# asyncio future

### Low-Level API Index

#### Obtaining the Event Loop

**1. asyncio.get\_running\_loop**

* **Purpose:** Get the currently running event loop.
* **Usage:** This is the preferred way to get the running event loop. Call this function directly from your code.
* **Example:**

```python
import asyncio

async def my_async_function():
    loop = asyncio.get_running_loop()
    # Do something using the loop
```

**2. asyncio.get\_event\_loop**

* **Purpose:** Get the current event loop (running or current via the current policy).
* **Usage:** Use this function to get the event loop if you are not sure if there is one running.
* **Example:**

```python
import asyncio

def my_non_async_function():
    loop = asyncio.get_event_loop()
    # Do something using the loop
```

**3. asyncio.set\_event\_loop**

* **Purpose:** Set the event loop as the current event loop via the current policy.
* **Usage:** Use this function to set a specific event loop as the current event loop.
* **Example:**

```python
import asyncio

def my_function():
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    # Do something using the loop
```

**4. asyncio.new\_event\_loop**

* **Purpose:** Create a new event loop.
* **Usage:** Use this function to create a new event loop that is not associated with any running process.
* **Example:**

```python
import asyncio

loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
# Do something using the loop
```

#### Real-World Applications

* Asynchronous networking (e.g., web servers, chat clients)
* Concurrent data processing
* Event-based GUI frameworks

***

**Event Loop Methods**

An event loop is a mechanism that allows you to run multiple tasks concurrently using a single thread. It does this by "polling" for tasks that need to be executed, and then running them one at a time until they complete.

In Python, the `asyncio` module provides an event loop that you can use to run asynchronous tasks.

**Using `asyncio.get_running_loop()`**

The `asyncio.get_running_loop()` function returns the current running event loop. If no event loop is running, it will create a new one.

Here is an example of how to use `asyncio.get_running_loop()`:

```python
import asyncio

async def main():
    print('Hello from the event loop!')

asyncio.run(main())
```

Output:

```
Hello from the event loop!
```

In this example, the `asyncio.run()` function starts the event loop and runs the `main()` coroutine. The `main()` coroutine prints a message to the console.

**Applications**

Event loops are used in a variety of applications, including:

* Web servers
* Network servers
* GUI applications
* Data processing pipelines
* Machine learning training

***

**Loop Lifecycle**

An event loop is the core of an asyncio application. It manages tasks, schedules their execution, and handles I/O (input/output) operations. Here's a breakdown of the key lifecycle methods for an event loop:

**1. run\_until\_complete:**

* This method runs a "Future" or "Task" (an object representing an asynchronous operation) until it completes.
* You can use this when you want to wait for a specific asynchronous task to finish.
* For example:

```
loop.run_until_complete(task)
```

**2. run\_forever:**

* This method starts the event loop and keeps it running indefinitely.
* It's used when you want the event loop to handle multiple asynchronous tasks continuously.
* For example:

```
loop.run_forever()
```

**3. stop:**

* This method stops the event loop.
* Use this when you want to stop the loop and exit the program.
* For example:

```
loop.stop()
```

**4. close:**

* This method closes the event loop and releases any resources it's using.
* Use this when you want to terminate the event loop and deallocate resources.
* For example:

```
loop.close()
```

**5. is\_running:**

* This method checks if the event loop is currently running.
* Use this to determine if the loop is active.
* For example:

```
if loop.is_running():
    print("Loop is running")
```

**6. is\_closed:**

* This method checks if the event loop has been closed.
* Use this to determine if the loop has been terminated.
* For example:

```
if loop.is_closed():
    print("Loop is closed")
```

**Potential Applications:**

Event loops are essential for developing asynchronous applications using asyncio, which can be beneficial in scenarios where:

* You need to handle multiple tasks concurrently without blocking.
* You want to perform I/O operations efficiently.
* You want to implement real-time or near-real-time applications.

***

**Debugging**

**What is debugging?**

Debugging is the process of finding and fixing errors in code. It involves running the code and checking for errors or unexpected behavior, and then modifying the code to address those issues.

**How to debug asyncio code:**

There are two main ways to debug asyncio code:

1. **Enable debug mode:** When debug mode is enabled, asyncio will print more detailed information about its operations, making it easier to track down and fix errors. To enable debug mode, call the `loop.set_debug()` method on the event loop.
2. **Use a debugger:** A debugger is a tool that allows you to step through your code line by line, inspecting the state of the code and variables at each step. There are many different debuggers available, such as the built-in Python debugger (pdb) and the more advanced Visual Studio Code debugger.

**Real-world example:**

The following code snippet shows how to enable debug mode and use the built-in Python debugger to track down an error in an asyncio script:

```python
import asyncio

# Enable debug mode
asyncio.get_event_loop().set_debug(True)

async def main():
    try:
        # Code with potential errors
        await asyncio.sleep(1)
        print("Hello world!")
    except Exception as e:
        # Catch any errors and print the stack trace
        import pdb; pdb.post_mortem()

asyncio.run(main())
```

When this code is run, the asyncio event loop will print detailed information about its operations. If an error occurs, the built-in Python debugger will automatically open and allow you to step through the code and inspect the state of the variables.

**Potential applications:**

Debugging is essential for developing and maintaining asyncio applications. It allows you to quickly track down and fix errors, ensuring that your applications run smoothly and reliably. Debugging is also useful for understanding the behavior of asyncio code and for learning how to use asyncio effectively.

***

**Scheduling Callbacks**

Sometimes, you may want to run a function or code block at a specific time or in response to an event. In asyncio, this is done using callbacks.

