The transaction engine is the core system of Tencent billing. The data of each transaction order needs to be monitored by dozens of downstream business systems, including item price approval, delivery, reward point, and stream computing analysis. Such systems use different message processing logic, making it impossible for a single system to adapt to all associated business. In this case, TDMQ for RocketMQ can implement efficient async communication and application decoupling to ensure the business continuity of the primary site.
Companies hold promotional campaigns such as new product launch and festival red packet grabbing from time to time, which often cause temporary traffic spikes and pose huge challenges to each backend application system. In this case, TDMQ for RocketMQ can act as a buffer to centrally collect the suddenly increased requests in the upstream, allowing downstream businesses to consume the request messages based on their actual processing capacities.
Sequential messages are used in some business scenarios, such as order creation, payment, delivery, and refund of in-app/game items, which are all strictly executed in sequence. Similar to the First In, First Out (FIFO) principle, TDMQ for RocketMQ offers a sequential message feature dedicated to such scenarios to ensure message FIFO.
A billing system often has a long transaction linkage with a significant chance of error or timeout. TDMQ for RocketMQ's automated repush and abundant message retention features can be used to provide transaction compensation, and the eventual consistency of payment tips notifications and transaction pushes can also be achieved through TDMQ for RocketMQ.
During sales and promotions, there are a wide variety of products with frequent price changes. When users query item prices multiple times, the cache server's network interface may be fully loaded, which makes page opening slower. After the broadcast consumption mode of TDMQ for RocketMQ is adopted, a message will be consumed by all nodes once, which is equivalent to syncing the price information to each server as needed in place of the cache.
Data creates value in the "flow". Most traditional data analysis are based on batch computing models, which means they cannot analyze data in real time. In contrast, TDMQ for RocketMQ can easily implement real-time analysis of business data when combined with a stream computing engine.