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

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 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 use of eventual consis‐ tency to instead of consistency when improve concurrency of transaction processing. TCC and Saga are two regular implementations. They use reverse operation implemented by developers

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 ShardingSphere document, v5.0.0-beta To avoid the deadlock, ShardingSphere will go through synchronous processing when acquiring database connection. When creating execution units, it acquires all the database 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

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 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 use of eventual consis‐ tency to instead of consistency when improve concurrency of transaction processing. TCC and Sage are two regular implementations. They use reverse operation implemented by developers

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 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 use of eventual consis‐ tency to instead of consistency when improve concurrency of transaction processing. TCC and Saga are two regular implementations. They use reverse operation implemented by developers

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

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