As an append-only and read optimized open-source distributed file system, HDFS (Hadoop distributed file system) provides portability, high fault-tolerance, and massive horizontal scalability. Over the past decade, HDFS has been widely used for big data storage, and it manages various data, such as text, graph, key-values, etc. Moreover, big data systems based on or compatible with HDFS have been prevalent in many application scenarios such as complex SQL analysis, ad-hoc queries, interactive analysis, key-value storage, and iterative computation. HDFS has been the universal underlying file system to store massive data and support manifold analytical applications. Therefore, it is of great significance to optimizing the storage performance and data access efficiency of HDFS. In this study, the principles and features of HDFS are summarized and a survey on storage and optimization techniques of HDFS is carried out from three dimensions, including logic file structure, hardware, and application scenarios. It is also proposed that storage over heterogeneous hardware, workload-guided adaptive storage optimization, and storage optimization combined with machine learning technologies could be the most appealing research directions in the future.