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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

Active Publication Date: 2021-08-13
北京宇信科技集团股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of this, the purpose of the embodiments of the present invention is to provide a method, device, medium, equipment and system for real-time determination of bonus points, so as to solve performance problems such as high delay and low throughput in the prior art

Method used

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  • Point right real-time determination method, device, medium, equipment and system

Examples

Experimental program
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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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Abstract

The invention provides a point right real-time determination method, device, medium, equipment and system, and the method comprises the steps: reading an event table and a task table in a database, and storing the event table and the task table in an Flink table; monitoring the Redis message queue, discovering the newest event and task, and updating the event and task table in the Flink table in real time; monitoring a theme configured by the Kafka, and obtaining a Kafka message from the theme; storing the Kafka messages in the tables in the Hbases corresponding to the Kafka messages respectively; querying an event filtering expression from Redis according to the theme, filtering data according to a filtering condition, and querying an associated activity; querying all tasks under the activity, and copying the tasks into a plurality of task data streams; performing corresponding task standard-reaching rule calculation on the generated task data streams; and performing task group standard rule calculation, determining that the activity reaches the standard when a plurality of tasks in the task group are all completed, and sending a standard result to kafka. The problem of low delay is solved by adopting an Flink new generation big data computing platform.

Description

technical field [0001] The present invention relates to the field of big data real-time calculation, and in particular to a method, device, medium, equipment and system for real-time determination of integral benefits. Background technique [0002] The point rights system has now become the main supporting subsystem of the e-commerce platform and marketing system. Financial institutions can carry out secondary marketing to existing users by publishing an activity. After the user completes the activity task, the system will generate a notification of points and rights collection, which can effectively bond users. Due to the large scale of the current customer base and the increase in the intensity of event gifts, a large number of users often rush to buy online, which puts forward high concurrency, low latency and high throughput requirements for the system. [0003] However, the prior art mainly adopts the spark big data computing platform and redis for development. In the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06F16/245G06F16/23
CPCG06Q30/0222G06Q30/0226G06F16/23G06F16/245
Inventor 赵鑫邢鹏
Owner 北京宇信科技集团股份有限公司