# valkey Valkey is an open-source, high-performance in-memory key/value datastore and data-structure server used for databases, caching, message brokering, and streaming. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The Inference Cloud Memory Layer: A Technical Dive into DigitalOcean Managed Databases DevFeed: [The Inference Cloud Memory Layer: A Technical Dive into DigitalOcean Managed Databases]() Original publisher: [Read original article]() Author: Joe Keegan Published: 2026-04-17T20:10:00Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Inference](), [Databases](), [Cloud](), [Artificial Intelligence](), [MongoDB](), [PostgreSQL](), [valkey]() Tags: [agent](), [agentic](), [ai](), [cloud](), [cloud-platform](), [databases](), [digitalocean](), [engineering](), [inference](), [mongodb](), [platform](), [postgresql](), [stateful](), [valkey]() ### AI overview This technical article presents DigitalOcean Managed Databases as the memory layer for an inference cloud. It explains how PostgreSQL, MongoDB, and Valkey can serve as systems of record for stateful AI applications, supporting persistent context, recovery in multi-stage workflows, and access to organization-specific data. ### Source excerpt As AI moves from experimental chat interfaces to production-grade agents, the need for a foundational memory layer to transform these AI-powered tasks into stateful models is apparent. The absence of a robust memory layer causes agents to lose vital statefulness, leading to: Inability to maintain long-term recall. Without persistent memory to track context across sessions, an agent might recognize specific user preferences in January but fail to apply that data months later, requiring the user to repeat the entire briefing. Vulnerability in multi-stage workflows. Lacking durable execution, there is no "save point" for recovery; consequently, a simple network interruption forces complex agentic processes, such as gathering diagnostic data via multiple tool calls, to restart entirely rather than resume from the point of failure. Disconnect from business-specific realities. If an agent cannot access private internal records or real-time operational data, it relies on general training data and guesswork, often confidently fabricating generic policies or specifications that are factually inaccurate for your organization. DigitalOcean is constantly evolving to meet this challenge, and we've entered the era of the inference cloud: A full-stack cloud platform purpose-built to run AI in production. With Gradient™AI Platform providing the specialized compute for AI applications, DigitalOcean Managed Databases serves as the foundational memory layer. Offerings from PostgreSQL, MongoDB, and Valkey function as the system of record for today's stateful AI applications, particularly so when they're connected to the DigitalOcean Agentic Inference Cloud. What is the inference cloud? The need for an inference cloud stems from a fundamental shift in how AI is being built, deployed, and used in 2026. For years, the industry's focus was on training or the capital-intensive process of building a model. But now developers are shifting to running that pre-trained model in a live product. T ## DigitalOcean Managed Databases: 2025 Releases and Updates DevFeed: [DigitalOcean Managed Databases: 2025 Releases and Updates]() Original publisher: [Read original article]() Author: Nicole Ghalwash Published: 2025-12-17T16:37:44Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Databases](), [Digital Ocean](), [releases](), [observability](), [Scalability](), [valkey](), [MongoDB](), [MySQL](), [Kafka]() Tags: [2025](), [databases](), [digitalocean](), [kafka](), [mongodb](), [mysql](), [observability](), [releases](), [scalability](), [valkey]() ### AI overview A recap of DigitalOcean Managed Databases releases in 2025, covering database engine updates, increased storage limits, Valkey caching, MongoDB access controls, observability and hardening improvements, Kafka Schema Registry support, and an MCP Server for managing cloud resources through natural-language commands. ### Source excerpt 2025 was a big year for DigitalOcean Managed Databases, packed with meaningful enhancements designed to deliver more power, flexibility, and simplicity for developers and businesses of all sizes. With performance boosts and new database engines introduced to improve scalability, automation, and observability, this year's releases focused on making it easier than ever to build and run reliable data-backed applications on DigitalOcean. In this recap, we'll walk through the most impactful launches in both the first and second half of the year and how they help teams move faster while keeping complexity low. First, let's take a look at our releases in the first half of the year (H1): - Managed PostgreSQL support for v17 [February] - Managed MongoDB support for v8 [March] - Support for up to 20TB for Managed MySQL and 30TB for PostgreSQL [March] - Introducing DigitalOcean Managed Caching for Valkey [April] - Introducing Role-Based Access Control to DigitalOcean Managed MongoDB with Predefined Roles [May] - Database Observability, Monitoring, and Hardening Advancements [June] - Support