CPython VS MySQL

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

MariaDB is one of the most popular relational database management systems (RDBMS), forked from MySQL by some of the original developers. It is open source licensed under GNU GPL 2, and ready for both community and enterprise use with long history and large knowledge to manage and maintain.

The application needs to store the user information and passkey details in the database. To set up the database schema, log in to your MySQL server, create a new database named webauthen_demo, and run the query below to create the database schema.

MySql Database

As your API grows, you'll likely outgrow JSON files. While suitable for small applications, JSON files can struggle with file locking conflicts, slow read/write operations, and complex querying needs. When these issues arise, it's time to consider a database like MySQL or PostgreSQL.

You can find detailed installation instructions in the docs. We will choose the Docker installation and use a MySQL database as a persistent data store.

DbSchema is not a database itself. It’s a visual tool that connects to your MySQL or MongoDB server and works directly with your database. Once connected, it shows you the real structure of your data - the tables or collections, the columns or fields, and even the relationships between them. You can explore the data, edit the schema, and understand your database more clearly, all in one place.

MySQL MySQL natively supports partitioning, but sharding needs add-ons like Vitess to run platforms like YouTube.

Open source software is built on the democratic idea that everyone should be able to inspect and contribute to the source code. Major projects like Linux, WordPress, and the Apache HTTP Server have shown how collaborative efforts can produce robust, scalable solutions. Indie hackers, often working with limited budgets, gain access to highly dependable tools such as Python and MySQL, which were originally developed and maintained by global communities.

Hey everyone! I recently got fed up with waiting minutes for simple analytics queries to finish, so I threw together a little demo to see how SingleStore, MySQL, and PostgreSQL stack up against each other on a 110 million‑row banking transactions dataset.

Traditional databases — PostgreSQL, MySQL, etc. — store their data in proprietary formats. That format is optimized for that engine and can’t be directly accessed by anything else. Even if something like Trino can connect to Postgres, it’s still running queries through Postgres itself, not reading its storage directly. You’re just a client.