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INTRODUCTION
Products
Active-Active地理分佈
Active-Active架構是一種資料彈性架構，透過獨立且地理分佈的叢集和節點將資料庫資訊分佈在多個資料中心。它是一個由獨立處理節點組成的網絡，每個節點都可以訪問一個公共複製資料庫，以便所有節點都可以參與一個公共應用程序，確保本地低延遲，每個區域都可以獨立運行。
高可用性
Redis 企業版透過資料複製、自動故障轉移、備份和恢復，確保高達99.999% 的高可用性。即使某些伺服器發生故障，應用程式也能持續運行，不會中斷。
多租戶技術
多租戶是指單一伺服器高效管理各種租戶的需求，每個租戶都安全且獨立地維護其資料。無需為每個新租戶提供額外的實體基礎設施，實現高效的資源利用率。
企業級安全
Redis 企業版擁有存取控制、內建加密、問責制、增強災難復原及使用外部身分提供者等多個專有功能。
Automatic Layering
Redis Enterprise 的自動分層功能讓您可以使用固態硬碟(SSD) 將資料庫擴展到DRAM 容量之外，這樣您就可以使用相同的Redis API 來建立需要大型資料集的應用程式。與僅使用DRAM 部署相比，使用SSD 可將基礎架構成本降低高達70%。
內建代理
透過處理路由、負載平衡和簡化應用程式與Redis 分片之間的客戶端連接來提高效能。
查詢和搜尋
Redis 查詢引擎透過以下搜尋和查詢功能提供增強的Redis 體驗：豐富的查詢語言、JSON 和雜湊文檔的增量索引、向量搜尋、全文搜尋、地理空間查詢、聚合
Data Integration
自動將Redis 部署與其他資料來源和接收器連接起來，以實現整個架構中的無縫資料移動和同步。
Relying on open source Redis as the kernel, constructed by the official Redis core team, provides enterprise-class high-performance cache Database solution to meet your expansion needs. --Enterprise-class caching solution developed and supported by Redis, your universal data platform.
# Create a new session and store it as a JSON document
> JSON.SET session:12345 . '{"user_id": 1001, "login_time": "2024-02-27T10:00:00Z "preferences": {"theme": "dark"}}}'
"OK"
# Fetch the entire session
> JSON.GET session:12345
"{\"user_id\":1001,\"login_time\":\"2024-02-27T10:00:00Z\ ",\"data\":{\"last_page_visited\":\"/home\",\\ "preferences\":{\"theme\":\"dark\"}}}"
# Fetch a specific part of the session
> JSON.GET session:12345 .data.preferences
"{\"theme\":\"dark\"}}"
# Update a field within the session
> JSON.SET session:12345 .data.last_page_visited '"/settings"'
"OK"
# Delete a field within the session
> JSON.DEL session:12345 .data.preferences
(integer) 1
# Delete a session
> DEL session:12345
(integer) 1
</xmp
Advanced, real-time vector retrieval capabilities, easily build your document semantic search, recommendation system and other advanced functions, empowering the intelligence and response speed of AI applications. --Explore the unlimited possibilities of big model application, semantic caching, RAG (retrieval augmentation generation) and other use cases to build your private big model.
# Create a vector index using the HNSW algorithm, 768 dimension length, and inner product distance metric
> FT.CREATE idx-videos ON HASH PREFIX 1 video: SCHEMA content_vector VECTOR HNSW 6 TYPE FLOAT32 DIM 768 DISTANCE_METRIC IP content TEXT metadata TEXT
# Add a video vector with metadata
> HSET video:0 content_vector "\xa4q\t=\xc1\xdes\xbdZ$<\xbd\xd5\xc1\x99<b\xf0\xf2<x[...\xf8 FT.SEARCH idx-videos "* => [KNN 3 @content_vector $vector AS vector_score]" RETURN 3 metadata content vector_score SORTBY vector_score LIMIT 0 3 PARAMS 2 vector "\b[\xb7;\x81\x12\x9c\xbc\xc6!...\xfe<" DIALECT 2
</xmp
The industry leader in NoSQL databases, Redis Enterprise Edition helps you build fast, reliable continuity with up to 99.999% of high availability. --Redis Enterprise Edition helps you build fast, reliable applications with up to 99.9991 TP3T of high availability, with offsite multi-activity and extremely fast access.
Create an index on "users:*"
> FT.CREATE user-idx ON JSON PREFIX 1 users: SCHEMA $.user.name AS name TEXT $.user.hobbies AS hobbies TAG $.user.age as age NUMERIC
"OK"
# Add a JSON document to be indexed
> JSON.SET users:1 $ '{"user":{"name": "John Smith ", "hobbies":["sports", "computers"], "age":23}}', {"user":{"name": "John Smith "age":23}}'
"OK"
# Search all user documents with name "John"
> FT.SEARCH user-idx '@name:(John)'
1) "1"
2) "users:1"
3) 1) "$"
2) "{\"user\":{\"name\":\"John Smith\",\ "hobbies\":[\"sports\",\"computers\"],\"age\". ":23}}"
# Search for users named "John" with hobbies "sports" or "writing " and age between 20 and 30
> FT.SEARCH user-idx '@name:(John) @hobbies:{sports | writing} @age:[20 30]'
1) "1"
2) "users:1"
3) 1) "$" 2) "{\"user\":{\"name\":\ "John Smith\",\"hobbies\":[\"sports\",\" computers\"],\"age\":23}}"
</xmp
Designed for your role
Adaptable to multiple roles
Easy to use and manage: Fully compatible with open source Redis, no extra learning costs, easy to manage with the official IDE.
Supports multi-language access:Any development language can get started quickly, whether it's the Redis OM or the officially supported Redis client libraries.
In-depth data insights:With RedisInsight, monitor and manage your data in a visualized format with full control of the data layer.
Construct real-time applications:It fully supports many common data models including JSON, provides efficient search and query functionality, and supports seamless migration from open source and Redis Stacks.
Data Governance and Security:From concept to implementation, rapidly deploy while ensuring data compliance and security.
Wide range of application scenarios:Whether it's fraud detection or claims processing, there are multiple use cases that Redis Enterprise Edition can support.
Global deployment capability:Supports deployment and scaling of real-time applications anywhere in the world to meet distributed business needs.
Unified data layer:Efficiently convert legacy data to real-time data, guaranteeing consistency and scalability across any infrastructure, whether local, hybrid or global cloud.
Stay ahead of the curve:Full support for the latest release version of Redis, the latest technology, the latest features synchronization updates.
High Availability and Disaster Tolerance:Provides up to 99.999% guaranteed uptime with very short failure recovery time and no data loss.
Break the memory limit:Applications for large data sets, scaled by auto-tiering and SSDs, save you up to 70% in storage costs while maintaining efficient performance.
Comprehensive technical support:Enjoy 24/7 technical support from Redis technology experts to protect your business continuity.
Applications
Application Scenarios
Fast, Scalable and Highly Available Cache Layers Improve Application Performance
Conversation Management
Building a Trusted View of Users with Distributed Session Management Solutions
Real Time Ranking
Delivering reliable results to millions of users
Fraud Identification
Easy handling of identity data, faster fraud detection based on AI
Real-time inventory
Delivering Optimal Performance, Scale and Availability for Online Retailing
Repeated data deletion
Organize user data through probabilistic filtering
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