In this paper, a related-subspace-based local outlier detection algorithm is proposed in MapReduce programming model for high-dimensional and massive data set. Firstly, the relevant subspace, which can effectively describe the local distribution of the various data sets, is redefined by using local sparseness of attribute dimensions. Secondly, a local outlier factor calculation formula in the relevant subspace is defined with probability density of local data sets. The formula can not only effectively reflect the outlierness of data object that does not obey the distribution of the local data set in relevant subspace, but also select N data objects with the greatest-outlierness as local outliers. Furthermore, a related-subspace-based local outlier detection algorithm is constructed by using LSH distributed strategy in MapReduce programming model. Finally, experimental results validate the effectiveness, scalability and extensibility of the presented algorithms by using artificial data and stellar spectral data as experimental data sets.