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Scenarios

Last Updated: Aug 19, 2021

This topic describes the typical scenarios of OceanBase Database.

Transparent partitioning for payments

Payment is the core business of Ant Group. It used the sharding solution in the beginning. Sharding gave this core business not only horizontal scalability but also the ability of gray release, which could significantly reduce system risks. However, after the rapid business expansion of Alipay, the capacity bottleneck of single nodes surfaced. In the sharding architecture, this bottleneck could be resolved only by breaking down data into M × N parts, which is time-consuming and has high technical risks.

This issue can be resolved by the partitioning feature of OceanBase Database, which brings horizontal scalability. OceanBase Database breaks down each of the N pieces of data into M data partitions. The partitioned tables can bypass the capacity limit of a single node. This way, OceanBase Database implements partitioning in the data layer without the need to modify the business. This saves costs and lowers technical risks. At the same time, the partitioning feature of OceanBase Database can free you from considering the detailed data distribution. OceanBase Database also supports distributed transactions and JOIN operations across partitions, which allows transparent access to distributed databases.

Multidimensional query in membership systems

Many enterprises have membership systems that store important user information. User information is often queried from multiple dimensions. For example, you may want to query the detailed information about a user by using the ID or telephone number of the user, or group users by age. This type of multidimensional queries, especially queries on non-partition key columns, are common in scenarios that involve massive amounts of distributed data. To handle these queries, you can create a shadow table that contains all the columns except for the partition key column. This approach uses no special database features. It is at best a workaround in the application layer. OceanBase Database can use the partitioning feature to break down data and distribute the data across multiple nodes in a cluster to handle a part of the queries. Then, OceanBase Database uses the global indexing feature with strong consistency for queries and analysis from other dimensions. This way, data sharding and horizontal scalability are achieved on the database level. This process is transparent to you.

Batch processing system

In many industries, batch processing systems need to process a large amount of data, such as the computation based on multiple joined large tables and data update. In batch processing, a large amount of data is processed or updated at a time. Multiple large tables may need to be joined and complex queries may be required. Traditional centralized databases use single nodes, which are incapable of handling batch processing. The costs for vertical scaling are too high to be accepted. The SQL engine of OceanBase Database has been developed for more than 10 years. It can provide excellent SQL execution and distributed computing capabilities to support complex hybrid transactional and analytical processing (HTAP) applications. As a cloud-native distributed database service, OceanBase Database has no single-node bottlenecks and can help you save scaling costs.