Point right real-time determination method, device, medium, equipment and system
A technology for determining methods and rights and interests, applied in the field of big data real-time computing, can solve problems such as low throughput and high delay, achieve low delay, high throughput performance, and reduce network transmission
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Embodiment 1
[0073] figure 1 It is a functional block diagram of the real-time determination engine of integral rights and interests in the embodiment of the present invention. Such as figure 1 As shown, it mainly includes the following modules:
[0074] Flink real-time computing platform:
[0075] Flink is called the third-generation big data processing solution, which has the advantages of low latency, high throughput, high performance, and Exactly-Once semantic stream data processing. The embodiment of the present invention is also based on these advantages of Flink, utilizes the real-time computing API of streaming computing, adopts multi-threaded model computing, and utilizes the deployment scheme of yarn to apply for resources of computing units. At the same time, the reading of the configuration file is encapsulated, and some configuration functions of the embodiment of the present invention are transplanted into the configuration file of yaml (which is a highly readable format u...
Embodiment 2
[0152] Figure 4 It is a functional block diagram of a real-time determination device for integral rights and interests in an embodiment of the present invention. Such as Figure 4 As shown, the device 200 includes:
[0153] The reading module 201 is used to read the event table and the task table in the database;
[0154] The first storage module 202 is used to store the read event table and task table in the Flink table;
[0155] The first monitoring module 203 is used to monitor the message queue in Redis, discover the latest events and tasks, and update the event table and task table in the Flink table in real time;
[0156] The second monitoring module 204 is used to monitor the topic configured by Kafka, and obtain user transaction data from Kafka according to the topic;
[0157] The second storage module 205 is used to store user transaction data in Hbase, wherein each topic corresponds to a table in Hbase;
[0158] The activity query module 206 is used to query th...
Embodiment 3
[0171] The embodiment of the present invention also provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the following steps are implemented:
[0172] Read the event table and task table in the database;
[0173] Store the read event table and task table into the Flink table;
[0174] Listen to the message queue in Redis, discover the latest events and tasks, and update the event table and task table in the Flink table in real time;
[0175] Listen to the topic configured by Kafka, and obtain user transaction data from Kafka according to the topic;
[0176] The user transaction data is stored in Hbase, wherein each topic corresponds to a table in Hbase;
[0177] Query the event filter expression from the event table according to the topic, filter the user transaction data sent from Kafka according to the event filter expression, obtain the filtered us...
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