for Kafka Schema Registry [July] The second half (H2) of 2025 saw even more big developments for DigitalOcean Managed Databases: MCP Server (August) The DigitalOcean MCP (Model Context Protocol) Server lets you manage your cloud resources using simple, natural-language commands through AI-powered tools like Cursor, Claude, or your own custom LLMs. Running locally, it streamlines tasks such as provisioning Managed Databases, making cloud operations faster, easier, and more intuitive for developers. DigitalOcean MCP Server marks a major step forward in bringing AI directly into cloud infrastructure workflows. It allows users to seamlessly integrate cloud management into AI assistants or custom-built agents while eliminating context switching, manual API calls, and constant console navigation. MCP Servers make managing cloud infrastructure with LLMs faster, more secure, and far more accessible ## Heroku Key-Value Store Now Supports Valkey 8.1 with JSON and Bloom Modules DevFeed: [Heroku Key-Value Store Now Supports Valkey 8.1 with JSON and Bloom Modules]() Original publisher: [Read original article]() Author: Jonathan Brown Published: 2025-07-02T15:00:03Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [valkey](), [Redis](), [modules](), [Latency](), [Open Source](), [Benchmark]() Tags: [availability](), [benchmarks](), [caching](), [cost](), [heroku](), [heroku-key-value-store](), [json](), [key-value-store](), [modules](), [news](), [open-source](), [performance](), [redis](), [valkey]() ### AI overview Heroku announces general availability of Valkey v8.1 for Heroku Key-Value Store, adding Valkey Bloom and ValkeyJSON modules. The release includes performance, memory-efficiency, and I/O-threading improvements, with upgrade instructions and benchmark comparisons to Valkey 7.2. ### Source excerpt We're thrilled to announce the general availability of Valkey v8.1 in Redis OSS compatible Heroku Key-Value Store. This isn't just an incremental update; it's a significant leap forward, bringing enhanced performance and greater efficiency. To add to this excitement, we're bringing powerful new module capabilities to v8.1, with Valkey Bloom and ValkeyJSON. For years, Heroku [...] The post Heroku Key-Value Store Now Supports Valkey 8.1 with JSON and Bloom Modules appeared first on Heroku. ## Scaling Nextdoor's Datastores: Part 3 DevFeed: [Scaling Nextdoor's Datastores: Part 3]() Original publisher: [Read original article]() Author: Ronak Shah Published: 2025-03-19T15:08:43Z Content type: article Language: en Sources: [Nextdoor]() Topics: [Caching](), [Databases](), [schema-evolution](), [Object-relational mapping](), [Django](), [Python](), [Redis](), [valkey]() Tags: [blog](), [cache](), [cache-invalidation](), [caching](), [compatibility](), [database](), [django](), [lua](), [python](), [redis](), [schema](), [serialization-format](), [thundering-herd](), [valkey]() ### AI overview Part 3 of Nextdoor's datastore-scaling series explains how applications serialize database objects for Redis or Valkey look-aside caches. It describes compatibility problems caused by runtime, package, and schema changes, including cache misses and thundering-herd effects during migrations. ### Source excerpt In this part of the Scaling Nextdoor's Datastores blog series, we'll explore how the Core-Services team at Nextdoor serializes database data for caching while ensuring forward and backward compatibility between the cache and application code. In part 1 of this series we discussed how ORMs, object-relational mapping frameworks, help abstract away database specific schemas and queries from application code. Developers simply utilize objects in their application's language to access database data. Here's a simple example of using Python's Django ORM to define a model: from django.db import models class Users(models.Model): first_name = models.CharField(max_length=30) last_name = models.CharField(max_length=30) The associated SQL create table would look like: CREATE TABLE users ( "id" bigint NOT NULL PRIMARY KEY GENERATED BY DEFAULT AS IDENTITY, "first_name" varchar(30) NOT NULL, "last_name" varchar(30) NOT NULL ); Developers would then access database data like this: user_id = 123 user = User.objects.get(id=user_id) print(user.first_name)Object Byte Serialization for Caching An issue arises when adding a look-aside cache such as Redis/Valkey to an application: How do you store what you got from the database in the cache? A common solution to caching complex objects, such as those from ORMs, is object byte serialization. This process converts language objects into bytes before storing them in the cache. When reading from the cache the process is done in reverse where the byte data is turned into language objects. For instance in Python this is often done with the pickle package. The interaction between the application, database, and the cache looks like this: Look-Aside Cacheimport pickle # Try getting from cache ('None' if not in cache) user_bytes = cache.get("user_123") if user_bytes is not None: # Read bytes using pickle user = pickle.loads(user_bytes) else: # Fetch from database user = User.objects.get(id=123) # Convert to bytes user_bytes = pickle.dumps(user) # Sto