Time series similarity search is one of the most basic operations for temporal data analysis, which has various application scenarios. Existing distributed methods face the problems of dimension explosion, too large scan range, and time-consuming similarity calculation. To this end, this study proposes a distributed time series similarity search algorithm KV-Search. First, time series are segmented into blocks and stored in the key-value database, which solves the problem of high and growing dimension. Second, the lower bound is calculated based on Chebyshev distance, and the invalid data is filtered out in advance using key value range scanni ng, which reduce the data transmission and calculation overhead. Third, a block-based time series representation is used to calculate the lower bound of distance, which avoids the calculation of higher dimensional real data. KV-Search is implemented based on HBase, and a set of extensive experiments are conducted using both real and synthetic time series data. The results show that the proposed KV-Search is superior to benchmark experiment in efficiency and scalability.