CPython VS fastapi

Here's the relevant diff: https://github.com/python/cpython/pull/137968/files#diff-966...

Search is limited to 20 attributes and non-descriptors only to avoid arbitrary code execution.

I assume constructing AttributeErrors isn't highly performance sensitive.

If there is an audience for release notes I haven't seen anything better than just committing entries to pre-release folder as you change things and have release automation compile the folder into the actual release notes. Python and many other large projects handle it like this: https://github.com/python/cpython/tree/main/Misc/NEWS.d/next (The release notes for major releases are crafted manually)

Python Software Foundation. Python Programming Language. https://www.python.org/

import smtplib from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart from email.mime.base import MIMEBase from email import encoders # SMTP Server details port = 587 smtp_server = "smtp.sendlayer.net" username = "paulie" # Your username generated by SendLayer password = "sendlayer_smtp_password" # Your password generated by SendLayer sender_email = "paulie@example.com" receiver_email = "johndoe@example.com" # Email content subject = "Email Example with Attachment" html_message = """\ Hi, This is a test email sent from "https://www.python.org">Python using "https://sendlayer.com">SendLayer's SMTP server The email also includes an attachment """ # Create a multipart message and set headers message = MIMEMultipart() message["From"] = sender_email message["Subject"] = subject message["To"] = receiver_email # Attach the HTML part message.attach(MIMEText(html_message, "html")) # Specify the file path for the attachment filename = "./path/to/attachment/file.pdf" # Change this to the correct path # Open the file in binary mode with open(filename, "rb") as attachment: part = MIMEBase("application", "octet-stream") part.set_payload(attachment.read()) # Encode file in ASCII characters to send by email encoders.encode_base64(part) # Add header as key/value pair to attachment part part.add_header("Content-Disposition", f"attachment; filename= {filename}") # Add attachment to message message.attach(part) # Send the email with smtplib.SMTP(smtp_server, port) as server: server.starttls() server.login(username, password) server.sendmail(sender_email, receiver_email, message.as_string()) print('Email sent successfully')

import requests from concurrent.futures import ThreadPoolExecutor URLS = [ "https://www.python.org/", "https://www.djangoproject.com/", "https://flask.palletsprojects.com/", ] def fetch_url(url: str): print(f"Fetching {url}...") response = requests.get(url) print(f"Fetched {url} with status {response.status_code}") return len(response.content) with ThreadPoolExecutor(max_workers=5) as executor: # The map function runs `fetch_url` for each item in URLS results = executor.map(fetch_url, URLS) for url, length in zip(URLS, results): print(f"URL: {url}, Length: {length}")

Use KeysView and ValuesView instead of dict_keys and dict_values respectively because type checkers don't support dict_keys and dict_values in _collections_abc.

According to the benchmark in the PR that introduced this optimization, sorting a list that consists only of floats rather than a list of floats with even a single integer at the end is almost twice as fast.

If you want to a UUIDv7 key for partitioning your table by date (e.g., one partition per day or month), you need to be able to compute the partition range via the minimal UUIDv7 for a given date.

There is some discussion whether or not to add helpers for this to Python‘s uuid7 module: https://github.com/python/cpython/issues/130843#issuecomment...

With respect to tagged pointers, there seems to be some recent movements on that front in CPython: https://github.com/python/cpython/issues/132509

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Python's renaissance in web development comes from FastAPI - my personal favorite nowadays - which brings modern async capabilities and automatic API documentation through Pydantic models. The framework feels designed for 2025, with type hints throughout and excellent developer experience. If your application needs to integrate with machine learning models, data processing pipelines, or scientific computing libraries, Python is often the only practical choice.

Node.js is 40–60% faster in handling concurrent connections than Python due to its non-blocking I/O. Python, on the other hand, can outperform Node in CPU-bound computation only when optimized with C extensions or async frameworks like asyncio or FastAPI (FastAPI Docs).