Big data platform architecture system based on container cluster

Abstract

The invention discloses a big data platform architecture system based on a container cluster. The big data platform architecture system comprises a physical resource layer, an operating system layer,a meso resource management layer, a framework layer and an interface layer which are connected in sequence from bottom to top, all the layers carry out cooperation and communication management througha distributed coordination manager Zookeeper; kubernetes are used as a framework to be integrated into a meso resource management layer, and bottom physical resources are obtained through the meso resource management layer; wherein the container cluster is used for running the Docker application, the micro-service application is packaged in the Docker application, and the external system calls the micro-service application in the architecture system through the interface layer. According to the big data platform architecture system based on the container cluster, rapid deployment can be achieved, various applications can be stopped at a second level, and cluster deployment, load balancing, disaster recovery and elastic expansion and contraction of various services are supported.

Description

technical field [0001] The invention relates to the technical field of cloud computing storage, in particular to a container cluster-based big data platform architecture system. Background technique [0002] At present, 59% of the data in the era of big data is invalid data, 70% of the data is too complex, 85% of the enterprise data architecture cannot meet the needs of increasing data volume and complexity, and 98% of the enterprises cannot provide timely and accurate information for the business. Information. Today, big data challenges and opportunities coexist, and the construction of big data service platforms is undergoing an all-round integrated development process from simple to complex, from single to multiple, and from part to whole. However, cloud data centers that support big data services often have the following challenges: 1. It is difficult to start large-scale computing resources in a short time to meet the needs of a large number of distributed computing no...

Images