Abstract: Techniques are described herein for analyzing and tuning database workloads to optimize application performance. In some embodiments, a workload analyzer identifies a captured workload that includes a set of database queries executed within a particular timeframe. The workload analyzer compares the workload within one or more other workloads executed within a previous timeframe to determine differences between the different workloads. For example, the workload analyzer may identify changes in the distributions of queries, including how many queries are unchanged, missing, and/or new. The workload analyzer may further detect changes in the performance of individual queries. The workload analyzer may determine the overall performance impact of such changes on the total workload. Based on the analysis, the workload analyzer may generate reports, alerts, tuning advice, and/or recommendations to boost performance.

Abstract: Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. In addition, a method and system is provided for implementing collection-wise processing within a log analytics system.

Abstract: Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. In addition, a method and system is provided for implementing collection-wise processing within a log analytics system.

Abstract: An approach for automatically determining database tables that are best suited for index tuning involves analyzing query performance data that specifies an average amount of time required to execute a plurality of queries on the database tables. One or more particular queries are selected from the plurality of queries based upon the query performance data. Data tables required to process the one or more particular queries are selected for index tuning.

Abstract: A method for non-recursive data driven parameter evaluation within an information management system (e.g., a relational database management system, RDBMS). Parameters are attributes within an RDBMS system whose value can be modified to influence the manner in which a database environment responds. Depending on the character of the data within a database environment or the hardware resources allocated to the database, the parameter values have a large influence on the performance efficiency of the database. For instance, a parameter value may influence the memory size of certain data storage areas within a database environment and, if set too small, can cause an inefficiently large amount of input/output accesses to occur as data is moved in and out of disk storage. Also, many parameters are data dependent on other parameters. The novel system presents an automated system for evaluating parameters using a non-recursive evaluation process.