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Even if there exists modules that do this (see other answer), I tried to write one simple, self-contained version. Here is a class KeyValueStore (key and value are strings) that works like this: Demo from sqlitekeyvaluestore import KeyValueStore kv = KeyValueStore('test.db') # uses SQLite print(len(kv)) # 0 item kv['hello1'] = 'you1' kv['hello2'] = 'you2' kv['hello3'] = 'you3' print(kv['hello1'])
What is HPy?¶ HPy provides a new API for extending Python in C. In other words, you use #include <hpy.h> instead of #include <Python.h>. What are the advantages of HPy?¶ Zero overhead on CPython: extensions written in HPy run at the same speed as "normal" extensions. Much faster on alternative implementations such as PyPy, GraalPy. Universal binaries: extensions built for the HPy Universal ABI can
This article was discussed on Hacker News (later), on reddit (also), featured in BSD Now 294. Also check out this Endlessh analysis. Iâm a big fan of tarpits: a network service that intentionally inserts delays in its protocol, slowing down clients by forcing them to wait. This arrests the speed at which a bad actor can attack or probe the host system, and it ties up some of the attackerâs resourc
... in which we look at one or two ways to make life easier when working with Python regular expressions. tl;dr: You can compose verbose regular expressions using fâ-âstrings. Here's a real-world example â instead of this: 1pattern = r"((?:\(\s*)?[A-Z]*H\d+[a-z]*(?:\s*\+\s*[A-Z]*H\d+[a-z]*)*(?:\s*[\):+])?)(.*?)(?=(?:\(\s*)?[A-Z]*H\d+[a-z]*(?:\s*\+\s*[A-Z]*H\d+[a-z]*)*(?:\s*[\):+])?(?![^\w\s])|$)"
PyScript is a framework that allows users to create rich Python applications in the browser using HTML's interface and the power of Pyodide, MicroPython and WASM, and modern web technologies. To get started see the Beginning PyScript tutorial. For examples see here. Other useful resources: The official technical docs. Our current Home Page on the web. A free-to-use online editor for trying PyScrip
ScaNN (Scalable Nearest Neighbors) is a method for efficient vector similarity search at scale. This code implements [1, 2], which includes search space pruning and quantization for Maximum Inner Product Search and also supports other distance functions such as Euclidean distance. The implementation is designed for x86 processors with AVX2 support. ScaNN achieves state-of-the-art performance on an
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