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

. . . . . . . . . . . . . . . . . . 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

. . . . . . . . . . . . . . . . . . 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

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,

. . . . . . . . . . . . . . . . . . 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

. . . . . . . . . . . . . . . . . . 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

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

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

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

specific language governing permissions and # limitations under the License. # ## @section Governance-Node parameters 9.2. ShardingSphere-Proxy 139 Apache ShardingSphere document ## @param governance.enabled enable or disable the governance helm chart ## governance: enabled: true ## @section Governance-Node ZooKeeper parameters zookeeper: ## @param governance.zookeeper.enabled Switch to enable or disable resources: limits: {} requests: memory: 256Mi cpu: 250m ## @section Compute-Node parameters ## compute: ## @section Compute-Node ShardingSphere-Proxy parameters 9.2. ShardingSphere-Proxy 140 Apache ShardingSphere