{"categories":[{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/5"},{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/2"},{"label":"Front-Ends","url":"https://skillfed.io/packages/category/database-front-ends"}],"enrichment":{"capability":"Pottery provides Python dict, set, list, deque, queue, and counter interfaces backed by Redis, letting you use familiar Python collection syntax to read and write data persisted in Redis.","skillfed_tags":["redis-abstraction","distributed-storage","microservices"],"use_cases":["Implement a distributed cache or session store shared across multiple application instances or microservices.","Build a multi-producer, multi-consumer work queue that persists across application crashes using RedisSimpleQueue.","Use RedisDict or RedisSet as a persistent, shared data structure for configuration or feature flags.","Implement distributed locking or synchronization patterns with Redlock for coordinating work across processes.","Count occurrences or track metrics across a distributed system using RedisCounter."],"what_it_does":"Pottery wraps Redis with Python standard-library collection interfaces\u2014RedisDict, RedisSet, RedisList, RedisDeque, RedisCounter, RedisSimpleQueue\u2014so you can treat Redis as if it were a local Python dict or set. Instead of learning Redis commands, you use the same syntax you already know from Python's built-in collections. The package is designed for scenarios where you need persistent, shared storage across machines or processes, or where you want your data to survive application restarts.\n\nUnder the hood, Pottery serializes your data to JSON and stores it in Redis, handling the translation between Python objects and Redis operations. It supports async patterns via AIORedlock and includes utilities like Bloom filters, HyperLogLogs, and distributed locking. The package has been battle-tested in production at scale and is actively maintained, supporting Python 3.9 through 3.13.","worth_installing":"Yes, if you need a straightforward way to use Redis as a persistent, shared data store and prefer Python collection syntax over raw Redis commands. The low install friction, active maintenance, and lack of known vulnerabilities make it a safe choice. The main caveat is that all values must be JSON-serializable, and index-based access on RedisList is slow; for those constraints, it's a solid fit for caching, queues, and distributed state."},"id":"pottery","links":{"html":"https://skillfed.io/packages/pottery","md":"https://skillfed.io/packages/pottery.md","pypi":"https://pypi.org/project/pottery/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-03-21","license_spdx":null,"license_treatment":"unclear","name":"pottery","python_support":"supports_current","summary":"Redis for Humans."},"popularity":{"monthly_downloads":557205,"position":6012,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.0.1"}