**1. Call Soon**

* **Function:** `loop.call_soon(callback)`
* **Description:** Schedules the callback to be run as soon as possible on the event loop.
* **Example:**

```python
import asyncio

async def hello_world():
    print("Hello, World!")

loop = asyncio.get_event_loop()
loop.call_soon(hello_world)
loop.run_forever()
```

**2. Call Soon Threadsafe**

* **Function:** `loop.call_soon_threadsafe(callback)`
* **Description:** Similar to `call_soon`, but can be called from any thread, even if the event loop is not running.
* **Example:**

```python
import asyncio

def threaded_callback():
    print("Hello from another thread!")

loop = asyncio.get_event_loop()
loop.call_soon_threadsafe(threaded_callback)
```

**3. Call Later**

* **Function:** `loop.call_later(delay, callback)`
* **Description:** Schedules the callback to be run after the specified delay in seconds.
* **Example:**

```python
import asyncio

async def after_5_seconds():
    print("Five seconds have passed.")

loop = asyncio.get_event_loop()
loop.call_later(5, after_5_seconds)
loop.run_forever()
```

**4. Call At**

* **Function:** `loop.call_at(when, callback)`
* **Description:** Schedules the callback to be run at a specific time (Unix timestamp).
* **Example:**

```python
import asyncio
import time

def on_the_hour():
    print(f"It is {time.strftime('%H:%M:%S')}")

loop = asyncio.get_event_loop()
now = time.time()
loop.call_at(now + 60*60, on_the_hour)
loop.run_forever()
```

**Real-World Applications:**

* **Scheduled tasks:** Running background tasks at a specific time or interval.
* **Event handling:** Responding to events, such as mouse clicks or network requests.
* **Time-sensitive operations:** Performing actions at precise times.
* **Coordination:** Scheduling multiple tasks to run in a specific order or at the same time.

***

**Thread/Process Pool**

**What is a thread/process pool?**

A thread or process is a way for your computer to run multiple tasks at the same time. A pool is a group of threads or processes that are ready to perform tasks.

**Why would I use a thread/process pool?**

There are a few reasons why you might want to use a thread or process pool:

* **Speed:** Using multiple threads or processes can speed up your program by performing tasks in parallel.
* **Responsiveness:** If your program is doing a lot of CPU-bound work (e.g., number crunching), using a thread or process pool can prevent your program from freezing up.
* **Concurrency:** Threads and processes allow your program to perform multiple tasks at the same time, even if your computer only has one CPU.

**How to use a thread/process pool**

The `asyncio` library provides two functions for using thread or process pools: `run_in_executor()` and `set_default_executor()`.

`run_in_executor()` runs a function in a thread or process pool. The function can be any function that doesn't require access to the event loop. For example:

```python
import asyncio
import concurrent

def cpu_bound_function(n):
    # This function takes a long time to run
    total = 0
    for i in range(n):
        total += i
    return total

async def main():
    # Create a thread pool with 4 threads
    executor = concurrent.futures.ThreadPoolExecutor(4)

    # Run the CPU-bound function in the thread pool
    result = await asyncio.run_in_executor(executor, cpu_bound_function, 1000000)

    # Print the result
    print(result)
```

`set_default_executor()` sets the default executor for `run_in_executor()`. This can be useful if you want to use a thread or process pool for all of your CPU-bound functions. For example:

```python
import asyncio
import concurrent

# Create a thread pool with 4 threads
executor = concurrent.futures.ThreadPoolExecutor(4)

# Set the default executor for run_in_executor()
asyncio.set_default_executor(executor)

async def main():
    # Run the CPU-bound function in the thread pool
    result = await asyncio.run_in_executor(cpu_bound_function, 1000000)

    # Print the result
    print(result)
```

**Real-world applications**

Thread and process pools are used in a variety of applications, including:

* **Web servers:** Thread and process pools are used to handle multiple HTTP requests at the same time.
* **Data processing:** Thread and process pools are used to speed up data processing tasks, such as image processing and machine learning.
* **Scientific computing:** Thread and process pools are used to parallelize scientific computing tasks, such as simulations and modeling.

***

**Tasks and Futures**

Imagine you want to ask your friend to do something, like bring you a glass of water. You can give your friend the instruction (the task) and wait for them to complete it (the future).

**Future**

A Future is like a placeholder for a result that will be available in the future. It's like a "promise" that you'll eventually get the water.

**Task**

A Task is like a scheduled event. You can tell your friend to bring you water at a specific time (schedule a task). The task will keep running until it's complete.

**Creating a Future**

To create a Future, you can use the `create_future()` method of an asyncio loop:

```python
loop = asyncio.get_event_loop()
future = loop.create_future()
```

**Creating a Task**

To create a Task, you can use the `create_task()` method of an asyncio loop:

```python
@asyncio.coroutine
def get_water():
    return await asyncio.sleep(1)

task = loop.create_task(get_water())
```

**Real-World Applications**

* **Asynchronous Operations:** Tasks and Futures allow you to perform long-running operations (like fetching data from a server) without blocking the entire program.
* **Concurrency:** You can schedule multiple tasks to run at the same time, allowing your program to handle multiple events simultaneously.
* **Error Handling:** Futures provide a way to handle errors that may occur during asynchronous operations.

**Example**

Here's an example of using Tasks and Futures to fetch data from multiple URLs concurrently:

```python
import asyncio

async def fetch_url(url):
    response = await asyncio.get(url)
    return response.text

async def main():
    loop = asyncio.get_event_loop()
    urls = ["url1", "url2", "url3"]
    tasks = [loop.create_task(fetch_url(url)) for url in urls]
    results = await asyncio.gather(*tasks)
    for result in results:
        print(result)

loop.run_until_complete(main())
```

***

### 1. asyncio.getaddrinfo:

asyncio.getaddrinfo is an asynchronous version of the socket.getaddrinfo function. It allows you to resolve a hostname to a list of IP addresses and port numbers. This is useful for connecting to a remote server over a network.

