SpeCA:A Speculative Parallel Crawling Approach on Apache Spark

Abstract The World Wide Web today is growing at a phenomenal rate. The crawling approach is of vital importance to improve the efficiency of crawling the web. The existing crawling algorithms on multicore platforms are time consuming and do not support large data well. In order to improve parallelism and efficiency of crawler on distributed network environments, based on the software thread-level speculation technique, this paper raises a Speculative parallel crawler approach (SpeCA) on Apache Spark. By analyzing the process of web crawler, the SpeCA firstly hires a function to divide a crawling process into several subprocesses which can be implemented independently and then spawns a number of threads to speculatively crawl in parallel. At last, the speculative results are merged to form the final outcome. Comparing with the conventional parallel approach on multicore platform, SpeCA is very efficiency and obtains a high parallelism degree by making the best of the resources of the cluster. Experiments show that the proposed approach could achieve a significant speedup improvement with compare to the traditional approach in average. In addition, with the growing number of working nodes, the execution time decreases gradually, and the speedup scales linearly. The results indicate that the crawling efficiency can be significantly enhanced by adopting this speculative parallel algorithm.