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Source channel @githubtrending · Post #15110 · Sep 1

#other Cognitive load is the mental effort needed to understand and work with code. Since our brain can only hold about four pieces of information at once, complex code with many conditions, deep inheritance, or too many small modules increases this load, making it harder to understand and maintain. To reduce cognitive load, use clear, meaningful variable names, prefer composition over inheritance, avoid too many tiny modules, and keep interfaces simple. Also, avoid excessive abstractions, tight coupling with frameworks, and overly complex architectures. Lower cognitive load helps you and your team understand code faster, reduce bugs, and be more productive. https://github.com/zakirullin/cognitive-load

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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