. . . . . . . . . . . . . . . . . . 41 Overloaded compute node protection . . . . . . . . . . . . . . . . . . . . . . . . 41 Storage node traffic limit . . . . . . . . . . . . . . . . . . . . . . . Sharding 3.1.1 Background The traditional solution that stores all the data in one concentrated node has hardly satisfied the re‐ quirement of massive data scenario in three aspects, performance, availability stateless service, which can make all the pressure, at last, fall on the database. But the single data node or simple primary‐ replica structure has been harder and harder to take these pressures. Therefore

parsed_sql_total C OU NT ER 按类型（INSERT、UPDATE、DELETE、SELECT、DDL、DCL、D AL、TCL、RQL、RDL、RAL、RUL）分类的解析总数 routed_sql_total C OU NT ER 按类型（INS ERT、UPDATE、DELETE、SELECT）分类的路由总 数 ro uted_result_total C OU NT ER ent_execute_total C OU NT ER 语句执行总数 j dbc_statement_exe cute_errors_total C OU NT ER 语句执行错误总数 jdb c_statement_execu te_latency_millis H IS TO GR AM 语句执行耗时 jdbc_t ransac‐ tions_total C OU NT ER 事务总数，按 specific language governing permissions and # limitations under the License. # ## @section Governance-Node parameters 9.2. ShardingSphere-Proxy 157 Apache ShardingSphere document ## @param governance.enabled

Scaling Job . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Data Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Inventory Data Sharding 3.1.1 Background The traditional solution that stores all the data in one concentrated node has hardly satisfied the re‐ quirement of massive Internet data scenario in three aspects, performance stateless service, which can make all the pressure, at last, fall on the database. But the single data node or simple replica query structure has been harder and harder to take these pressures. Therefore,

Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Data Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Sharding . . . Sharding 4.2.1 Background The traditional solution that stores all the data in one concentrated node has hardly satisfied the re‐ quirement of massive data scenario in three aspects, performance, availability stateless service, which can make all the pressure, at last, fall on the database. But the single data node or simple primary‐ replica structure has been harder and harder to take these pressures. Therefore

Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Data Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Sharding . . . is ability which are unified management of centralized management, and operation in case of single node in failure. Centralized management is to uniformly manage the state of database storage nodes and scenario, circuit breaker and request limiting for a node to ensure whole database cluster can run continuously is a challenge to control ability of a single node. 4.2.3 Goal The goal of Apache ShardingSphere

. . . . . . . . . . . . . . . . . . 43 Overloaded compute node protection . . . . . . . . . . . . . . . . . . . . . . . . 43 Storage node traffic limit . . . . . . . . . . . . . . . . . . . . . . . Sharding 8.1.1 Background The traditional solution that stores all the data in one concentrated node has hardly satisfied the re‐ quirement of massive data scenario in three aspects, performance, availability stateless service, which can make all the pressure, at last, fall on the database. But the single data node or simple primary‐ replica structure has been harder and harder to take these pressures. Therefore

Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Data Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Sharding . . . is ability which are unified management of centralized management, and operation in case of single node in failure. Centralized management is to uniformly manage the state of database storage nodes and scenario, circuit breaker and request limiting for a node to ensure whole database cluster can run continuously is a challenge to control ability of a single node. 4.2. Cluster Management 23 Apache ShardingSphere

. . . . . . . . . . . . . . . . . . 43 Overloaded compute node protection . . . . . . . . . . . . . . . . . . . . . . . . 43 Storage node traffic limit . . . . . . . . . . . . . . . . . . . . . . . Sharding 8.1.1 Background The traditional solution that stores all the data in one concentrated node has hardly satisfied the re‐ quirement of massive data scenario in three aspects, performance, availability stateless service, which can make all the pressure, at last, fall on the database. But the single data node or simple primary‐ replica structure has been harder and harder to take these pressures. Therefore

config node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 instances node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 sharding node . . . . . . . . . . . . . . . . . . . . . . . 11 servers node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 leader node . . . . . . . . . . . . . . . . . . . . . . . . . . jobs immediately. 5.2.4 Implementation Principle ElasticJob does not have a job scheduling center node, but the programs based on the deployment job framework trigger the scheduling when the corresponding

. . . . . . . . . . . . . . . . . . 42 Overloaded compute node protection . . . . . . . . . . . . . . . . . . . . . . . . 42 Storage node traffic limit . . . . . . . . . . . . . . . . . . . . . . . Sharding 3.1.1 Background The traditional solution that stores all the data in one concentrated node has hardly satisfied the re‐ quirement of massive data scenario in three aspects, performance, availability stateless service, which can make all the pressure, at last, fall on the database. But the single data node or simple primary‐ replica structure has been harder and harder to take these pressures. Therefore