The Spark computing framework has been adopted as the framework for big data analysis by an increasing number of enterprises. However, the complexity of the system is increased due to the characteristic that it is typically deployed in distributed and cloud environments. Therefore, it is always considered to be difficult to monitor the performance of the Spark framework and finding jobs that lead to performance degradation. In order to solve this problem, a real-time monitoring and analysis method for Spark performance in distributed container environment was proposed and compiled. Firstly, the resource consumption information of jobs at runtime was acquired and integrated through the implantation of code in Spark and monitoring of Application Program Interface (API) files in Docker containers. Then, the Gaussian Mixture Model (GMM) was trained based on job history information of Spark. Finally, the trained model was used to classify the resource consumption information of Spark jobs at runtime and find jobs that led to performance degradation. The experimental results show that, the proposed method can detect 90.2% of the abnormal jobs and it only introduces 4.7% degradation to the performance of Spark jobs. The proposde method can lighten the burden of error checking and help users find the abnormal jobs of Spark in a shorter time.