Optimization And Implementation Of Vehicle Monitoring System Based On Big Data Technology

With the rapid development of Internet and "Internet +" boom, gradually to the informatization in the direction of the traditional industry, it has become an important strategic layout in traditional industries, so the Internet information data of rapid growth in the global scope, the data type is becoming more and more rich.In recent years, with the improvement of living standards, cars have become the main means of transportation, car sales increased year by year, caused the vehicle data is increasing. The current vehicle monitoring system by most car terminal to upload data to traditional relational database schema stored, traditional relational database data is stored in the quantity reach a certain scale degree after query efficiency will decrease greatly, have a great influence on the overall performance of the system, so the problem of large amount of data has become the main performance bottlenecks in the vehicle monitoring system.This article introduces several big data processing technology and use, including Spark distributed computing framework, Hbase database, Kafka message queue, Redis cache server.Make full use of the several technical advantages, optimize the current vehicle monitoring system, using Kafka as vehicle terminal and monitor data bridge platform, vehicle-mounted terminal services acquisition vehicle information uploaded to Kafka, Spark from Kafka receive upload of data parallel processing and calculation, calculation process need to read configuration information from Redis, finally the results of calculation of transient data cache to Redis, offline data in Hbase, used for monitoring platform;Online data is sent to Kafka, for the use of superior(Government) platform.By using the above optimized technology of vehicle monitoring system, the system throughput and performance has obvious improvement, and good scalability to meet the increasing data requirements, the optimization of vehicle monitoring system for big data storage and efficiency have a significant effect.