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Valkey 7.2 On Memorystore: Open-Source Key-Value Service

The 100% open-source key-value service Memorystore for Valkey is launched by Google Cloud.

In order to give users a high-performance, genuinely open-source key-value service, the Memorystore team is happy to announce the preview launch of Valkey 7.2 support for Memorystore.

Memorystore for Valkey

A completely managed Valkey Cluster service for Google Cloud is called Memorystore for Valkey. By utilizing the highly scalable, reliable, and secure Valkey service, Google Cloud applications may achieve exceptional performance without having to worry about handling intricate Valkey deployments.

In order to guarantee high availability, Memorystore for Valkey distributes (or “shards”) your data among the primary nodes and duplicates it among the optional replica nodes. Because Valkey performance is greater on many smaller nodes rather than fewer bigger nodes, the horizontally scalable architecture outperforms the vertically scalable architecture in terms of performance.

Memorystore for Valkey is a game-changer for enterprises looking for high-performance data management solutions reliant on 100% open source software. It was added to the Memorystore portfolio in response to customer demand, along with Memorystore for Redis Cluster and Memorystore for Redis. From the console or gcloud, users can now quickly and simply construct a fully-managed Valkey Cluster, which they can then scale up or down to suit the demands of their workloads.

Thanks to its outstanding performance, scalability, and flexibility, Valkey has quickly gained popularity as an open-source key-value datastore. Valkey 7.2 provides Google Cloud users with a genuinely open source solution via the Linux Foundation. It is fully compatible with Redis 7.2 and the most widely used Redis clients, including Jedis, redis-py, node-redis, and go-redis.

Valkey is already being used by customers to replace their key-value software, and it is being used for common use cases such as caching, session management, real-time analytics, and many more.

Customers may enjoy a nearly comparable (and code-compatible) Valkey Cluster experience with Memorystore for Valkey, which launches with all the GA capabilities of Memorystore for Redis Cluster. Similar to Memorystore for Redis Cluster, Memorystore for Valkey provides RDB and AOF persistence, zero-downtime scaling in and out, single- or multi-zone clusters, instantaneous integrations with Google Cloud, extremely low and dependable performance, and much more. Instances up to 14.5 TB are also available.

Memorystore for Valkey, Memorystore for Redis Cluster, and Memorystore for Redis have an exciting roadmap of features and capabilities.

The momentum of Valkey

Just days after Redis Inc. withdrew the Redis open-source license, the open-source community launched Valkey in collaboration with the Linux Foundation in March 2024 (1, 2, 3). Since then, they have had the pleasure of working with developers and businesses worldwide to propel Valkey into the forefront of key-value data stores and establish it as a premier open source software (OSS) project. Google Cloud is excited to participate in this community launch with partners and industry experts like Snap, Ericsson, AWS, Verizon, Alibaba Cloud, Aiven, Chainguard, Heroku, Huawei, Oracle, Percona, Ampere, AlmaLinux OS Foundation, DigitalOcean, Broadcom, Memurai, Instaclustr from NetApp, and numerous others. They fervently support open source software.

The Valkey community has grown into a thriving group committed to developing Valkey the greatest open source key-value service available thanks to the support of thousands of enthusiastic developers and the former core OSS Redis maintainers who were not hired by Redis Inc.

With more than 100 million unique active users each month, Mercado Libre is the biggest finance, logistics, and e-commerce company in Latin America. Diego Delgado discusses Valkey with Mercado Libre as a Software Senior Expert:

At Mercado Libre, Google Cloud need to handle billions of requests per minute with minimal latency, which makes caching solutions essential. Google Cloud especially thrilled about the cutting-edge possibilities that Valkey offers. They have excited to investigate its fresh features and add to this open-source endeavor.”

The finest is still to come

By releasing Memorystore for Valkey 7.2, Memorystore offers more than only Redis Cluster, Redis, and Memcached. And Google Cloud is even more eager about Valkey 8.0’s revolutionary features. Major improvements in five important areas performance, reliability, replication, observability, and efficiency were introduced by the community in the first release candidate of Valkey 8.0. With a single click or command, users will be able to accept Valkey 7.2 and later upgrade to Valkey 8.0. Additionally, Valkey 8.0 is compatible with Redis 7.2, exactly like Valkey 7.2 was, guaranteeing a seamless transition for users.

The performance improvements in Valkey 8.0 are possibly the most intriguing ones. Asynchronous I/O threading allows commands to be processed in parallel, which can lead to multi-core nodes working at a rate that is more than twice as fast as Redis 7.2. From a reliability perspective, a number of improvements provided by Google, such as replicating slot migration states, guaranteeing automatic failover for empty shards, and ensuring slot state recovery is handled, significantly increase the dependability of Cluster scaling operations. The anticipation for Valkey 8.0 is already fueling the demand for Valkey 7.2 on Memorystore, with a plethora of further advancements across several dimensions (release notes).

Similar to how Redis previously expanded capability through modules with restricted licensing, the community is also speeding up the development of Valkey’s capabilities through open-source additions that complement and extend Valkey’s functionality. The capabilities covered by recently published RFCs (“Request for Comments”) include vector search for extremely high performance vector similarly search, JSON for native JSON support, and BloomFilters for high performance and space-efficient probabilistic filters.

Former vice president of Gartner and principal analyst of SanjMo Sanjeev Mohan offers his viewpoint:

The advancement of community-led initiatives to offer feature-rich, open-source database substitutes depends on Valkey. Another illustration of Google’s commitment to offering really open and accessible solutions for customers is the introduction of Valkey support in Memorystore. In addition to helping developers looking for flexibility, their contributions to Valkey also support the larger open-source ecosystem.

