3. More commands for atomicity and expirations
HGETDEL: Atomic fetch and delete
A frequent application pattern involves reading a hash field and deleting it immediately (such as consuming single-use authentication tokens or short-lived session states). Valkey 9.1 introduces HGETDEL, which retrieves the value of a hash field and deletes it atomically in a single network round-trip.
Real-world example:
HSET user:1001 temp_token “abcde”
(integer) 1
HGETDEL user:1001 FIELDS 1 temp_token
1. “abcde”
HGET user:1001 temp_token
(nil)
MSETEX: Shared expiration for multiple keys
To eliminate multi-command pipeline overhead, the new MSETEX command enables setting multiple keys simultaneously with a single, shared expiration time.
Real-world example: Setting up a temporary session state where multiple distinct keys must expire together in 300 seconds:
MSETEX 2 session:auth “ok” session:user_id “1001” EX 300
(integer) 1
TTL session:auth
(integer) 300
Enhanced HSETEX with conditional flags
HSETEX now supports the NX (only set if the field does not exist) and XX (only set if the field exists) conditional flags.
Real-world example: Initializing a rate-limit threshold field with a 1-hour TTL, ensuring you don’t overwrite an existing active limit:
HSETEX config:123 NX EX 3600 FIELDS 1 “rate_limit” “100”
(integer) 1
Built on Memorystore for Valkey 9.0
The release of Valkey 9.1 builds upon the major updates we unveiled for Memorystore for Valkey at Google Cloud Next ’26:
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Built-in modules for AI & vector workloads: Native JSON support and Bloom filters enable fast document querying and membership checks.
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Six new node sizes: To help you manage costs and scale, we added six new node sizes.
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Small Size Nodes: Custom-Pico (1.25 GB), Custom-Micro (2.5 GB), and Custom-Mini (3.5 GB) for lightweight microservices and dev/test environments. These are only available for cluster mode disabled environments.
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High CPU and Large SKUs: HighCPU-Medium (8 vCPU/13 GB) and Standard-Large (8 vCPU/26 GB) optimized for CPU-heavy applications.
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XXL SKU: Highmem-XXLarge with 110 GB RAM and 16 vCPUs per node for massive cluster consolidation to power your most demanding workloads.
(Note: The figures above are based on open-source benchmarks; actual performance improvements will vary depending on your specific workloads.)
Migrating to fully managed Memorystore for Valkey
Having to self-manage your Redis OSS /Valkey caching layers drains valuable engineering bandwidth and creates operational friction during scaling. We are also excited to announce a new migration workflow to Memorystore for Valkey.
With this release, migrating your infrastructure is straightforward, fully managed, and requires a simple configuration change on your application to point to Memorystore for Valkey once your data is migrated. This workflow is generally available.To move off self-managed Redis or Valkey to fully managed Memorystore for Valkey, follow these four steps:
1. Provision the target instance: Deploy a Memorystore for Valkey instance configured with your required shard count, node sizing, and clustered database options.
2. Establish online replication: Initiate continuous, dual-sync online migration directly from your source database to Memorystore.
3. Validate data synchronization: Monitor replication metrics in real time to verify full dataset alignment and low-latency replication health.
4. Execute the cutover: Switch application connection endpoints over to Memorystore for Valkey to start using the new cache.
What Memorystore for Valkey customers are saying
Already, over 95% of the top 100 Google Cloud customers already rely on Google Cloud Memorystore to power demanding, high-throughput workloads, led by increasing numbers of Memorystore for Valkey users.
Consider the fast-paced world of live sports, where delivering a flawless digital experience is of utmost importance. When a game-changing play happens, millions of fans immediately reach for their devices to check real-time stats, watch highlights, and engage with interactive features. These massive, unpredictable traffic spikes require an underlying architecture capable of immense scale. For organizations like Major League Baseball (MLB) , a partner since Valkey’s early days, managing unpredictable traffic spikes without compromising performance is essential.
“We trust Memorystore for Valkey to power the massive scale of live baseball, delivering real-time stats and uninterrupted digital experiences to millions of fans. As we look ahead, we are incredibly excited about the Memorystore for Valkey 9.1 launch. The engine optimizations and latency enhancements will give us even more horsepower to handle the most unpredictable game-day traffic spikes, ensuring fans get the best technology-powered experience the game has to offer.” – Rob Engel, SVP of Software Engineering, Major League Baseball
Beyond the stadium, the retail industry faces its own intense scaling challenges, particularly during major shopping holidays or flash sales. Modern e-commerce platforms rely on real-time personalization, dynamic pricing, and instant inventory updates to keep shoppers engaged. A lag of even a few milliseconds can disrupt the customer journey and impact the bottom line. To maintain a competitive edge, leading retailers such as Target require ultra-responsive caching layers to power their most crucial customer-facing platforms.
“By leveraging Google Cloud Memorystore for Valkey, Target delivers ultra-low-latency, resilient caching for personalization services. We look forward to leveraging the performance enhancements in Valkey 9.1 to make our personalization platform even faster, more scalable, and more resilient during periods of peak demand.” – Scott Weide and Sumanth Huddar, Senior Engineering Managers, Target
The demand for these ultra-low-latency architectures extends far beyond sports and retail. Across the digital landscape, organizations in banking, AI-native development, digital streaming, and telecommunications all share a common mandate: the need for superfast, highly available caches. Whether it is processing high-frequency financial transactions, serving complex machine learning inferences in real time, delivering seamless global video streams, or routing immense volumes of telecom data, microsecond latency is the new baseline for success.
Make the move to Valkey
Stop letting cache bottlenecks slow down your most demanding applications. Experience the performance, dynamic scalability, and enhanced security of Memorystore for Valkey 9.1 today.
