. . . . . . . . . . . . . . . . . . . . . . . 444 xii 1 Overview Stargazers Over Time Contributors Over Time Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard Apache ShardingSphere document, v5.1.1 1.3 Roadmap 1.3. Roadmap 6 2 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. 2.1 ShardingSphere-JDBC Data is increasing explosively, the data storage and computing method are facing innovation all the time. Transaction, big data, association analysis, Internet of things and other scenarios subdivided quickly

Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 19 Mass data real‐time analysis in OLAP scenarios . . . . . . . . . . . . . . . . . . . 20 3.1.5 Related References . . provides access to high‐availability computing services based on stateless ser‐ vices. At the same time, it can sense and use the underlying database’s HA solution to achieve its overall high availability suggest using cluster mode in production environment. 1.3. Deployment 10 2 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. Example Codes:

. . . . . . . . . . . . . . . . . . . . . . . 489 xii 1 Overview Stargazers Over Time Contributors Over Time Apache ShardingSphere is positioned as a Database Plus, and aims at building a standard Apache ShardingSphere document, v5.1.2 1.3 Roadmap 1.3. Roadmap 6 2 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. Example Codes: Data is increasing explosively, the data storage and computing method are facing innovation all the time. Transaction, big data, association analysis, Internet of things and other scenarios subdivided quickly

Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 18 Mass data real‐time analysis in OLAP scenarios . . . . . . . . . . . . . . . . . . . 19 3.1.5 Related References . . to check out the mailing list and discuss via mail. 1.5. Roadmap 9 2 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. Example Codes: will increase the disk IO access number, and thereby, weaken the performance of query. In the same time, high concurrency requests also make the centralized database to be the greatest limitation of the

Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 22 Mass data real‐time analysis in OLAP scenarios . . . . . . . . . . . . . . . . . . . 23 8.1.5 Related References . . are welcome to check out the mailing list and discuss via mail. 13 7 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. Example Codes: will increase the disk IO access number, and thereby, weaken the performance of query. In the same time, high concurrency requests also make the centralized database to be the greatest limitation of the

Mass data high concurrency in OLTP scenarios . . . . . . . . . . . . . . . . . . . 22 Mass data real‐time analysis in OLAP scenarios . . . . . . . . . . . . . . . . . . . 23 8.1.5 Related References . . are welcome to check out the mailing list and discuss via mail. 13 7 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. Example Codes: will increase the disk IO access number, and thereby, weaken the performance of query. In the same time, high concurrency requests also make the centralized database to be the greatest limitation of the

. . . . . . . . . . . . . . . . . . . . . . . . 391 x 1 Overview Stargazers Over Time Contributors Over Time Apache ShardingSphere is positioned as a Database Plus, and aims at building a new criterion Apache ShardingSphere document, v5.0.0 1.3 Roadmap 1.3. Roadmap 6 2 Quick Start In shortest time, this chapter provides users with a simplest quick start with Apache ShardingSphere. 2.1 ShardingSphere-JDBC Data is increasing explosively, the data storage and computing method are facing innovation all the time. Transaction, big data, association analysis, Internet of things and other scenarios subdivided quickly

sfunc = avg_transfn, finalfunc = avg_finalfn) CREATE TABLE agg_data_2k AS SELECT g FROM generate_series(0, 1999) g; CREATE SCHEMA alt_nsp1; ALTER AGGREGATE alt_agg3(int) OWNER TO regress_alter_generic_user2; sfunc = avg_transfn, finalfunc = avg_finalfn) CREATE TABLE agg_data_2k AS SELECT g FROM generate_series(0, 1999) g; CREATE SCHEMA alt_nsp1; ALTER AGGREGATE alt_agg3(int) OWNER TO regress_alter_generic_user2; 运算表达式中包含分片键 当分片键处于运算表达式中时，无法通过 SQL 字面提取用于分片的值，将导致全路由。 例如，假设 create_time 为分片键： SELECT * FROM t_order WHERE to_date(create_time, 'yyyy-mm-dd') = '2019-01-01'; 实验性支持 实验性支持特指使用 Federation 执行引擎提供支持。该

sfunc = avg_transfn, finalfunc = avg_finalfn) CREATE TABLE agg_data_2k AS SELECT g FROM generate_series(0, 1999) g; CREATE SCHEMA alt_nsp1; ALTER AGGREGATE alt_agg3(int) OWNER TO regress_alter_generic_user2; sfunc = avg_transfn, finalfunc = avg_finalfn) CREATE TABLE agg_data_2k AS SELECT g FROM generate_series(0, 1999) g; CREATE SCHEMA alt_nsp1; ALTER AGGREGATE alt_agg3(int) OWNER TO regress_alter_generic_user2; 运算表达式中包含分片键 当分片键处于运算表达式中时，无法通过 SQL 字面提取用于分片的值，将导致全路由。 例如，假设 create_time 为分片键： SELECT * FROM t_order WHERE to_date(create_time, 'yyyy-mm-dd') = '2019-01-01'; 实验性支持 实验性支持特指使用 Federation 执行引擎提供支持。该

sfunc = avg_transfn, finalfunc = avg_finalfn) CREATE TABLE agg_data_2k AS SELECT g FROM generate_series(0, 1999) g; CREATE SCHEMA alt_nsp1; ALTER AGGREGATE alt_agg3(int) OWNER TO regress_alter_generic_user2; sfunc = avg_transfn, finalfunc = avg_finalfn) CREATE TABLE agg_data_2k AS SELECT g FROM generate_series(0, 1999) g; CREATE SCHEMA alt_nsp1; ALTER AGGREGATE alt_agg3(int) OWNER TO regress_alter_generic_user2; 运算表达式中包含分片键 当分片键处于运算表达式中时，无法通过 SQL 字面提取用于分片的值，将导致全路由。 例如，假设 create_time 为分片键： SELECT * FROM t_order WHERE to_date(create_time, 'yyyy-mm-dd') = '2019-01-01'; 实验性支持 实验性支持特指使用 Federation 执行引擎提供支持。该