Abstract: In an approach for automatic vertical partitioning of fact tables in a distributed query engine a processor analyzes a sample end-user workload of queries to extract filter predicates associated with each of multiple fact tables relating to a big data store. A processor, for each fact table, and for each column in the fact table to which a filter predicate is applied and where coarsification is required, generates a candidate partitioning expression incorporating an adjustment to a coarsification function based on a data distribution of values in the column. A processor scores the candidate partitioning expressions for each fact table based on cost data relating to the sample end-user workload and selects one or more candidate partitioning expressions to optimize partitioning of each fact table with each partition data being placed in a separate directory in a distributed file system.

Abstract: A computer-implemented method, system and computer program product for optimally performing stress testing against big data management systems. A set of random test queries is generated and compiled to determine the data points of the features (e.g., table type being queried) of the set of random test queries. A distance (e.g., Mahalanobis distance) is then measured between the data points of the features and the mean of a distribution of data points corresponding to each same feature of an extracted feature set. Each random test query whose distance exceeds a threshold distance is then ranked. The ranked random test queries are then executed in order of rank. Those executed random test queries which resulted in an error (e.g., system failure) are added to a log, which is used to identify those queries to perform a stress test against the big data management system.

Abstract: A computer-implemented method, system and computer program product for optimally performing stress testing against big data management systems. A set of random test queries is generated and compiled to determine the data points of the features (e.g., table type being queried) of the set of random test queries. A distance (e.g., Mahalanobis distance) is then measured between the data points of the features and the mean of a distribution of data points corresponding to each same feature of an extracted feature set. Each random test query whose distance exceeds a threshold distance is then ranked. The ranked random test queries are then executed in order of rank. Those executed random test queries which resulted in an error (e.g., system failure) are added to a log, which is used to identify those queries to perform a stress test against the big data management system.

Abstract: A computer-implemented method, system and computer program product for optimally performing stress testing against big data management systems. A set of random test queries is generated and compiled to determine the data points of the features (e.g., table type being queried) of the set of random test queries. A distance (e.g., Mahalanobis distance) is then measured between the data points of the features and the mean of a distribution of data points corresponding to each same feature of an extracted feature set. Each random test query whose distance exceeds a threshold distance is then ranked. The ranked random test queries are then executed in order of rank. Those executed random test queries which resulted in an error (e.g., system failure) are added to a log, which is used to identify those queries to perform a stress test against the big data management system.

Abstract: In an approach for automatic vertical partitioning of fact tables in a distributed query engine a processor analyzes a sample end-user workload of queries to extract filter predicates associated with each of multiple fact tables relating to a big data store. A processor, for each fact table, and for each column in the fact table to which a filter predicate is applied and where coarsification is required, generates a candidate partitioning expression incorporating an adjustment to a coarsification function based on a data distribution of values in the column. A processor scores the candidate partitioning expressions for each fact table based on cost data relating to the sample end-user workload and selects one or more candidate partitioning expressions to optimize partitioning of each fact table with each partition data being placed in a separate directory in a distributed file system.