#### Simplified explanation:

asyncio.getaddrinfo is like a phone book for computers. It takes a hostname, which is like a name for a computer, and returns a list of IP addresses and port numbers, which are like phone numbers for computers. This allows you to connect to the correct computer and port when sending data over a network.

#### Code snippet:

```python
import asyncio

async def get_address_info():
    # hostname is the name of the computer you want to connect to
    # port is the port number you want to connect to
    hostname = 'example.com'
    port = 80

    # Get the IP addresses and port numbers associated with the hostname
    addrinfo = await asyncio.getaddrinfo(hostname, port)

    # Print the IP addresses and port numbers
    for addr in addrinfo:
        print(addr)
```

#### Real-world applications:

asyncio.getaddrinfo is used in a variety of applications, including:

* Web browsing
* Email
* File sharing
* Remote desktop

### 2. asyncio.getnameinfo

asyncio.getnameinfo is an asynchronous version of the socket.getnameinfo function. It allows you to get the hostname and port number associated with a given IP address. This is useful for displaying the origin of incoming data or for debugging network issues.

#### Simplified explanation:

asyncio.getnameinfo is like the reverse of asyncio.getaddrinfo. It takes an IP address and port number and returns the hostname associated with them. This allows you to identify the computer that sent data to you or to troubleshoot network problems by looking up the hostname of an IP address.

#### Code snippet:

```python
import asyncio

async def get_name_info():
    # ip_address is the IP address of the computer you want to get the hostname for
    # port is the port number associated with the IP address
    ip_address = '127.0.0.1'
    port = 80

    # Get the hostname and port number associated with the IP address
    name, port = await asyncio.getnameinfo((ip_address, port))

    # Print the hostname and port number
    print(name, port)
```

#### Real-world applications:

asyncio.getnameinfo is used in a variety of applications, including:

* Network monitoring
* Debugging
* Security

***

**Networking and IPC**

Networking and IPC (Inter-Process Communication) in Python's `asyncio` module allow you to create and manage network connections between your program and other computers or devices.

**1. Creating TCP Connections and Servers**

* **create\_connection()**: Opens a TCP (Transmission Control Protocol) connection to a remote host. TCP is the most common protocol for establishing reliable connections over a network.

```python
async def connect_to_server():
    reader, writer = await asyncio.open_connection('example.com', 8080)  # port can be int or string
    writer.write(b'Hello, world!\n')  # send data to the server
```

* **create\_server()**: Creates a TCP server that can listen for incoming connections and handle them concurrently.

```python
async def start_server():
    server = await asyncio.start_server(handle_client, 'localhost', 8080)
    async with server:
        await server.serve_forever()  # keep the server running until stopped

async def handle_client(reader, writer):
    data = await reader.read(1024)  # read data from the client
    writer.write(data.upper())  # send a response back to the client
```

**2. Creating Unix Socket Connections and Servers**

Unix sockets provide a way to communicate between processes on the same computer.

* **create\_unix\_connection()**: Opens a Unix socket connection to a remote socket.

```python
async def connect_to_unix_socket():
    reader, writer = await asyncio.open_unix_connection('/tmp/my_socket')
    writer.write(b'Hello, world!\n')
```

* **create\_unix\_server()**: Creates a Unix socket server that can listen for incoming connections.

```python
async def start_unix_socket_server():
    server = await asyncio.start_unix_server(handle_client, '/tmp/my_socket')
    async with server:
        await server.serve_forever()

async def handle_client(reader, writer):
    data = await reader.read(1024)
    writer.write(data.upper())
```

**3. Wrapping Sockets**

* **connect\_accepted\_socket()**: Converts a raw socket into a pair of asyncio transport and protocol objects.

```python
async def handle_accepted_socket(sock):
    transport, protocol = await asyncio.connect_accepted_socket(protocol_factory, sock)
    transport.close()
```

**4. Opening a Datagram (UDP) Connection**

* **create\_datagram\_endpoint()**: Opens a UDP (User Datagram Protocol) connection. UDP is a connectionless protocol for sending unreliable packets of data.

```python
async def send_udp_message():
    transport, protocol = await asyncio.create_datagram_endpoint(
        lambda: asyncio.DatagramProtocol(),
        local_addr=('127.0.0.1', 5000)
    )
    transport.sendto(b'Hello, world!', ('127.0.0.1', 5001))
```

**5. Sending Files**

* **sendfile()**: Sends a file over a transport.

```python
async def send_file(reader, writer, filename):
    with open(filename, 'rb') as f:
        await writer.sendfile(f)
```

**6. TLS Encryption**

* **start\_tls()**: Upgrades an existing connection to TLS (Transport Layer Security) for secure data transmission.

```python
async def start_tls_connection(reader, writer):
    transport = await asyncio.start_tls(transport, ssl_context)
    reader = asyncio.StreamReader(limit=65536, loop=transport.get_loop())
    writer = asyncio.StreamWriter(transport=transport, protocol=StreamReaderProtocol(reader))
```

**7. Pipes**

Pipes provide a way to communicate between processes on the same computer using a file-like interface.

* **connect\_read\_pipe()**: Wraps the read end of a pipe.
* **connect\_write\_pipe()**: Wraps the write end of a pipe.

```python
# Create a pipe
read_pipe, write_pipe = os.pipe()

async def read_from_pipe():
    reader, writer = await asyncio.connect_read_pipe(lambda: asyncio.StreamReader(), read_pipe)
    data = await reader.read(1024)
    return data

async def write_to_pipe():
    reader, writer = await asyncio.connect_write_pipe(lambda: asyncio.StreamWriter(), write_pipe)
    writer.write(b'Hello, world!')
    writer.close()

asyncio.gather(read_from_pipe(), write_to_pipe())
```

**Real-World Applications:**

* **TCP Connections**: Establishing connections with web servers, databases, or other remote devices.
* **Unix Sockets**: Inter-process communication within the same machine, especially in containerized environments.
* **UDP Connections**: Sending and receiving UDP packets for gaming, multimedia streaming, or device control.
* **TLS Encryption**: Securing data transmitted over the network, protecting user credentials, and preventing eavesdropping.
* **Pipes**: Facilitating communication between multiple processes running on the same machine, such as data processing or script chaining.

