item. If all items have been processed and there are tasks waiting on Queue.join() they will be awakened.

Here is an example of using queues in a producer-consumer problem:

import curio

async def producer(queue):
    for n in range(10):
        await queue.put(n)
    await queue.join()
    print('Producer done')

async def consumer(queue):
    while True:
        item = await queue.get()
        print('Consumer got', item)
        await queue.task_done()

async def main():
    q = curio.Queue()
    prod_task = await curio.spawn(producer(q))
    cons_task = await curio.spawn(consumer(q))
    await prod_task.join()
    await cons_task.cancel()

curio.run(main())

Synchronizing with Threads and Processes

Curio’s synchronization primitives aren’t safe to use with externel threads or processes. However, Curio can work with existing thread or process-level synchronization primitives if you use the abide() function.

await abide(op, *args, **kwargs)

Makes curio abide by the execution requirements of a given function, coroutine, or context manager. If op is coroutine function, it is called with the given arguments and returned. If op is an asynchronous context manager, it is returned unmodified. If op is a synchronous function, run_in_thread(op, *args, **kwargs) is returned. If op is a synchronous context manager, it is wrapped by an asynchronous context manager that executes the __enter__() and __exit__() methods in a backing thread.

The main use of this function is in code that wants to safely synchronize curio with threads and processes. For example, here is how you would synchronize a thread with a curio task using a threading lock:

import curio
import threading
import time

# A curio task
async def child(lock):
    async with curio.abide(lock):
        print('Child has the lock')

# A thread
def parent(lock):
    with lock:
         print('Parent has the lock')
         time.sleep(5)

lock = threading.Lock()
threading.Thread(target=parent, args=(lock,)).start()
curio.run(child(lock))

Here is how you would implement a producer/consumer problem between threads and curio using a standard thread queue:

import curio
import threading
import queue

# A thread
def producer(queue):
    for n in range(10):
        queue.put(n)
    queue.join()
    print('Producer done')
    queue.put(None)

# A curio task
async def consumer(queue):
    while True:
        item = await curio.abide(queue.get)
        if item is None:
            break
        print('Consumer got', item)
        await curio.abide(queue.task_done)
    print('Consumer done')

q = queue.Queue()
threading.Thread(target=producer, args=(q,)).start()
curio.run(consumer(q))

A notable feature of abide() is that it also accepts Curio’s own synchronization primitives. Thus, you can write code that works independently of the lock type. For example, the first locking example could be rewritten as follows and the child would still work:

import curio

# A curio task (works with any lock)
async def child(lock):
    async with curio.abide(lock):
        print('Child has the lock')

# Another curio task
async def main():
    lock = curio.Lock()
    async with lock:
         print('Parent has the lock')
         await curio.spawn(child(lock))
         await curio.sleep(5)

curio.run(main())

A special circle of hell is reserved for code that combines the use of the abide() function with task cancellation. Although cancellation is supported, there are a few things to keep in mind about it. First, if you are using abide(func, arg1, arg2, ...) to run a synchronous function, that function will fully run to completion in a separate thread regardless of the cancellation. So, if there are any side-effects associated with that code executing, you’ll need to take them into account. Second, if you are using async with abide(lock) with a thread-lock and a cancellation request is received while waiting for the lock.__enter__() method to execute, a background thread continues to run waiting for the eventual lock acquisition. Once acquired, it will be immediately released again. Without this, task cancellation would surely cause a deadlock of threads waiting to use the same lock.

The abide() function can be used to synchronize with a thread reentrant lock (e.g., threading.RLock). However, reentrancy is not supported. Each lock acquisition using abide() involves a backing thread. Repeated acquisitions would violate the constraint that reentrant locks have on only acquired by a single thread.

All things considered, it’s probably best to try and avoid code that synchronizes Curio tasks with threads. However, if you must, Curio abides.

Signals

Unix signals are managed by the SignalSet class. This class operates as an asynchronous context manager. The recommended usage looks like this:

import signal

async def coro():
    ...
    async with SignalSet(signal.SIGUSR1, signal.SIGHUP) as sigset:
          ...
          signo = await sigset.wait()
          print('Got signal', signo)
          ...

For all of the statements inside the context-manager, signals will be queued. The sigset.wait() operation will return received signals one at a time from the signal queue.

Signals can be temporarily ignored using a normal context manager:

async def coro():
    ...
    sigset = SignalSet(signal.SIGINT)
    with sigset.ignore():
          ...
          # Signals temporarily disabled
          ...

Caution: Signal handling only works if the curio kernel is running in Python’s main execution thread. Also, mixing signals with threads, subprocesses, and other concurrency primitives is a well-known way to make your head shatter into small pieces. Tread lightly.

class SignalSet(*signals)

Represents a set of one or more Unix signals. signals is a list of signals as defined in the built-in signal module.

