Flow data processing method in multi-user environment

Abstract

The invention relates to a flow data processing method in a multi-user environment, and relates to computer data processing technology. The method comprises the following steps of: 1) reading data content from a data stream, and storing the read data content in the memory area of a memory ring according to the read sequence; 2) adding the user request into the request queue according to the priority of the user request if a user data request is received; 3) processing the user request according to the queue sequence, retrieving the data content in the request in the memory ring, and proceedingto step 4) if the requested data is retrieved; proceeding to step 5) if the requested data is not retrieved; 4) returning the address pointer of the requested data in the memory ring to the user; 5)re-adding the user request in the request queue after waiting for a predetermined time, and returning prompt information to the user if the data corresponding to the request is still not retrieved after a predetermined number of times or a preset time. According to the invention, the consumption of system resource is reduced.

Description

technical field [0001] The present invention relates to computer data processing technology. Background technique [0002] Streaming data, also known as streaming data, is a set of data sequences that arrive sequentially and continuously. In general, streaming data can be regarded as a dynamic data collection that grows infinitely over time; The data continuously generated by thousands of data sources is usually sent in the form of data records at the same time, and the data scale changes from time to time. At the same time, the characteristics of streaming data: [0003] 1) Real-time data transmission; [0004] 2) Data transmission sequence independence, not controlled by external factors; [0005] 3) The data scale is uncertain, and its maximum or minimum value cannot be predicted; [0006] 4) It is very difficult to trace back the data. Once processed, it cannot be retrieved again unless it is specially saved, or the cost of re-extracting the data is high. [0007] B...

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Problems solved by technology

[0005] 3) The data scale is uncertain, and its maximum or minimum value cannot be predicted;

[0006] 4) Data backtracking is difficult. Once processed, it cannot be retrieved again unless it is specially saved, or the cost of re-extracting data is high

This can effectively protect the integrity and correctness of metadata, but it will inevitably cause excessive consumption of system resources and reduce system efficiency. As the amount of data continues to increase, this problem becomes more obvious and serious

At the same time, because the mechanism is dynamically applied from the memory, in the system with frequent data volume operations, it also brings problems such as system real-time and memory fragmentation.

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