***

**Sockets**

**What are sockets?**

Sockets are like doorways between your computer and the outside world. They let your computer talk to other computers or devices over the internet.

**What are socket methods in asyncio-future?**

Asyncio-future provides methods to help you manage sockets. These methods allow you to:

* **Receive data** from a socket.
* **Send data** to a socket.
* **Connect** to a socket.
* **Accept** connections from other computers.
* **Watch** for when a socket is ready to read or write.

**Real-world examples:**

* **Web servers:** Web servers use sockets to listen for incoming connections from web browsers. When a connection is made, the server sends the requested web page to the browser.
* **Chat applications:** Chat applications use sockets to send and receive messages between users.
* **Online games:** Online games use sockets to exchange information between players, such as their positions and actions.

**Code examples:**

```python
# Receive data from a socket
data = await loop.sock_recv(socket, 1024)

# Send data to a socket
await loop.sock_sendall(socket, data)

# Connect to a socket
await loop.sock_connect(socket, ('example.com', 80))

# Accept a connection from another computer
conn, addr = await loop.sock_accept(socket)
```

**Potential applications:**

* **Building web servers**
* **Creating chat applications**
* **Developing online games**
* **Managing any kind of network communication**

***

**Unix Signals**

Signals are a way for the operating system to notify programs about events, such as a user pressing Ctrl+C or a file being modified. Asynchronous IO frameworks like asyncio provide ways to handle signals in your programs.

**Adding a Signal Handler**

To add a handler for a signal, use `loop.add_signal_handler(signal_number, callback)`. `signal_number` is the number of the signal you want to handle, and `callback` is a function that will be called when the signal is received.

```python
import signal
import asyncio

async def my_handler(signum):
    print("Received signal:", signum)

loop = asyncio.get_event_loop()
# Add a handler for signal 2 (SIGINT, Ctrl+C)
loop.add_signal_handler(signal.SIGINT, my_handler)

# Run the event loop
loop.run_forever()
```

When you press Ctrl+C, the `my_handler()` function will be called and print the signal number.

**Removing a Signal Handler**

To remove a signal handler, use `loop.remove_signal_handler(signal_number)`.

```python
loop.remove_signal_handler(signal.SIGINT)
```

**Real-World Applications**

Signal handlers are useful for handling events that require immediate action, such as:

* Gracefully exiting the program when the user presses Ctrl+C
* Stopping a long-running task when a file is modified
* Responding to system events, such as power failures or network outages

***

**Subprocesses**

A subprocess is a new program that runs within the current program. You can create subprocesses to perform tasks concurrently or to delegate tasks to external programs.

**Creating a subprocess**

To create a subprocess in asyncio, you can use the following methods:

1. `loop.subprocess_exec()`
   * This method allows you to specify the exact command and arguments to run as a subprocess.
   * For example:

```python
    import asyncio

    loop = asyncio.get_event_loop()

    proc = await loop.subprocess_exec(['ls', '-l'])
    stdout, stderr = await proc.communicate()
    print(f'stdout: {stdout}\nstderr: {stderr}')
```

* In this example, we create a subprocess to run the `ls -l` command. We then wait for the subprocess to complete and print its output and error streams.

2. `loop.subprocess_shell()`
   * This method allows you to specify a shell command to run as a subprocess.
   * For example:

```python
    import asyncio

    loop = asyncio.get_event_loop()

    proc = await loop.subprocess_shell('ls -l')
    stdout, stderr = await proc.communicate()
    print(f'stdout: {stdout}\nstderr: {stderr}')
```

* In this example, we create a subprocess to run the `ls -l` command using the shell. We then wait for the subprocess to complete and print its output and error streams.

**Potential applications**

Subprocesses can be used for a variety of tasks, such as:

* Running long-running tasks concurrently without blocking the main program.
* Delegating tasks to external programs that are not available in the current program.
* Interfacing with other programs or services using their command-line interfaces.

***

**Error Handling**

**1. loop.call\_exception\_handler(context)**

* This method is called when an exception occurs inside the event loop. It allows you to handle the exception and decide what to do with it.

**2. loop.set\_exception\_handler(handler)**

* This method sets a new exception handler for the event loop. The exception handler is a function that takes a single argument, which is the exception that occurred.

**3. loop.get\_exception\_handler()**

* This method returns the current exception handler for the event loop.

**4. loop.default\_exception\_handler(context)**

* This is the default exception handler for the event loop. It simply prints the exception and its traceback to the console.

**Real World Code Implementation**

Here is an example of how you can use the error handling methods in the asyncio-future module:

```python
import asyncio

loop = asyncio.get_event_loop()

async def main():
    try:
        await asyncio.sleep(1)
    except Exception as e:
        loop.call_exception_handler({'message': str(e)})

loop.run_until_complete(main())
```

In this example, the `loop.call_exception_handler()` method is called if an exception occurs in the `main()` coroutine. The exception handler function receives a context dictionary, which contains information about the exception. In this case, the context dictionary contains the message of the exception.

**Potential Applications**

Error handling is an important part of any asynchronous programming system. It allows you to handle exceptions and decide what to do with them, such as logging the exception, retrying the operation, or notifying the user.

