Database engine applicable to big data of power equipment

Abstract

The invention relates to a database engine, in particular to a database engine applicable to big data of power equipment. The database engine particularly comprises a coordinator node and worker nodes. The coordinator node comprises an analyzer, a planner and a scheduler; the analyzer, the planner and the scheduler are sequentially connected with one another. The database engine has the advantagesthat data views of points and time marks are measured in a unified manner for the first time, accordingly, passive situation of numerous data tables due to metadata structures and relations and the like can be prevented, and high-concurrency and low-delay storage requirements of massive structured data can be met to a great extent; index structures oriented to time serial data of measurement points of power monitoring systems are creatively proposed, accordingly, the shortcoming of limitation that relational databases only can be applied to single-row indexes can be overcome, and the data retrieval and read-write efficiency can be greatly enhanced.

Description

technical field [0001] The invention relates to a database engine, in particular to a database engine suitable for big data of electric power equipment. Background technique [0002] Big data (big data) can be generally understood as a collection of data that cannot be captured, managed and processed with traditional database software tools within a certain period of time. In view of the huge potential impact of big data, many countries regard big data as a strategic resource, and promote big data research to a national strategy, involving finance, telecommunications, network, retail, manufacturing, medical and technological fields. The application of big data in the field of energy and power is in its infancy. In 2013, the Chinese Society of Electrical Engineering released the "White Paper on the Development of China's Electric Power Big Data". The white paper first proposed the definition of electric power big data, and pointed out that reshaping the core value of electri...

AI Technical Summary

Problems solved by technology

The data warehouse infrastructure (Hive) still mainly relies on the big data parallel computing framework (MapReduce) to run, so as it ages, its speed can no longer meet the growing data requirements

At the same time, with the increasing scale of the power grid, the data scale in the state monitoring and evaluation center of the power transmission and transformation equipment of the provincial power grid has also expanded rapidly. It may cost A few minutes to a few hours, which is completely incompatible with the development needs of the smart grid

[0004] Although HIVE is relatively mature and stable compared to other big data components, because it uses MapReduce as the background computing engine, the real-time response capability of HIVE is poor, so it is not suitable for real-time interactive analysis scenarios, and cannot meet the real-time data analysis requirements of power big data

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