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title: Valkey · valkey-bundle: One stop shop for real-time...
description: Valkey-bundle packages Valkey 8.1.1 with four modules — Valkey JSON, Valkey Bloom, Valkey Search, and Valkey LDAP — into a single container to simplify...
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# Valkey · valkey-bundle: One stop shop for real-time applications

**[Valkey](https://daily.dev/sources/valkey-blog)** · 16 min read · 0 upvotes · 0 comments

## Summary

Valkey-bundle packages Valkey 8.1.1 with four modules — Valkey JSON, Valkey Bloom, Valkey Search, and Valkey LDAP — into a single container to simplify deployment for real-time applications. It covers getting started with Docker, CLI examples for each module (JSON document storage, bloom filters for deduplication, HNSW-based vector similarity search, and LDAP authentication with ACL integration), production configuration guidance, and a worked example building a personalized ad platform combining rich user profiles, vector-based recommendations, ad-fatigue prevention, and role-based access control.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://valkey.io/blog/valkey-bundle-one-stop-shop-for-low-latency-modern-applications>

## Questions this post answers

### What modules are included in the valkey-bundle container and what version of Valkey does it use?

Valkey-bundle bundles Valkey 8.1.1 with four modules in a single container: Valkey JSON 1.0 for native JSON document storage and JSONPath queries, Valkey Bloom 1.0 for space-efficient probabilistic membership testing, Valkey Search 1.0 for HNSW-based vector similarity search, and Valkey LDAP 1.0 for enterprise authentication integrated with Valkey ACLs.

_daily.dev helps engineers track new module bundles like this before adopting them into production stacks._

### How much memory can bloom filters save compared to using a Redis or Valkey SET for deduplication?

Bloom filters in Valkey Bloom can reduce memory usage by up to 98% compared to a SET data structure while maintaining fast lookup times, with 93-98% savings typical for items using UUIDs for uniqueness. With a 1% false positive rate, a single filter can track roughly 112 million items within a 128MB memory limit.

_Developers comparing data-structure tradeoffs for caching often surface these benchmarks on daily.dev._

### What algorithms does Valkey Search use for vector similarity search and what latency can it achieve?

Valkey Search uses Hierarchical Navigable Small World (HNSW) for approximate nearest neighbor search and K-Nearest Neighbors (KNN) for exact matching, achieving single-digit millisecond latency at 99% recall even with billions of vectors. It supports FLOAT32 vectors with L2, Inner Product, and Cosine distance metrics, plus hybrid queries combining vector search with tag and numeric filters.

_Teams evaluating vector search options for AI features can follow these details on daily.dev._

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---

Tags: [#authentication](https://daily.dev/tags/authentication), [#redis](https://daily.dev/tags/redis), [#vector-search](https://daily.dev/tags/vector-search), [#valkey](https://daily.dev/tags/valkey)

[View this post on daily.dev](https://daily.dev/posts/valkey-valkey-bundle-one-stop-shop-for-real-time-applications-9jpmzb0bu)

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