Some potential applications of error handling in the asyncio-future module include:

* Logging exceptions to a file or database
* Retrying operations that fail due to temporary errors
* Notifying users about errors that occur in their tasks
* Shutting down the event loop if a critical error occurs

***

### Examples

#### 1. Using asyncio.new\_event\_loop() and loop.run\_forever()

```python
import asyncio

async def hello_world():
    print("Hello World!")

loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
loop.run_until_complete(hello_world())
loop.close()
```

This example creates a new event loop, runs the `hello_world` coroutine until it completes, and then closes the event loop. Coroutines are a special type of function that can be paused and resumed, which makes them ideal for asynchronous programming.

#### 2. Using loop.call\_later()

```python
import asyncio

async def hello_after(delay):
    print("Hello World! After", delay, "seconds.")

loop = asyncio.get_event_loop()
loop.call_later(2, hello_after, 2)
loop.run_forever()
loop.close()
```

This example calls the `hello_after` coroutine after a delay of 2 seconds. The `loop.call_later()` function schedules a callback to be called after a specified delay.

#### 3. Using loop.create\_connection() to implement an echo-client

```python
import asyncio

class EchoClientProtocol(asyncio.Protocol):
    def connection_made(self, transport):
        self.transport = transport
        self.transport.write("Hello World!\n")

    def data_received(self, data):
        self.transport.write(data.decode())

loop = asyncio.get_event_loop()
coro = loop.create_connection(lambda: EchoClientProtocol(),
                              "127.0.0.1", 8888)
loop.run_until_complete(coro)
loop.close()
```

This example creates a simple echo client using the `loop.create_connection()` function. The `EchoClientProtocol` class is a protocol that handles the connection and data exchange.

#### 4. Using loop.create\_connection() to connect a socket

```python
import asyncio

async def connect_socket():
    reader, writer = await asyncio.open_connection("127.0.0.1", 8888)
    writer.write("Hello World!\n")
    data = await reader.read(100)
    print(data.decode())
    writer.close()

loop = asyncio.get_event_loop()
loop.run_until_complete(connect_socket())
loop.close()
```

This example demonstrates how to connect to a socket using the `loop.create_connection()` function. The `asyncio.open_connection()` function returns a reader and writer pair that can be used to send and receive data.

#### 5. Using add\_reader() to watch an FD for read events

```python
import asyncio

async def watch_fd():
    fd = os.open("myfile.txt", os.O_RDONLY)
    loop = asyncio.get_event_loop()
    loop.add_reader(fd, lambda: print("FD ready!"))
    await asyncio.Future()  # Block forever

loop = asyncio.get_event_loop()
loop.run_until_complete(watch_fd())
loop.close()
```

This example uses the `loop.add_reader()` function to watch a file descriptor for read events. When the file descriptor is ready for reading, the callback function is called.

#### 6. Using loop.add\_signal\_handler()

```python
import asyncio
import signal

async def handle_signal(signum):
    print("Received signal", signum)

loop = asyncio.get_event_loop()
loop.add_signal_handler(signal.SIGINT, handle_signal)
loop.run_forever()
```

This example uses the `loop.add_signal_handler()` function to handle a signal. When the signal is received, the callback function is called.

#### 7. Using loop.subprocess\_exec()

```python
import asyncio

async def call_subprocess():
    proc = await asyncio.create_subprocess_exec("ls", "-l")
    stdout, stderr = await proc.communicate()
    print(f"stdout: {stdout}")
    print(f"stderr: {stderr}")

loop = asyncio.get_event_loop()
loop.run_until_complete(call_subprocess())
loop.close()
```

This example uses the `loop.subprocess_exec()` function to call a subprocess. The `asyncio.create_subprocess_exec()` function returns a `subprocess.Process` object that can be used to communicate with the subprocess.

### Transports

Transports are responsible for managing the underlying network connection. They provide methods for sending and receiving data, as well as for closing the connection.

All transports implement the following methods:

* **close()**: Close the transport.
* **is\_closing()**: Return `True` if the transport is closing or is closed.
* **get\_extra\_info()**: Request for information about the transport.
* **set\_protocol()**: Set a new protocol.
* **get\_protocol()**: Return the current protocol.

Transports that can receive data (TCP and Unix connections, pipes, etc). Returned from methods like :meth:`loop.create_connection`, :meth:`loop.create_unix_connection`, :meth:`loop.connect_read_pipe`, etc:

**Potential applications in real world**:

* **Web servers**: Transports are used to manage the connections between the web server and the clients.
* **Email servers**: Transports are used to manage the connections between the email server and the clients.
* **File servers**: Transports are used to manage the connections between the file server and the clients.
* **Game servers**: Transports are used to manage the connections between the game server and the players.

***

### Read Transports in Python's Asyncio-future Module

#### Understanding Read Transports

Read transports are used in asyncio to handle incoming data from a network connection. They provide methods to pause and resume receiving data, as well as a flag to indicate if the transport is currently receiving data.

#### Essential Functions

**`transport.is_reading()`**

* **Purpose:** Checks if the transport is actively receiving data.
* **Usage:** Use this function to determine if the transport is receiving data.
* **Simplified Explanation:** It's like checking if a pipe is open and water is flowing through it.

**`transport.pause_reading()`**

* **Purpose:** Pauses the receiving of data.
* **Usage:** Use this function when you want to temporarily stop receiving data, perhaps to process what you've already received or to handle other tasks.
* **Simplified Explanation:** It's like putting a cork in a pipe to stop the flow of water.

**`transport.resume_reading()`**

* **Purpose:** Resumes the receiving of data after it has been paused.
* **Usage:** Use this function to start receiving data again after it has been paused.
* **Simplified Explanation:** It's like removing the cork from the pipe to allow water to flow again.

#### Real-World Example

Consider a chat application where users can send and receive messages. The client's read transport would be responsible for receiving incoming messages from the server. When a new message arrives, the client's code would process it and display it to the user.

#### Applications

Read transports are essential for handling incoming data in various applications, including:

* Chat and messaging applications
* Data streaming services
* Web servers that receive requests from clients
* HTTP clients that send requests to remote servers

#### Code Example

Here's a simple code example that demonstrates reading data using a read transport:

```python
import asyncio

async def read_data(transport):
    while True:
        data = await transport.read(1024)  # Read up to 1024 bytes
        if not data:  # If no data was received, end the loop
            break
        print(f"Received data: {data.decode()}")  # Decoding the data assuming it's a text message

loop = asyncio.get_event_loop()
transport, _ = await loop.create_connection(read_data, "127.0.0.1", 8888)
loop.run_forever()  # Keeps the loop running until explicitly stopped
```

***

### Write Transports

Write transports provide a way to write data to a network or other destination. They are typically used to send data over a network, but can also be used to write to a file or other storage device.

#### Methods

**`write()`**

The `write()` method writes data to the transport. The data must be a bytes-like object, such as a `bytes` object or a `memoryview`. The method returns a `Future` that is resolved when the data has been successfully written to the transport.

```python
async def write_data(transport, data):
    try:
        await transport.write(data)
        print("Data written successfully.")
    except Exception as e:
        print(f"Error writing data: {e}")
```

**`writelines()`**

The `writelines()` method writes a list of buffers to the transport. The buffers must be bytes-like objects. The method returns a `Future` that is resolved when all of the buffers have been successfully written to the transport.

```python
async def write_lines(transport, buffers):
    try:
        await transport.writelines(buffers)
        print("Lines written successfully.")
    except Exception as e:
        print(f"Error writing lines: {e}")
```

**`can_write_eof()`**

The `can_write_eof()` method returns `True` if the transport supports sending an end-of-file (EOF) signal. This signal indicates that no more data will be sent over the transport.

```python
if transport.can_write_eof():
    print("Transport supports writing EOF.")
else:
    print("Transport does not support writing EOF.")
```

**`write_eof()`**

The `write_eof()` method closes the transport and sends an EOF signal. The method returns a `Future` that is resolved when the EOF signal has been successfully sent.

```python
async def write_eof(transport):
    try:
        await transport.write_eof()
        print("EOF written successfully.")
    except Exception as e:
        print(f"Error writing EOF: {e}")
```

**`abort()`**

The `abort()` method closes the transport immediately. This method does not send an EOF signal. The method returns a `Future` that is resolved when the transport has been successfully closed.

```python
async def abort(transport):
    try:
        await transport.abort()
        print("Transport aborted successfully.")
    except Exception as e:
        print(f"Error aborting transport: {e}")
```

**`get_write_buffer_size()`**

The `get_write_buffer_size()` method returns the current size of the output buffer. The output buffer is used to store data that has been written to the transport but has not yet been sent.

```python
buffer_size = transport.get_write_buffer_size()
print(f"Write buffer size: {buffer_size}")
```

**`get_write_buffer_limits()`**

The `get_write_buffer_limits()` method returns the high and low water marks for write flow control. The high water mark is the maximum size that the output buffer can reach before the transport starts to block writes. The low water mark is the minimum size that the output buffer can reach before the transport starts to unblock writes.

```python
high_water_mark, low_water_mark = transport.get_write_buffer_limits()
print(f"Write buffer limits: high water mark={high_water_mark}, low water mark={low_water_mark}")
```

**`set_write_buffer_limits()`**

The `set_write_buffer_limits()` method sets the high and low water marks for write flow control. The high water mark is the maximum size that the output buffer can reach before the transport starts to block writes. The low water mark is the minimum size that the output buffer can reach before the transport starts to unblock writes.

```python
transport.set_write_buffer_limits(high_water_mark, low_water_mark)
print(f"Write buffer limits set to: high water mark={high_water_mark}, low water mark={low_water_mark}")
```

#### Real-World Applications

Write transports are used in a variety of real-world applications, including:

* Sending data over a network
* Writing data to a file
* Writing data to a database
* Writing data to a log file

***

**Datagram Transports: Low-Level Communication with Subprocesses**

In Python's asyncio library, a DatagramTransport is a special class that provides a low-level way to send and receive data over a network connection to a subprocess. It's like a postman who can only deliver messages to a specific address (the subprocess) and receive messages from that address as well.

**Methods and their simplifications:**

**1. sendto() Method:**

* **Explanation in detail:**
  * Imagine you want to send a letter to your friend. You write the letter, put it in an envelope, and write your friend's address on the envelope.
  * Similarly, with sendto(), you write the data you want to send, wrap it in a "network envelope", and specify the address (IP address and port) of the subprocess you want to send it to.
* **Simplified explanation:**
  * Send a message to the subprocess.
* **Code snippet:**

```python
transport.sendto(b"Hello subprocess!", ("127.0.0.1", 8888))
```

**2. abort() Method:**

* **Explanation in detail:**
  * Imagine your postman can't reach your friend's address because it's wrong or the house doesn't exist.
  * abort() does something similar. If the subprocess is not responding or there's a problem with the connection, you can call abort() to immediately close the datagram transport.
* **Simplified explanation:**
  * Close the connection to the subprocess.
* **Code snippet:**

```python
transport.abort()
```

**Real-World Applications:**

* **Remote Program Execution:** Launching and communicating with subprocesses to execute commands or run programs on remote servers.
* **Process Monitoring:** Using datagrams to send status updates or error messages from asubprocess to a main process for monitoring.
* **High-Performance Communication:** Datagram transports can provide higher throughput and lower latency for specialized applications that require fast and efficient data transfer.

