Design and Implementation of Elasticsearch for Media Data

With the development of science and technology, a lot of information and data are generated rapidly in the process of using computers, and the amount of data also presents an explosive growth. Traditional relational databases such as mysql are gradually unable to meet the needs of users for quick retrieval. But Elasticsearch makes up for this slow retrieval by providing users with a quick way to do it. Therefore, in this paper, we use the Flask framework in python to write a system for rapid retrieval and visualization of media data. Firstly, a nonrelational database like MongoDB was used to store the raw media data we crawled from the network. Then import the data into the cluster set up by Elasticsearch, create a map of the data, add a Chinese word segmentation parser, and set up an inverted index, so that the data can be used to accurately retrieve information in the future. At the same time, Kibana can be used to visually display and present the data.