Research on Data Parallel Processing Technology for Data Lake Access Performance Optimization
This paper focuses on the high-performance data access requirements for massive data in the context of data lake applications,with the goal of designing a new data storage model and a distributed storage and caching mechanism. Through the analysis of the storage structure,data access patterns,and data processing methods in data lake,this paper conducts research on optimizing data lake access performance. First,by combining the file system storage method in data lake systems,a column-based data storage structure is designed and data index optimization technique is utilized to improve data access speed. Second,in order to improve the efficiency and stability of data access,a data access optimization method based on data partitioning and caching mechanism is proposed for the two commonly used access modes in data lakes,namely batch processing and stream processing. Finally,a data processing scheme based on Spark parallel computing and Hadoop Distributed File System ( HDFS) is proposed to solve the problem of long computation time for data lake updates and incremental calculation in large-scale data scenarios. Experimental results show that the techniques for data lake access performance optimization,proposed in this paper,can effectively improve the data storage,access and processing efficiency compared with the existing methods.
data lakeaccess performancedata partitioningparallel computingindex optimization
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