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Source channel @githubtrending · Post #14743 · May 23

#javascript#ecmascript_proposals#es2015#es2019#es6#es7#esnext#javascript#js#polyfill#ponyfill#promise#proposal#proposals#shim#symbol#weakmap core-js is a modular JavaScript library that provides polyfills for modern ECMAScript features up to 2024, including promises, symbols, collections, iterators, typed arrays, and many web standards like URL and structuredClone. It lets you use new JavaScript features in older browsers by loading only the needed parts without polluting the global namespace. It integrates well with tools like Babel and swc for optimized polyfilling. This helps you write modern, compatible code that runs smoothly across different environments, improving development efficiency and user experience. You can customize polyfill usage and even build your own tailored version for your project. https://github.com/zloirock/core-js

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djangoproject

@djangoproject · Post #411 · 08/13/2017, 12:08 PM

http://sendapatch.se/projects/pylibmc/ #pylibmc is a client in Python for #memcached. It is a wrapper around TangentOrg‘s libmemcached library. The interface is intentionally made as close to python-memcached as possible, so that applications can drop-in replace it. pylibmc leverages among other things configurable behaviors, data pickling, data compression, battle-tested GIL retention, consistent distribution, and the binary memcached protocol.

djangoproject

@djangoproject · Post #410 · 08/13/2017, 11:53 AM

https://pypi.python.org/pypi/python-memcached This software is a 100% Python interface to the #memcached#memory#cache daemon. It is the #client side software which allows storing values in one or more, possibly remote, memcached servers. Search google for memcached for more information.

GitHub Trends

@githubtrending · Post #14772 · 06/01/2025, 12:00 AM

#cplusplus#cache#cpp#database#fibers#in_memory#in_memory_database#key_value#keydb#memcached#message_broker#multi_threading#nosql#redis#valkey#vector_search Dragonfly is a modern in-memory data store compatible with Redis and Memcached, offering up to 25 times higher throughput and better cache efficiency while using up to 80% fewer resources. It scales well with larger servers, supports many Redis commands, and features a unique, memory-efficient cache and fast snapshotting. Dragonfly provides low latency, high performance, and is easy to configure with familiar Redis options. Its design ensures atomic operations and efficient resource use, making it ideal for fast, cost-effective cloud applications needing real-time data access and high scalability. This means you get faster, more efficient caching and data handling with minimal changes to your existing setup[5][2][4]. https://github.com/dragonflydb/dragonfly