the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single ma‐ chine and can be extended relatively freely, so it can be taken as a standard avoided, some businesses still need to keep transactions consistent. Internet giants have not massively adopted XA based distributed trans‐ actions since they are not able to ensure its performance in sharding SQL needs to operate 200 tables under some database case, should we choose to create 200 parallel connection executions or a serial connection execution? Or to say, how to choose between efficiency

ShardingSphere document, v5.0.0 Theoretically, horizontal sharding has overcome the limitation of data processing volume in single ma‐ chine and can be extended relatively freely, so it can be taken as a standard avoided, some businesses still need to keep transactions consistent. Internet giants have not massively adopted XA based distributed trans‐ actions since they are not able to ensure its performance in Transaction The earliest distributed transaction model of XA standard is X/Open Distributed Transaction Processing (DTP) model brought up by X/Open, XA for short. Distributed transaction based on XA standard has

the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single ma‐ chine and can be extended relatively freely, so it can be taken as a standard avoided, some businesses still need to keep transactions consistent. Internet giants have not massively adopted XA based distributed trans‐ actions since they are not able to ensure its performance in Transaction The earliest distributed transaction model of XA standard is X/Open Distributed Transaction Processing (DTP) model brought up by X/Open, XA for short. Distributed transaction based on XA standard has

the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single ma‐ chine and can be extended relatively freely, so it can be taken as a standard avoided, some businesses still need to keep transactions consistent. Internet giants have not massively adopted XA based distributed trans‐ actions since they are not able to ensure its performance in Transaction The earliest distributed transaction model of XA standard is X/Open Distributed Transaction Processing (DTP) model brought up by X/Open, XA for short. Distributed transaction based on XA standard has

the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single ma‐ chine and can be extended relatively freely, so it can be taken as a standard Co ncurrent per formance no loss severe loss slight loss Applied s cenar‐ ios Inconsistent processing by the business side short transaction & low‐ level concurrency long transaction & high concurrency effectively avoiding row locks. One primary database with multiple replica databases can further enhance processing capacity by dis‐ tributing queries evenly into multiple data replicas. Multiple primary databases

the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single ma‐ chine and can be extended relatively freely, so it can be taken as a standard Co ncurrent per formance no loss severe loss slight loss Applied s cenar‐ ios Inconsistent processing by the business side short transaction & low‐ level concurrency long transaction & high concurrency effectively avoiding row locks. One primary database with multiple replica databases can further enhance processing capacity by dis‐ tributing queries evenly into multiple data replicas. Multiple primary databases

the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single ma‐ chine and can be extended relatively freely, so it can be taken as a standard Co ncurrent per formance no loss severe loss slight loss Applied s cenar‐ ios Inconsistent processing by the business side short transaction & low‐ level concurrency long transaction & high concurrency effectively avoiding row locks. One primary database with multiple replica databases can further enhance processing capacity by dis‐ tributing queries evenly into multiple data replicas. Multiple primary databases

the following diagram. Theoretically, horizontal sharding has overcome the limitation of data processing volume in single ma‐ chine and can be extended relatively freely, so it can be taken as a standard Co ncurrent per formance no loss severe loss slight loss Applied s cenar‐ ios Inconsistent processing by the business side short transaction & low‐ level concurrency long transaction & high concurrency effectively avoiding row locks. One primary database with multiple replica databases can further enhance processing capacity by dis‐ tributing queries evenly into multiple data replicas. Multiple primary databases

availability in distributed system – Scale out for throughput and efficiency improvement – Job processing capacity is flexible and scalable with the allocation of resources • Resource Assign – Execute Elastic Schedule Elastic schedule is the most important feature in ElasticJob, which acts as a job processing system that enables the horizontal scaling of jobs by sharding, it’s also the origin of the project election, sharding and failover. They are used for master node election, sharding and failover processing respectively. The leader node is an internally used node. If you are not interested in the principle

are compatible with the SPRING framework, so it should not be a problem to choose one of them. Processing math: 29%