Abstract: A system and method for extending compression-aware aggregation logic to column partitioned database sources when an SQL query involves simple or complex aggregate expressions. The logic can be applied when there are multiple fields specified in a Group By clause, when a Group By clause includes an expression involving multiple columns from a column partitioned table, or when there is no Group By clause in the query. This logic extends the benefits of push-down aggregation to complex aggregate queries to build partially aggregated rows that can be directly added into an intermediate cache. For cases where the fields within aggregate expressions are themselves compressed, the aggregation techniques leverage the compression information of the aggregate fields. This aggregation mechanism can be applicable to compression techniques including run-length encoding (RLE), value list compression (VLC) and Presence, Delta on Mean (PDM) on columnar source tables such as Column Partitioned (CP) or Parquet tables.

Abstract: Improved techniques for management of memory (or memory management) for computing systems and environments are disclosed. The improved techniques are especially well suited for computing systems that operate in highly complex and/or demanding computing environments (e.g., massively parallel database systems that may be required to process many complex database queries in parallel. Memory can be managed dynamically at run time to determine and designate one of multiple memories that are available for execution of executable components (e.g., database queries, Opcodes of a Virtual Machine). In addition, memory can be managed dynamically at run time to effectively reuse memory locations of a memory (e.g., stack memory) being used for execution of one or more executable components (e.g., Opcodes of a Virtual Machine) at run time when the memory is being actively used to execute the one or more executable components.

Abstract: An apparatus, method and computer program product for query optimization in a Relational Database Management System (RDBMS), wherein an optimizer accesses a query expression repository (QER), so that the optimizer learns from previous versions of the queries to improve current and subsequent versions of the queries. The QER stores planning and execution information for QEs from the previous versions of the queries, wherein the QEs comprise table relations, intermediate results and/or final results of operations in the previous versions of the queries. The optimizer searches the QER for QEs from the query execution plans, and uses information from the QEs stored in the QER when optimizing the current and subsequent versions of the queries. The optimizer may also reuses results from the QEs stored in the QER.