high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 18 Mass data real‐time analysis in OLAP scenarios . . . . . . . . . . . . . . . . . . . 19 3.1.5 Related References . . . . . utilizing data in a complex data environment. Sharding‐ Sphere provides complex data query and analysis capabilities across data sources, simpli‐ fying the data aggregation from different data locations enhancement capabili‐ ties through traffic deformation, redirection, governance, authentication, and analysis. 1.2.3 Pluggable: Building database function ecology The pluggable architecture of Apache ShardingSphere

high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 19 Mass data real‐time analysis in OLAP scenarios . . . . . . . . . . . . . . . . . . . 20 3.1.5 Related References . . . . . enhancement capabili‐ ties through traffic deformation, redirection, governance, authentication, and analysis. 1.2. Design Philosophy 5 Apache ShardingSphere document, v5.2.0 1.2.3 Pluggable: Building database in OLTP scenarios. 3.1. Sharding 19 Apache ShardingSphere document, v5.2.0 Mass data real-time analysis in OLAP scenarios In traditional database architecture, if users want to analyze data, they need

high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 22 Mass data real‐time analysis in OLAP scenarios . . . . . . . . . . . . . . . . . . . 23 8.1.5 Related References . . . . . utilizing data in a complex data environment. Sharding‐ Sphere provides complex data query and analysis capabilities across data sources, simpli‐ fying the data aggregation from different data locations enhancement capabili‐ ties through traffic deformation, redirection, governance, authentication, and analysis. 2.3 Pluggable: Building database function ecology The pluggable architecture of Apache ShardingSphere

high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 22 Mass data real‐time analysis in OLAP scenarios . . . . . . . . . . . . . . . . . . . 23 8.1.5 Related References . . . . . utilizing data in a complex data environment. Sharding‐ Sphere provides complex data query and analysis capabilities across data sources, simpli‐ fying the data aggregation from different data locations enhancement capabili‐ ties through traffic deformation, redirection, governance, authentication, and analysis. 2.3 Pluggable: Building database function ecology The pluggable architecture of Apache ShardingSphere

storage and computing method are facing innovation all the time. Transaction, big data, association analysis, Internet of things and other scenarios subdivided quickly, a single database can not apply to splitting routes read and write separately to primary database and replica databases according SQL analysis. 4.4. Readwrite-splitting 45 Apache ShardingSphere document, v5.0.0 Data in readwrite‐splitting performance diagnosis of distributed systems, including chain demonstration, application topology analysis and so on. Apache ShardingSphere is not responsible for gathering, storing and demonstrating APM

storage and computing method are facing innovation all the time. Transaction, big data, association analysis, Internet of things and other scenarios subdivided quickly, a single database can not apply to splitting routes read and write separately to primary database and replica databases according SQL analysis. Data in readwrite‐splitting nodes are consistent, whereas that in shards is not. The combined observable data of the system. Its main functions include per‐ formance index monitoring, call stack analysis, service topology, etc. Apache ShardingSphere is not responsible for gathering, storing and demonstrating

storage and computing method are facing innovation all the time. Transaction, big data, association analysis, Internet of things and other scenarios subdivided quickly, a single database can not apply to splitting routes read and write separately to primary database and replica databases according SQL analysis. Data in readwrite‐splitting nodes are consistent, whereas that in shards is not. The combined observable data of the system. Its main functions include per‐ formance index monitoring, call stack analysis, service topology, etc. Apache ShardingSphere is not responsible for gathering, storing and demonstrating

operations separately to the primary database and the replica database according to SQL meaning analysis. Data in replica query split nodes is consistent, whereas that in horizontal shards is not. The performance diagnosis of distributed systems, including chain demonstration, application topology analysis and so on. Apache ShardingSphere is not responsible for gathering, storing and demonstrating APM should ShardingSphere be used to achieve business requirements? New Business Business scenario analysis: The newly launched business is relatively simple because everything starts from scratch and there