and Ounis, I., Query efficiency prediction for dynamic pruning. Dissertation, Department of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, NL, 2010. Hauff, C., Predicting the effectiveness of queries and retrieval systems. J., A bayesian approach to online performance modeling for database appliances using gaussian models. and Aboulnaga, A., Q-Cop: Avoiding bad query mixes to minimize client timeouts under heavy loads. ![]() ACM SIGMETRICS Performance Evaluation Review. G., A model for predicting the response time of an on-line system for electronic fund transfer. V., Information retrieval: Implementing and evaluating search engines. and Tong, Z., Elasticsearch: The definitive guide, Sebastopol, CA, USA: O’Reilly, 2015. Regression models are then built and compared to find the most accurate method for predicting query time. The number of terms in a query and the Total Term Frequency (TTF) from Elasticsearch’s API are found to significantly predict execution time. This research investigates the ability of different pre-retrieval statistics, available through Elasticsearch, to accurately predict the execution time of queries on a typical Elasticsearch cluster. Elasticsearch uses multiple Lucene instances on multiple hosts as an underlying search engine implementation, but this abstraction makes it difficult to predict execution with previously known predictors such as the number of postings. This information can be used to enforce rate limiting or distribute requests equitably among multiple clusters. In a shared Elasticsearch environment it can be useful to know how long a particular query will take to execute. ![]() ![]() Elasticsearch, Elasticsearch Query, Query Cost Model, Document Frequency, Total Term Frequency, Term Vectors.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |