Increment calculation and optimization of data lake for structured data
This study aims to optimize the incremental calculation in the data lake for structured data,and to improve the com-putational efficiency by deeply exploring the SHUFFLE mechanism and the data tilt problem.In this study,the staff analyzed the SHUFFLE mechanism and its data tilt phenomenon in Spark in detail,and pointed out that the data tilt can seriously affect the task execution time and resource utilization.On this basis,the problem of small files is analyzed,and the negative impact of small files on data processing performance is discussed.To meet these challenges,optimization solutions based on Spark parallel computing and HDFS distributed storage.These algorithms designed to reduce data tilt and optimize data storage and processing process.The results shows that these optimization schemes significantly improve the efficiency of incremental computing in data lakes,reduce the consumption of computing resources,and enhance the overall performance of the system.
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