Abstract: Methods for cardinality estimation feedback loops in query processing are performed by systems and devices. A query host executes queries against data sources via an engine based on estimated cardinalities, and query monitors generate event signals during and at completion of execution. Event signals include indicia of actual data cardinality, runtime statistics, and query parameters in query plans, and are routed to analyzers of a feedback optimizer where event signal information is analyzed. The feedback optimizer utilizes analysis results to generate change recommendations as feedback for later executions of the queries, or similar queries, performed by a query optimizer of the query host. The query host stores change recommendations, and subsequent queries are monitored for the same or similar queries to which change recommendations are applied to query plans for execution and observance by the query monitors. Change recommendations are optionally viewed and selected via a user interface.

Abstract: Methods for cardinality estimation feedback loops in query processing are performed by systems and devices. A query host executes queries against data sources via an engine based on estimated cardinalities, and query monitors generate event signals during and at completion of execution. Event signals include indicia of actual data cardinality, runtime statistics, and query parameters in query plans, and are routed to analyzers of a feedback optimizer where event signal information is analyzed. The feedback optimizer utilizes analysis results to generate change recommendations as feedback for later executions of the queries, or similar queries, performed by a query optimizer of the query host. The query host stores change recommendations, and subsequent queries are monitored for the same or similar queries to which change recommendations are applied to query plans for execution and observance by the query monitors. Change recommendations are optionally viewed and selected via a user interface.

Abstract: Methods for cardinality estimation feedback loops in query processing are performed by systems and devices. A query host executes queries against data sources via an engine based on estimated cardinalities, and query monitors generate event signals during and at completion of execution. Event signals include indicia of actual data cardinality, runtime statistics, and query parameters in query plans, and are routed to analyzers of a feedback optimizer where event signal information is analyzed. The feedback optimizer utilizes analysis results to generate change recommendations as feedback for later executions of the queries, or similar queries, performed by a query optimizer of the query host. The query host stores change recommendations, and subsequent queries are monitored for the same or similar queries to which change recommendations are applied to query plans for execution and observance by the query monitors. Change recommendations are optionally viewed and selected via a user interface.

Abstract: The degree of parallel processing used to build a database index can be dynamically adjusted based on actual memory usage of individual parallel processing units. Memory can be reserved to prevent an out-of-memory condition. A predetermined number of initial parallel processing units can be activated. The actual usage of resources by the initial activated parallel processing unit(s) can be measured to establish an initial baseline for resource consumption per parallel processing unit. The baseline for resource consumption per parallel processing unit can be used to determine how many additional parallel processing units are activated. The actual resource usage of each parallel processing unit can be measured and used to refine the baseline memory usage. The refined average memory usage can be used to determine how many additional parallel processing units are activated.

Abstract: A database can have multiple requests applied at one time. Each of these requests requires a specific amount of server resources. There can be a differentiation of user-submitted workloads between each other. These workloads are a set of queries submitted by different users. Each query can have specific resource limits. In addition, each set can have specific resource limits.

Abstract: A database can have multiple requests applied at one time. Each of these requests requires a specific amount of server resources. There can be a differentiation of user-submitted workloads between each other. These workloads are a set of queries submitted by different users. Each query can have specific resource limits. In addition, each set can have specific resource limits.