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          Simple Cache Service SCS

          Product Features

          Flexible Architecture

          Stand-alone Architecture

          The stand-alone architecture contains only one cache node, supports flexible reconfiguration of a single node, meets the high QPS requirements, is suitable for pure cache scenarios, and provides ultra-high cost performance.

          Master-Slave Architecture

          The master-slave architecture adopts a master-slave dual-node deployment architecture, consists of one master cache node and one slave cache node, supports the automatic failover, backup and persistence, and is applicable to scenarios with high availability and high reliability.

          Cluster Architecture

          The cluster instance uses a distributed architecture and supports cutomization of the number of multiparts and replicas. Each multipart of a dual-replica cluster adopts a master-slave high-availability architecture and can perform the automatic disaster recovery switchover and fault migration. Multiple cluster specifications can be adapted to different business pressures, and the database performance can be expanded on an on-demand basis.

          Data Security

          Data Persistence Storage

          It adopts a hybrid storage method with memory and disk to provide high-speed data reading and writing capabilities and meet the data persistence requirements.

          Backup and One-click Recovery

          Automatically back up the data every day, have strong data disaster recovery capability and support one-click data recovery free of charge, effectively prevent the data mis-operation and minimize possible business loss.

          Multi-layer network security protection

          • The VPC performs the network isolation protection directly at TCP layer.
          • DDOS protection can monitor and clear the high-traffic attacks in a real time manner.
          • Support more than 1000 IP whitelist configurations, and perform the risk control directly from access sources.

          Deep kernel optimization

          Baidu’s expert team performs the deep kernel optimization on the source Redis to effectively prevent the memory overflows and repair the security vulnerabilities to provide the protection for you.

          High Availability

          Master-Slave Nodes

          Both the master-slave version and the cluster version of the dual-replica instances have master-slave nodes to avoid service interruption caused by a single-point failure.

          Automatic Detection and Recovery

          Automatically detect the hardware failures, perform the failover when a failure occurs, and resume the services in seconds.

          Resources Isolation

          The instance-level resource isolation is able to better guarantee the stability of individual user services.

          Flexible Expansion

          Data Capacity Expansion

          The cloud database SCS for Redis supports the product configurations with multiple memory specifications, which can be upgraded online based on the business volume without interrupting the services and affecting the business.

          Business Form Expansion

          Support the single-node cache architecture and the dual-node storage architecture and adapt to different business scenarios. Perform flexible change between the standard and dual-node versions.

          Performance Expansion

          Support the flexible expansion of the storage space and throughput performance of the database system under the cluster architecture, break through the high QPS performance bottleneck of massive data, easily meet the read and write requirements of one million times per second.

          Intelligent Operations

          Monitoring Platform

          Provide the real-time monitoring and alarm of instance information such as CPU utilization, connections, and disk space utilization, and track the instance dynamics anytime and anywhere.

          Database Kernel Version Management

          Timely upgrade it and quickly repair the defects, eliminate the need to handle headache troubles in the daily version management, optimize Redis parameter configuration and maximize the use of system resources.

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