It seems obvious that Valkey is going to be a game-changer in the high-performance data management area with all of the innovation in Valkey 8.0, as well as the open-source improvements like vector search and JSON, and for client libraries.

Valkey is the secret to an OSS future

Take a look at Memorystore for Valkey right now, and use the UI console or a straightforward gcloud command to establish your first cluster. Benefit from OSS Redis compatibility to simply port over your apps and scale in or out without any downtime.

Read more on govindhtech.com

Key Differences Between AI and Human Communication: Mechanisms, Intent, and Understanding

The differences between the way an AI communicates and the way a human does are significant, encompassing various aspects such as the underlying mechanisms, intent, adaptability, and the nature of understanding. Here’s a breakdown of key differences:

1. Mechanism of Communication:

AI: AI communication is based on algorithms, data processing, and pattern recognition. AI generates responses by analyzing input data, applying pre-programmed rules, and utilizing machine learning models that have been trained on large datasets. The AI does not understand language in a human sense; instead, it predicts likely responses based on patterns in the data.

Humans: Human communication is deeply rooted in biological, cognitive, and social processes. Humans use language as a tool for expressing thoughts, emotions, intentions, and experiences. Human communication is inherently tied to understanding and meaning-making, involving both conscious and unconscious processes.

2. Intent and Purpose:

AI: AI lacks true intent or purpose. It responds to input based on programming and training data, without any underlying motivation or goal beyond fulfilling the tasks it has been designed for. AI does not have desires, beliefs, or personal experiences that inform its communication.

Humans: Human communication is driven by intent and purpose. People communicate to share ideas, express emotions, seek information, build relationships, and achieve specific goals. Human communication is often nuanced, influenced by context, and shaped by personal experiences and social dynamics.

3. Understanding and Meaning:

AI: AI processes language at a syntactic and statistical level. It can identify patterns, generate coherent responses, and even mimic certain aspects of human communication, but it does not truly understand the meaning of the words it uses. AI lacks consciousness, self-awareness, and the ability to grasp abstract concepts in the way humans do.

Humans: Humans understand language semantically and contextually. They interpret meaning based on personal experience, cultural background, emotional state, and the context of the conversation. Human communication involves deep understanding, empathy, and the ability to infer meaning beyond the literal words spoken.

4. Adaptability and Learning:

AI: AI can adapt its communication style based on data and feedback, but this adaptability is limited to the parameters set by its algorithms and the data it has been trained on. AI can learn from new data, but it does so without understanding the implications of that data in a broader context.

Humans: Humans are highly adaptable communicators. They can adjust their language, tone, and approach based on the situation, the audience, and the emotional dynamics of the interaction. Humans learn not just from direct feedback but also from social and cultural experiences, emotional cues, and abstract reasoning.

5. Creativity and Innovation:

AI: AI can generate creative outputs, such as writing poems or composing music, by recombining existing patterns in novel ways. However, this creativity is constrained by the data it has been trained on and lacks the originality that comes from human creativity, which is often driven by personal experience, intuition, and a desire for expression.

Humans: Human creativity in communication is driven by a complex interplay of emotions, experiences, imagination, and intent. Humans can innovate in language, create new metaphors, and use language to express unique personal and cultural identities. Human creativity is often spontaneous and deeply tied to individual and collective experiences.

6. Emotional Engagement:

AI: AI can simulate emotional engagement by recognizing and responding to emotional cues in language, but it does not experience emotions. Its responses are based on patterns learned from data, without any true emotional understanding or empathy.

Humans: Human communication is inherently emotional. People express and respond to emotions in nuanced ways, using tone, body language, and context to convey feelings. Empathy, sympathy, and emotional intelligence play a crucial role in human communication, allowing for deep connections and understanding between individuals.

7. Contextual Sensitivity:

AI: AI's sensitivity to context is limited by its training data and algorithms. While it can take some context into account (like the previous messages in a conversation), it may struggle with complex or ambiguous situations, especially if they require a deep understanding of cultural, social, or personal nuances.

Humans: Humans are highly sensitive to context, using it to interpret meaning and guide their communication. They can understand subtext, read between the lines, and adjust their communication based on subtle cues like tone, body language, and shared history with the other person.

8. Ethical and Moral Considerations:

AI: AI lacks an inherent sense of ethics or morality. Its communication is governed by the data it has been trained on and the parameters set by its developers. Any ethical considerations in AI communication come from human-designed rules or guidelines, not from an intrinsic understanding of right or wrong.

Humans: Human communication is deeply influenced by ethical and moral considerations. People often weigh the potential impact of their words on others, considering issues like honesty, fairness, and respect. These considerations are shaped by individual values, cultural norms, and societal expectations.

The key differences between AI and human communication lie in the underlying mechanisms, the presence or absence of intent and understanding, and the role of emotions, creativity, and ethics. While AI can simulate certain aspects of human communication, it fundamentally operates in a different way, lacking the consciousness, experience, and meaning-making processes that characterize human interaction.

I wish non-tech people would get into open source. You should post your crochet pattern on github. I want to see your wip novel on a webpage running Wikimedia.

Cas d'utilisation de l'agent IA avec bases de données

Cybersec --> Cybersecurity

In September 2010, I began using Tumblr sporadically as a cache of various meaningful quotes on the topic of cybersecurity. 

I am moving to a new name. This (cybersecurity.tumblr.com) is the new Tumblr page. 

If you want to see all of my past materials, feel free to visit cybersec.tumblr.com.

Enjoy, and feel free to provide any suggestions/comments! 

An overview of the Planner-Actor-Mediator paradigm

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