Application Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 19 Mass data real‐time analysis 6 Core Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 High Availability Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Dynamic Read/Write sources. Read/write Split‐ ting Read/write splitting can be used to cope with business access with high stress. Based on its understanding of SQL semantics and the topological awareness of the underlying

Application Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 18 Mass data real‐time analysis 6 Core Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 High Availability Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Dynamic Read/Write sources. Read/write Split‐ ting Read/write splitting can be used to cope with business access with high stress. Sharding‐ Sphere provides flexible read/write splitting capabilities and can achieve read

4 Core Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 high Availability Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Dynamic Readwrite‐Splitting SQL/PostgreSQL My SQL/PostgreSQL Connections Count Cost High Low High Supported Languages Java Only Any Any Performance Low loss Relatively High loss Low loss De centralization Yes No No Static Entry 1.1.4 Hybrid Architecture ShardingSphere‐JDBC adopts a decentralized architecture, applicable to high‐performance light‐weight OLTP application developed with Java. ShardingSphere‐Proxy provides static

4 Core Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 high Availability Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Dynamic Readwrite‐Splitting SQL/PostgreSQL My SQL/PostgreSQL Connections Count Cost High Low High Supported Languages Java Only Any Any Performance Low loss Relatively High loss Low loss De centralization Yes No No Static Entry 1.1.4 Hybrid Architecture ShardingSphere‐JDBC adopts a decentralized architecture, applicable to high‐performance light‐weight OLTP application developed with Java. ShardingSphere‐Proxy provides static

SQL/PostgreSQL My SQL/PostgreSQL Connections Count Cost High Low High Supported Languages Java Only Any Any Performance Low loss Relatively High loss Low loss De centralization Yes No No Static Entry No 1.1.4 Hybrid Architecture ShardingSphere‐JDBC adopts decentralized architecture, applicable to high‐performance light‐weight OLTP application developed with Java; ShardingSphere‐Proxy provides static satisfied the re‐ quirement of massive Internet data scenario in three aspects, performance, availability and operation cost. In performance, the relational database mostly uses B+ tree index. When

Application Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 22 Mass data real‐time analysis Re ad /w ri te S pl it ti ng Read/write splitting can be used to cope with business access with high stress. Sharding‐ Sphere provides flexible read/write splitting capabilities and can achieve read Connections Count Cost More Less Heterogeneous language Java Only Any Performance Low loss Relatively High loss Decentralization Yes No Static entry No Yes 3.2 Using ShardingSphere-Proxy ShardingSphere‐Proxy

Maximize the usage of resources . . . . . . . . . . . . . . . . . . . . . . . . . . 8 5.2.3 High Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 5.2.4 Implementation the same time, it can release operators too, so that they do not have to worry about jobs high availability and management, and can automatic operation by simply adding servers. ElasticJob is a lightweight distributed task sharding services. 2 2 Features • Elastic Schedule – Support job sharding and high availability in distributed system – Scale out for throughput and efficiency improvement – Job processing

SQL/PostgreSQL My SQL/PostgreSQL Connections Count Cost High Low High Supported Languages Java Only Any Any Performance Low loss Relatively High loss Low loss De centralization Yes No No Static Entry 1.1.4 Hybrid Architecture ShardingSphere‐JDBC adopts a decentralized architecture, applicable to high‐performance light‐weight OLTP application developed with Java. ShardingSphere‐Proxy provides static Connections Count Cost More Less Supported Languages Java Only Any Performance Low loss Relatively High loss Decentralization Yes No Static Entry No Yes ShardingSphere‐JDBC is suitable for java application

Application Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 22 Mass data real‐time analysis Re ad /w ri te S pl it ti ng Read/write splitting can be used to cope with business access with high stress. Sharding‐ Sphere provides flexible read/write splitting capabilities and can achieve read Connections Count Cost More Less Heterogeneous language Java Only Any Performance Low loss Relatively High loss Decentralization Yes No Static entry No Yes 3.2 Using ShardingSphere-Proxy ShardingSphere‐Proxy

Listener • ☐ Unified Schedule API • ☐ Unified Resource API • � Distributed Features – � High Availability – � Elastic scale in/out – � Failover – � Misfire – � Idempotency – � Reconcile • � Registry