The following methods are available on a SignalSet instance. They may only be used in coroutines.

await SignalSet.wait()

Wait for one of the signals in the signal set to arrive. Returns the signal number of the signal received. Normally this method is used inside an async with statement because this allows received signals to be properly queued. It can be used in isolation, but be aware that this will only catch a single signal right at that line of code. It’s possible that you might lose signals if you use this method outside of a context manager.

SignalSet.ignore()

Returns a context manager wherein signals from the signal set are temporarily disabled. Note: This is a normal context manager– use a normal with-statement.

Exceptions

The following exceptions are defined. All are subclasses of the CurioError base class.

exception CancelledError

Exception raised in a coroutine if it has been cancelled. If ignored, the coroutine is silently terminated. If caught, a coroutine can continue to run, but should work to terminate execution. Ignoring a cancellation request and continuing to execute will likely cause some other task to hang.

exception TaskTimeout

Exception raised in a coroutine if it has been cancelled by timeout.

exception TaskError

Exception raised by the Task.join() method if an uncaught exception occurs in a task. It is a chained exception. The __cause__ attribute contains the exception that causes the task to fail.

Low-level Kernel System Calls

The following system calls are available, but not typically used directly in user code. They are used to implement higher level objects such as locks, socket wrappers, and so forth. If you find yourself using these, you’re probably doing something wrong–or implementing a new curio primitive. These calls are found in the curio.traps submodule.

await curio.traps._read_wait(fileobj)

Sleep until data is available for reading on fileobj. fileobj is any file-like object with a fileno() method.

await curio.traps._write_wait(fileobj)

Sleep until data can be written on fileobj. fileobj is any file-like object with a fileno() method.

await curio.traps._future_wait(future)

Sleep until a result is set on future. future is an instance of concurrent.futures.Future.

await curio.traps._join_task(task)

Sleep until the indicated task completes. The final return value of the task is returned if it completed successfully. If the task failed with an exception, a TaskError exception is raised. This is a chained exception. The TaskError.__cause__ attribute of this exception contains the actual exception raised in the task.

await curio.traps._cancel_task(task)

Cancel the indicated task. Does not return until the task actually completes the cancellation. Note: It is usually better to use Task.cancel() instead of this function.

await curio.traps._wait_on_queue(kqueue, state_name)

Go to sleep on a queue. kqueue is an instance of a kernel queue which is typically a collections.deque instance. state_name is the name of the wait state (used in debugging).

await curio.traps._reschedule_tasks(kqueue, n=1, value=None, exc=None)

Reschedule one or more tasks from a queue. kqueue is an instance of a kernel queue. n is the number of tasks to release. value and exc specify the return value or exception to raise in the task when it resumes execution.

await curio.traps._sigwatch(sigset)

Tell the kernel to start queuing signals in the given signal set sigset.

await curio.traps._sigunwatch(sigset)

Tell the kernel to stop queuing signals in the given signal set.

await curio.traps._sigwait(sigset)

Wait for the arrival of a signal in a given signal set. Returns the signal number of the received signal.

await curio.traps._get_kernel()

Get a reference to the running Kernel object.

await curio.traps._get_current()

Get a reference to the currently running Task instance.

await curio.traps._set_timeout(seconds)

Set a timeout in the currently running task. Returns the previous timeout (if any)

await curio.traps._unset_timeout(previous)

Unset a timeout in the currently running task. previous is the value returned by the _set_timeout() call used to set the timeout.

Again, you’re unlikely to use any of these functions directly. However, here’s a small taste of how they’re used. For example, the curio.io.Socket.recv() method looks roughly like this:

class Socket(object):
    ...
    def recv(self, maxbytes):
        while True:
            try:
                return self._socket.recv(maxbytes)
            except BlockingIOError:
                await _read_wait(self._socket)
    ...

This method first tries to receive data. If none is available, the _read_wait() call is used to put the task to sleep until reading can be performed. When it awakes, the receive operation is retried. Just to emphasize, the _read_wait() doesn’t actually perform any I/O. It’s just scheduling a task for it.

Here’s an example of code that implements a mutex lock:

from collections import deque

class Lock(object):
    def __init__(self):
        self._acquired = False
        self._waiting = deque()

    async def acquire(self):
        if self._acquired:
            await _wait_on_queue(self._waiting, 'LOCK_ACQUIRE')

    async def release(self):
         if self._waiting:
             await _reschedule_tasks(self._waiting, n=1)
         else:
             self._acquired = False

In this code you can see the low-level calls related to managing a wait queue. This code is not significantly different than the actual implementation of a lock in curio. If you wanted to make your own task synchronization objects, the code would look similar.