Dynamic plan migration is concerned with the on-the-fly transition from one continuous query plan to a semantically equivalent yet more efficient plan. Migration is important for stream monitoring systems where long-running queries may have to withstand fluctuations in stream workloads and data characteristics. Existing migration methods generally adopt a pause-drain-resume strategy that pauses the processing of new data, purges all old data in the existing plan, until finally the new plan can be plugged into the system. However, these existing strategies do not address the problem of migrating query plans that contain stateful operators, such as joins. We now develop solutions for online plan migration for continuous stateful plans. In particular, in this paper, we propose two alternative strategies, called the moving state strategy and the parallel track strategy, one exploiting reusability and the second employs parallelism to seamlessly migrate between continuous join plans without affecting the results of the query. We develop cost models for both migration strategies to analytically compare them. We embed these migration strategies into the CAPE [7] , a prototype system of a stream query engine, and conduct a comparative experimental study to evaluate these two strategies for window-based join plans. Our experimental results illustrate that the two strategies can vary significantly in terms of output rates and intermediate storage spaces given distinct system configurations and stream workloads.
REF investigates the migration between query plans that contain window join operators in order to achieve dynamic query optimization.
Dynamic plan migration for continuous queries over data streams
SIGMOD '04
Computer Science