**Complete Code Implementation and Example:**

Here's a simplified example demonstrating how to create a datagram transport and send a message to a subprocess:

```python
import asyncio

async def main():
    # Create a subprocess using asyncio's subprocess_exec()
    transport, protocol = await asyncio.subprocess_exec("python", "/path/to/script.py")

    # Send a message to the subprocess
    transport.sendto(b"Hello subprocess!", ("127.0.0.1", 8888))

    # Close the transport
    transport.abort()

asyncio.run(main())
```

In this example, the main() coroutine launches a Python subprocess using subprocess\_exec() and obtains a transport object. Then, it sends a message to the subprocess using the transport's sendto() method. Finally, the transport is closed using abort() to terminate the connection.

***

***

ERROR OCCURED

.. rubric:: Subprocess Transports .. list-table:: :widths: 50 50 :class: full-width-table

```
* - :meth:`transport.get_pid() <SubprocessTransport.get_pid>`
  - Return the subprocess process id.

* - :meth:`transport.get_pipe_transport()
    <SubprocessTransport.get_pipe_transport>`
  - Return the transport for the requested communication pipe
    (*stdin*, *stdout*, or *stderr*).

* - :meth:`transport.get_returncode() <SubprocessTransport.get_returncode>`
  - Return the subprocess return code.

* - :meth:`transport.kill() <SubprocessTransport.kill>`
  - Kill the subprocess.

* - :meth:`transport.send_signal() <SubprocessTransport.send_signal>`
  - Send a signal to the subprocess.

* - :meth:`transport.terminate() <SubprocessTransport.terminate>`
  - Stop the subprocess.

* - :meth:`transport.close() <SubprocessTransport.close>`
  - Kill the subprocess and close all pipes.
```

## Protocols

Protocol classes can implement the following **callback methods**:

.. list-table:: :widths: 50 50 :class: full-width-table

```
* - ``callback`` :meth:`connection_made() <BaseProtocol.connection_made>`
  - Called when a connection is made.

* - ``callback`` :meth:`connection_lost() <BaseProtocol.connection_lost>`
  - Called when the connection is lost or closed.

* - ``callback`` :meth:`pause_writing() <BaseProtocol.pause_writing>`
  - Called when the transport's buffer goes over the high water mark.

* - ``callback`` :meth:`resume_writing() <BaseProtocol.resume_writing>`
  - Called when the transport's buffer drains below the low water mark.
```

Can you please simplify and explain the given content from python's asyncio-future module?

* explain each topic in detail and simplified manner (simplify in very plain english like explaining to a child).
* retain code snippets or provide if you have better and improved versions or examples.
* give real world complete code implementations and examples for each.
* provide potential applications in real world for each.
*   ignore version changes, changelogs, contributions, extra unnecessary content.

    ```
      The response was blocked.
    ```

***

***

### Streaming Protocols (TCP, Unix Sockets, Pipes)

In asyncio, "streaming protocols" are a way to handle data that is received over a network connection as a continuous stream. This is in contrast to "datagram protocols," which handle data that is received in discrete chunks.

#### Callbacks

Streaming protocols define two callback methods:

* **`data_received()`**: Called when some data is received from the network.
* **`eof_received()`**: Called when the other end of the connection has closed the connection and no more data will be received.

#### Real-World Implementations

Here is a simple example of a TCP streaming protocol that echos back any data it receives:

```python
import asyncio

class EchoProtocol(asyncio.Protocol):
    def data_received(self, data):
        self.transport.write(data)

    def eof_received(self):
        self.transport.close()

async def main():
    # Create a TCP server
    server = await asyncio.start_server(EchoProtocol, "127.0.0.1", 8888)

    # Serve forever
    await server.serve_forever()

asyncio.run(main())
```

This example creates a TCP server that listens on port 8888. When a client connects to the server, an instance of the `EchoProtocol` class is created. The `data_received()` method of the protocol is called whenever data is received from the client. The protocol simply echoes back the data to the client. The `eof_received()` method is called when the client closes the connection.

#### Potential Applications

Streaming protocols are used in a wide variety of applications, including:

* Web servers
* Chat servers
* File transfers
* Streaming media

***

### Buffered Streaming Protocols

Imagine you have a long message that you want to send over a network. You don't want to send it all at once, because that would be inefficient and might slow down the network. Instead, you want to send it in chunks, or buffers.

Buffered streaming protocols help you do this by providing a way to allocate buffers and manage the receiving of data. Here's how it works:

### Callback: `get_buffer()`

This callback is called to allocate a new buffer. It's like getting a new box to put the incoming data in.

### Callback: `buffer_updated()`

This callback is called when the buffer has been updated with the received data. It's like the box is now full and you can process the data inside it.

### Callback: `eof_received()`

This callback is called when an EOF (end of file) is received. It's like getting a signal that the sender has finished sending data and there's nothing left to receive.

Here's a simple example of how you might use a buffered streaming protocol:

```python
import asyncio

class EchoProtocol(asyncio.Protocol):
    def connection_made(self, transport):
        self.transport = transport
        self.buffer = bytearray()

    def data_received(self, data):
        self.buffer.extend(data)
        self.buffer_updated()

    def buffer_updated(self):
        if b'\n' in self.buffer:
            line, self.buffer = self.buffer.split(b'\n', 1)
            self.transport.write(line + b'\n')

    def eof_received(self):
        if self.buffer:
            self.transport.write(self.buffer + b'\n')
        self.transport.close()

loop = asyncio.get_event_loop()
coro = loop.create_server(EchoProtocol, '127.0.0.1', 8888)
server = loop.run_until_complete(coro)
loop.run_forever()
```

This code creates an echo server that receives data from clients and sends it back. It uses a buffered streaming protocol to handle the data in chunks. The `data_received()` method allocates a new buffer when needed, and the `buffer_updated()` method processes the data in the buffer. The `eof_received()` method is called when the client closes the connection.

