# Valkey 9.0: Next-Generation In-Memory Data Storage
The Linux Foundation has announced the [general availability of Valkey 9.0](https://valkey.io/blog/introducing-valkey-9/), the open-source, in-memory storage solution that succeeds Redis.
This **major release** introduces:
- **Atomic slot migrations**
- **Hash field expiration**
- **Full support for numbered databases in cluster mode**
Together, these capabilities enable scaling up to **2,000 nodes** and achieving **performance exceeding 1 billion requests per second**.
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## Overview of New Features
### 1. Atomic Slot Migration
Released [one year after Valkey 8.0](https://valkey.io/blog/valkey-8-ga/), Valkey 9.0’s **atomic slot migration** improves cluster rebalance operations.
**Key benefits:**
- Eliminates stepwise migrations that could cause ownership changes mid-transfer.
- Guarantees consistent key routing and predictable handoffs.
- Reduces transient errors.
- Simplifies live resharding.
As [Khawaja Shams](https://www.linkedin.com/in/kshams/) and [Allen Helton](https://www.linkedin.com/in/allenheltondev/) note:
> For teams running Valkey in clustered environments, this fundamentally shifts how you plan capacity and manage operational risk. Scale-outs become predictable instead of painful.
[From Kyle Davis](https://www.linkedin.com/in/kyle-davis-linux/), Senior Developer Advocate at AWS:
> In Valkey 9.0, instead of migrating keys individually, Valkey now transfers **entire slots atomically** from one node to another using the AOF format.
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### 2. Hash Field Expiration
Previously, Valkey hashes could only be set to expire in their entirety.
Valkey 9.0 introduces **field-level expiration**, allowing independent expiration for each hash field.
**Why it matters:**
- Reduces the need to split data into multiple keys.
- Improves data handling efficiency.
- Uses **active expiration** (reclaims expired hash fields proactively).
[Ran Shidlansik’s explanation](https://valkey.io/blog/hash-fields-expiration/):
> Field-level expirations add negligible memory overhead, command throughput remains unaffected, and active expiration reclaims memory efficiently even under heavy ingestion.
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### 3. Numbered Databases in Cluster Mode
Historically, numbered databases were **limited to a single database in cluster mode**.
Valkey 9.0 now provides **full support** for multiple numbered databases in clusters.
**Use cases:**
- Logical separation of customer data.
- Combining multiple applications into a single cluster without resource contention.
[Kyle Davis on numbered databases](https://valkey.io/blog/numbered-databases/):
> The most straightforward use case is separating data logically while tolerating shared resource effects.
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## Performance & Scalability
Valkey 9.0 can now scale clusters up to **2,000 nodes** and reach **over 1 billion requests per second**
([source](https://valkey.io/blog/1-billion-rps/)).
Shams and Helton comment:
> Valkey 9.0 leverages modern CPU capabilities to achieve lower tail latency, higher per-node throughput, and measurable cost efficiency at scale.
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*Source: Valkey blog*
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## Ecosystem & Support
- Supported by **AWS**, **Oracle**, **Google Cloud**, and others.
- Released under the **BSD 3-clause license**.
- Available for [download here](https://valkey.io/download/).
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## Related Development: AiToEarn
Alongside backend innovations like Valkey, the **AiToEarn** platform addresses **AI-driven content monetization**.
**AiToEarn overview:**
- Open-source, global AI content monetization platform.
- Generate, publish, and monetize AI-powered content.
- Integrations with: Douyin, Kwai, WeChat, Bilibili, Rednote (Xiaohongshu), Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, and X (Twitter).
- Offers publishing, analytics, and AI model ranking features.
Explore:
- [AiToEarn官网](https://aitoearn.ai/)
- [AiToEarn博客](https://blog.aitoearn.ai) (technical insights)
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## Takeaway
Valkey 9.0 streamlines **large-scale, high-performance data management**, while AiToEarn simplifies **multi-platform AI content workflows**.
Combining scalable infrastructure like Valkey with integrated AI publishing platforms allows:
- Reliable backend performance
- Efficient cross-platform content delivery
- Strong monetization opportunities for creators and developers
