November 2024 Summaries

In November, Redis released version 8.0-M02 with performance improvements and advanced scaling features for data structures like JSON, time series, and probabilistic types. The Community Edition now supports horizontal and vertical scaling for the Redis Query Engine. Additionally, Redis Software 7.8.2 was made available, offering new features such as support for Redis Community Edition 7.4 with hash field expiration, significant memory reduction for the Redis Query Engine for vector use cases, and client-side caching. Microsoft Azure also introduced Azure Managed Redis, a fully managed Redis solution developed in collaboration with Microsoft Azure. Furthermore, Redis University launched a new Redis Insight course to help users explore and interact with their Redis data using powerful analysis tools. Lastly, the go-redis client library now supports advanced data modeling capabilities, including secondary indexing, JSON documents, and powerful search features.

Microsoft has introduced Azure Managed Redis, a fully-managed first-party Redis offering on its Azure cloud platform. This marks the first time a major cloud service provider offers a licensed, multi-tiered Redis service. The new service is available in public preview and builds on existing integration with Microsoft Azure on the Enterprise and Enterprise Flash tiers for Azure Cache for Redis. Users of Azure Managed Redis will gain access to features previously found only on higher tiers, as well as upcoming versions of Redis. This new offering provides developers with flexibility and scalability for various workloads while delivering real-time data processing capabilities. It also meets regulatory compliance standards and integrates with Azure AI for faster development of GenAI applications.

As companies increasingly deploy Generative Artificial Intelligence (GenAI) applications in production, new challenges have emerged that focus more on scaling and securing usage, especially when interfacing with users outside the organization. To address these issues, companies are starting to use AI gateways as a key component of their AI infrastructure. An AI Gateway simplifies, secures, and governs access to Large Language Models (LLMs) within an enterprise setting by acting as a centralized platform for managing AI workflows. Key features of an AI gateway include unified API, rate limiting, routing based on intent, caching, PII redaction, guardrails, usage tracking and chargebacks, and credentials management. Companies can build their own custom platforms or use established open-source solutions like LiteLLM, Guardrails AI, Langfuse, Hashicorp vault, and Redis to construct GenAI gateways or platforms.

The go-redis client library now supports advanced data modeling, including secondary indexing and JSON documents. This allows for flexible data management and search capabilities within the database. Users can create simple JSON documents and execute standard commands, as well as model complex data structures such as shopping carts. Additionally, the library supports semantic search through vector search functionality, enabling powerful applications in AI and GenAI development.

Redis has announced the second milestone of its advanced and performant offering, Redis 8. The latest version offers significant performance improvements compared to previous versions. Latency for commonly used commands such as ZADD, SMEMBERS, and HGETALL have been reduced by up to 36%, 28%, and 10% respectively. Additionally, the new release includes two scaling features of the Redis Query Engine that were previously only available on Redis Cloud and Redis Software. These enhancements make Redis Community Edition (CE) the fastest vector database available for free.

In October, Redis released several updates and enhancements for its users. These include new buttons for Redis Data Integration (RDI) in the latest version of Redis Insight, allowing multiple elements to be added to list data types at once, and syntax auto-completion with index names, fields, etc. in the workbench for Redis Query Engine. Additionally, RDI 1.4 now supports helm charts for Kubernetes customers, while a new "viewer" role has been introduced for Redis Cloud API users. Lastly, Java and Python clients for client-side caching are now available through Jedis and redis-py client libraries.