ANALYSIS OF THE PRINCIPLES AND METHODS OF FORMATION OF TIME-SERIES STORAGE AND ORGANIZATION OF SOFTWARE INTERFACES IN REPOSITO-RIA OF NEURAL NETWORK MODELS
In the Russian Federation, there is an acute issue of strengthening the connectedness of the territory. An important role in solving this issue is played by spatial data infrastructures introduced for processing and analyzing information about complex geosystem models. The core of systems of this class is currently being machine learning algorithms, consolidated in the repositories of neural network models and allowing to automatically identify spatio-temporal patterns and, on the basis of available data, predict the onset or outcome of natural disasters. The purpose of this research is to study in detail the principles of InfluxDB, evaluate its capabilities and consider the possibility of its use as a repository of neural network models. InfluxDB works by using caching technology to store instant results and a WAL restore and startup file that stores the basic structure of the database organization. This file is read only in one case - to restore the system. In order to make the size of this file smaller, the Snappy compression library is applied to it. The TSM columnar storage system is used for disk storage. This format is based on the construction of logarithmically structured merge trees. With this structure, files are processed as the cache is flushed, constantly expanding the TSM structure. As a result of this research, the basic principles of InfluxDB operation, the basic methods and principles of its operation are considered, and an assessment of its applicability to the intended system is given. To solve the problem of using the application API in calculations, it is proposed to use the GraphQL paradigm, which allows you to create not a simple client server connection, but a group of microservices.