### Potential Applications

Buffered streaming protocols are useful in any situation where you need to send or receive data in chunks. Here are a few potential applications:

* **Streaming media:** Sending and receiving audio or video data.
* **File transfer:** Sending and receiving large files over a network.
* **Data logging:** Collecting and storing data in chunks.
* **Distributed systems:** Communicating between multiple processes or machines.

***

**Datagram Protocols**

Datagram protocols are used to send and receive packets (datagrams) over a network. In asyncio, there are two types of datagram protocols:

* **UDP protocols:** UDP is a connectionless protocol that sends datagrams directly to remote hosts without establishing a connection. It's often used for applications that don't require guaranteed delivery or ordering of messages, such as voice and video streaming.
* **Unix datagram protocols:** Unix datagram sockets are used to send and receive datagrams within the same operating system. They're often used for inter-process communication or networking within a single host.

**Callback methods**

Both UDP and Unix datagram protocols have two callback methods:

* **datagram\_received():** This method is called when a datagram is received. It takes the datagram as an argument and should process and respond to it.
* **error\_received():** This method is called when a previous send or receive operation raises an OSError. It takes the exception as an argument and should handle the error.

**Real-world example**

A simple example of a UDP datagram protocol is a chat server. The server creates a UDP socket and listens for incoming datagrams from clients. When a datagram is received, the server processes it by checking the sender's address and port and the message content. It then sends a response datagram back to the client.

Here is a simplified Python implementation:

```python
import asyncio

class ChatServer(asyncio.DatagramProtocol):
    def datagram_received(self, data, addr):
        print(f"Received datagram: {data!r} from {addr}")
        self.transport.sendto(b'Hello, world!', addr)

async def main():
    server = ChatServer()
    transport, _ = await asyncio.start_server(server, '127.0.0.1', 8080)
    await transport.serve_forever()

if __name__ == '__main__':
    asyncio.run(main())
```

This server listens on port 8080 and prints any incoming datagrams to the console. It then sends a response datagram back to the client. You can test the server by sending UDP datagrams to it using a tool like netcat:

```bash
nc 127.0.0.1 8080
> Hello, world!
Received datagram: b'Hello, world!' from ('127.0.0.1', 50772)
```

**Potential applications**

Datagram protocols are used in a variety of applications, including:

* **Voice and video streaming:** UDP is often used for streaming media because it can handle variable-bitrate traffic without introducing noticeable delays or jitter.
* **Online gaming:** UDP is also popular for online gaming because it provides low latency and allows for fast-paced gameplay.
* **Network monitoring:** UDP can be used to send and receive monitoring packets to track network performance and identify problems.
* **Inter-process communication:** Unix datagram sockets are often used for inter-process communication within the same host. This is useful for applications that need to communicate with each other without using shared memory or other IPC mechanisms.

***

### Subprocess Protocols

**Subprocess protocols** handle the communication with a child process, such as reading data from its stdout and stderr pipes and handling its exit.

*   **pipe\_data\_received:** This method is called when the child process sends data to its stdout or stderr pipe. The data is passed as a `bytes` object.

    Example:

    ```python
    def pipe_data_received(self, data: bytes):
        print(data.decode())
    ```
*   **pipe\_connection\_lost:** This method is called when the connection to one of the pipes communicating with the child process is lost.

    Example:

    ```python
    def pipe_connection_lost(self, exc):
        print("Pipe connection lost:", exc)
    ```
*   **process\_exited:** This method is called when the child process has exited. The exit code of the process is passed as an integer.

    Example:

    ```python
    def process_exited(self, returncode):
        print("Process exited with code", returncode)
    ```

### Event Loop Policies

**Event loop policies** allow you to customize the behavior of the event loop. For example, you can:

* Change the default event loop factory.
* Set a global policy for creating new event loops.
* Install custom policies for specific types of event loops.

Here is an example of how to install a custom policy for the default event loop factory:

```python
import asyncio

# Custom policy
class MyPolicy(asyncio.DefaultEventLoopPolicy):
    def get_event_loop(self):
        # Create a custom event loop here
        return asyncio.SelectorEventLoop()

# Install the policy
asyncio.set_event_loop_policy(MyPolicy())
```

***

### Accessing Policies

#### get\_event\_loop\_policy

Returns the current process-wide policy. This can be used to check which policy is currently in use or to change it.

```python
import asyncio

# Get the current policy
policy = asyncio.get_event_loop_policy()
```

#### set\_event\_loop\_policy

Sets a new process-wide policy. This can be used to change the behavior of the asyncio event loop.

```python
import asyncio

# Create a new policy
policy = asyncio.DefaultEventLoopPolicy()

# Set the new policy
asyncio.set_event_loop_policy(policy)
```

#### AbstractEventLoopPolicy

Base class for policy objects. This class defines the interface that all policy objects must implement.

```python
class AbstractEventLoopPolicy:

    def __init__(self):
        self._closed = False

    def get_event_loop(self):
        """Return an event loop.  This may be called multiple times."""
        if self._closed:
            raise RuntimeError("Policy is closed")
        return self._loop_factory()

    def set_event_loop(self, loop):
        """Set the event loop.  This will be called only once."""
        if self._closed:
            raise RuntimeError("Policy is closed")
        self._loop = loop
```

#### Real World Applications

* **Customizing the event loop:** Policies can be used to customize the behavior of the asyncio event loop. For example, you could create a policy that uses a different thread pool or that runs specific callbacks at a higher priority.
* **Testing:** Policies can be used to test asyncio code in a controlled environment. For example, you could create a policy that simulates a slow network connection or that throws errors.
