When the Log-Structured Merge-tree (LSM-tree) in the time series database is under high write load or resource constraints, file compaction not in time will cause a large accumulation of LSM C ₀ layer data, resulting in an increase in the latency of ad hoc queries of recently written data. To address this problem, a two-stage LSM compaction framework was proposed that realizes low-latency query of newly written time series data on the basis of maintaining efficient query for large blocks of data. Firstly, the file compaction process was divided into two stages:quickly merging of a small number of out-of-order files, merging of a large number of small files, then multiple file compaction strategies were provided in each stage, finally the two-stage compaction resource allocation was performed according to the query load of the system. By implementing the test of the traditional LSM compaction strategy and the two-stage LSM compaction framework on the time series database Apache IoTDB, the results showed that compared with the traditional LSM, the two-stage file compaction module was able to greatly reduce the number of ad hoc query reads while improving the flexibility of the strategy, and made the historical data analysis and query performance improved by about 20%. Experimental results show that the two-stage LSM compaction framework can increase the ad hoc query efficiency of recently written data, and can improve the performance of historical data analysis and query as well as the flexibility of compaction strategy.

For the complex multimodal optimization problems, the traditional Teaching-Learning-Based Optimization (TLBO) algorithm is easy to fall into local search and has low optima efficiency. In order to solve the problem, an improved TLBO algorithm based on K-means was proposed in this paper. It used the K-means to decide the population into small populations for reducing the population size and correspondingly improved the "teaching" and "learning" stages to improve the speed of global convergence. At the same time, the proposed algorithm added "mutation" operation to avoid the local optimum. In the experiments, seven unimodal and two multimodal optimization problems were optimized by the algorithm proposed in this paper. The optimization results were compared grenade explosion method and traditional TLBO algorithm. The experimental results show that the improved algorithm can quickly and efficiently find the globally optimal solution in both unimodal and multimodal functions and the improved algorithm is better than the traditional TLBO algorithm in the ability of searching the globally optimal solutions.