Progressive optimization in a shared-nothing parallel database

Commercial enterprise data warehouses are typically implemented on parallel databases due to the inherent scalability and performance limitation of a serial architecture. Queries used in such large data warehouses can contain complex predicates as well as multiple joins, and the resulting query execution plans generated by the optimizer may be suboptimal due to mis-estimates of row cardinalities. Progressive optimization (POP) is an approach to detect cardinality estimation errors by monitoring actual cardinalities at runtime and to recover by triggering re-optimization with the actual cardinalities measured. However, the original serial POP solution is based on a serial processing architecture, and the core ideas cannot be readily applied to a parallel shared-nothing environment. Extending the serial POP to a parallel environment is a challenging problem since we need to determine when and how we can trigger re-optimization based on cardinalities collected from multiple independent n...

• Type: Conference
• Year: 2007
• Where: SIGMOD
• Authors: Wook-Shin Han, Jack Ng, Volker Markl, Holger Kache, Mokhtar Kandil
• Keywords: Commercial Parallel Dbms, Database, Parallel Checkpoint Operators, Parallel Checkpoint Processing, SIGMOD 2007