An adaptive strategy for statistics collecting in distributed database

Collecting statistics is a time- and resource-consuming operation in database systems. It is even more challenging to efficiently collect statistics without affecting system performance, meanwhile keeping correctness in distributed database. Traditional strategies usually consider one dimension during collecting statistics, which is lack of adaptiveness. In this paper, we propose an adaptive strategy for statistics collecting(ASC), which well balances collecting efficiency, correctness of statistics and effect to system performance. We formally define the procedure of collecting statistics and abstract the relationships among collecting efficiency, correctness of statistics and effect to system performance, and introduce an elastic structure(ESI) storing necessary information generated during proceeding our strategy. ASC can pick appropriate time to trigger collecting action and filter unnecessary tasks, meanwhile reasonably allocating collecting tasks to appropriate executing locations with right executing models through the information stored at ESI. We implement and evaluate our strategy in a distributed database. Experiments show that our solutions generally improve the efficiency and correctness of collecting statistics, moreover, reduce the negative effect to system performance comparing with other strategies.