Scenarios of financial industry attributes that require high data consistency, reliability, and disaster recovery.
As we all know, the financial industry has high requirements for data consistency and reliability, high system availability, scalability, and disaster recovery. The traditional solution is that two computer rooms in the same city provide services, and one computer room in a remote location provides data disaster recovery capabilities but does not provide services. This solution has the following disadvantages: low resource utilization, high maintenance costs, and RTO (Recovery Time Objective) and RPO (Recovery Point Objective) cannot actually meet the values expected by the enterprise. TiDB uses multiple copies+multi-raft protocols to schedule data to different computer rooms, racks, and machines. When some machines fail, the system can automatically switch to ensure the system's RTO <= 30s and RPO = 0.

Massive data and high concurrency OLTP scenarios that require high storage capacity, scalability, and concurrency.
With the rapid development of business, data is growing explosively. Traditional stand-alone databases cannot meet the database capacity requirements due to explosive data growth. Possible solutions are to use middleware products or NewSQL databases that split tables and replace them with high-end storage devices. Among them, the most cost-effective is the NewSQL database, such as TiDB. TiDB uses a separate architecture for computing and storage, and can expand and reduce the capacity of computing and storage separately. The maximum computing support is 512 nodes, each node can support up to 1000 concurrency, and the maximum cluster capacity supports PB level.

Real-time HTAP scenario.
With the rapid development of 5G, the Internet of Things, and artificial intelligence, enterprises will produce more and more data, and its scale may reach the level of hundreds of TB or even PB. The traditional solution is to process online transactions through OLTP databases, and synchronize data to OLAP databases for data analysis through ETL tools. This processing scheme has many problems such as high storage costs and poor real-time performance. TiDB introduced the column storage engine TiFlash in version 4.0 to combine the row storage engine TiKV to build a real HTAP database. With a small increase in storage costs, online transaction processing and real-time data analysis can be performed in the same system, greatly saving enterprise costs.

Scenes of data aggregation and secondary processing.
Currently, the business data of the vast majority of enterprises is scattered in different systems, without a unified summary. As the business develops, the decision makers of the enterprise need to understand the business status of the entire company in order to make decisions in a timely manner, so it is necessary to aggregate the data scattered across various systems into the same system and perform secondary processing to generate T+0 or T+1 reports. The traditional common solution is to use ETL+ Hadoop to complete it, but the Hadoop system is too complicated, and operation, maintenance, and storage costs are too high to meet the needs of users. Compared with Hadoop, TiDB is much simpler. The business synchronizes data to TiDB through ETL tools or TiDB's synchronization tools, and reports can be generated directly through SQL in TiDB.