Power-Pipelining for Enhanced Query Performance

As random access memory gets cheaper, it becomes increasingly affordable to build computers with large main memories. In this paper, we consider processing queries within the context of a main memory database system and try to enhance the query execution engine of such a system. An execution plan is usually represented as an operator tree. Traditional query execution engines evaluate a query by recursively iterating each operator and returning exactly one tuple result for each iteration. This generates a large number of function calls. In a main-memory database system, the cost of a function call is relatively high. We propose a new evaluation method called power-pipelining. Each operator processes and passes many tuples instead of one tuple each time. We keep the number of matches generated in a join in a local counter array. We reconstitute the run-length encoding of the join result (broken down by input tables) at the end of the join processing or when necessary. We describe an overflow handling mechanism that allows us to always evaluate a query without using too much space. Besides the benefit of reducing the number of function calls, power-pipelining compresses the join result automatically and thus can reduce the transmission cost. However, power-pipelining may not always outperform the traditional pipelining method when the join selectivity is low. To get the benefit of both techniques, we propose to use them together in an execution engine and let the optimizer choose the preferred execution. We discuss how to incorporate this in a cost-based query optimizer. We implemented power-pipelining and the reconstitution algorithm in the Columbia main memory database system. We present a performance study of power-pipelining and the traditional pipelining method. We show that the improvement of using power-pipelining can be very significant. As a result, we believe that power-pipelining is a useful and feasible technique and should be incorporated into main memory database query execution engines