This paper mainly studies the design of an efficient social network log analysis program based on the distributeddatabase, MongoDB. The so-called social network log analysis is to gather and store the log information generatedwhen users access the social network pages, and then transform, clean and excavate. This article compares MongoDB databasewith traditional relational database, analyzes its advantages and application scenarios. Its anti-paradigm design dueto the nest avoids the association, making queries and storage of the large data efficiently by: storing social network logsin the MongoDB; directly analyzing the logs with its built-in MapReduce programming model, and saving the results ofthe analysis as files for business people to use. Our study aims to discover the hidden users' access rules and patterns inthe log data by effective data mining of the social network log data, thereby providing helpful information for optimizingwebsite structure and business model.
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