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Data high-concurrency processing method of healthy social platform Feed stream system

A technology of social platform and processing method, which is applied in the field of high concurrency processing of data, can solve problems such as inability to dynamically transmit data to subscribers, high concurrency performance bottlenecks, connection timeouts, etc., to improve user experience, reduce memory requirements, and reduce difficulty Effect

Inactive Publication Date: 2021-05-28
深圳市蟠桃树科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The feed push system requires two functions, one is to publish feeds, and the other is to read feed streams; with the access of massive devices, the high concurrency of data collection will cause performance bottlenecks, resulting in data backlogs, connection timeouts and other problems; resulting in The system cannot disseminate data to subscribers dynamically and in real time, and it is not easy to push the content of SNS-based social networks; in view of the problems exposed in the current high-concurrency data processing method, it is necessary to structure the high-concurrency data processing method Improvements and optimizations on

Method used

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  • Data high-concurrency processing method of healthy social platform Feed stream system
  • Data high-concurrency processing method of healthy social platform Feed stream system
  • Data high-concurrency processing method of healthy social platform Feed stream system

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Embodiment

[0039] see Figure 1-6 , the present invention provides the following technical solutions: the tweet is stored in the database, and the tweetmeta is stored in the timeline, comprising the following steps:

[0040] S1. When a user publishes a tweet, the tweetmeta is written into the timelinelist through fanout according to the social graph; only the metadata is saved, and Redis can well support a large amount of metadata push;

[0041] S2. When the user checks his timeline, he directly fetches tweetmeta from his timeline, and then obtains the corresponding tweet data from the DB. The test environment in this implementation is a single 4-core 8G Gigabit network card Centos7.5 server. Deploy redis4.2, mysql 5.7, feed stream system in docker. The pressure test client uses apachejmeter5.3, 100 thread group pressure test for 10 minutes.

[0042] Specifically, when publishing a Feed message;

[0043] 1). Feed messages first enter a queue service. Extract metadata (TweetMeta) from...

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Abstract

The invention belongs to the technical field of a data high-concurrency processing method for saving tweet into a database so that tweetmeta is saved into timeline. The method comprises the following steps of S1, when a user issues the tweet, writing the tweet meta into a timeline list by fanout according to a local graph; only saving metadata and supporting pushing a large amount of metadata by Redis; S2, when the user checks the own timeline, enabling the user to directly take the tweetmeta from the own timeline, and then obtain the corresponding tweet data from the DB; by upgrading an SNS social system, design and development of a ten-million-level data Feed stream service system can be realized, and better system performance is provided; a processing method optimizes and enhances a social recommendation algorithm, optimizes accurate data matching and solves the problem of data repetition in the social relationship; and the user is effectively promoted to increase the communication frequency and activeness, and the user experience is improved.

Description

technical field [0001] The invention belongs to the technical field of high-concurrency data processing methods, and in particular relates to a high-concurrency data processing method for a health social platform Feed flow system. Background technique [0002] The data collection of big data is based on the determination of the user's goals, and aims at the collection of all structured, semi-structured and unstructured data within the scope, and processes these data after collection, and analyzes and excavates valuable information from them. Information. In the process of collecting big data, it faces two main challenges. One is the diversity of communication protocols and data protocols of heterogeneous IoT devices; the two most critical cores of the feed stream system are storage, One is push. The content that needs to be stored in the feed stream system is divided into two parts, one is social relations (such as friends, community members, watch list), and the other is ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9536G06F16/957
CPCG06F16/9536G06F16/9574
Inventor 吕小健况红波
Owner 深圳市蟠桃树科技有限公司
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