pottery
Redis for Humans.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Redis server accessible at the connection URL; all keys and values must be JSON serializable.
- Low install friction with only 3 runtime dependencies (redis, mmh3, typing_extensions).
- Actively maintained with a recent release on 2025-03-21 and ongoing commits; the repository is not archived and has 1246 stars.
License · maintenance · safety
(unclear)
last release 2025-03-21 (511 days) · last repo commit 2026-08-14 · 1,246 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 557,205 downloads/mo, #6,012 on PyPI
Alternatives
Verify before relying
from redis import Redis
from pottery import RedisDict
redis = Redis.from_url('redis://localhost:6379/1')
tel = RedisDict({'jack': 4098}, redis=redis, key='tel')
tel['guido'] = 4127
print(tel['jack'])- Whether the package is suitable for high-throughput or latency-sensitive workloads given Redis round-trip overhead
- Performance characteristics of index-based access on RedisList (described as O(n) in the excerpt)
- Whether all advertised features (Redlock, AIORedlock, NextID, redis_cache, CachedOrderedDict, Bloom filters, HyperLogLogs, ContextTimer) are production-ready
What it is and what it does
Pottery wraps Redis with Python standard-library collection interfaces—RedisDict, RedisSet, RedisList, RedisDeque, RedisCounter, RedisSimpleQueue—so 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.
Under 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
pottery on PyPI
Before you install
Low install friction with only 3 runtime dependencies (redis, mmh3, typing_extensions). Actively maintained with a recent release on 2025-03-21 and ongoing commits; the repository is not archived and has 1246 stars.
Requires a running Redis server accessible at the connection URL; all keys and values must be JSON serializable.
Quickstart
from redis import Redis
from pottery import RedisDict
redis = Redis.from_url('redis://localhost:6379/1')
tel = RedisDict({'jack': 4098}, redis=redis, key='tel')
tel['guido'] = 4127
print(tel['jack'])Verify before relying
- Whether the package is suitable for high-throughput or latency-sensitive workloads given Redis round-trip overhead
- Performance characteristics of index-based access on RedisList (described as O(n) in the excerpt)
- Whether all advertised features (Redlock, AIORedlock, NextID, redis_cache, CachedOrderedDict, Bloom filters, HyperLogLogs, ContextTimer) are production-ready
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release <4,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesredismmh3typing_extensions |
| Maintenance | Actively maintained 511 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 557,205 / month, #6,012 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaFramework :: AsyncIOIntended Audience :: DevelopersProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9Topic :: Database :: Front-EndsTopic :: System :: Distributed ComputingTopic :: UtilitiesTyping :: Typed |
Evidence: pottery-3.0.1-py3-none-any.whl
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