The SOH (SQL over HDFS) systems usually store the data into distributed file system HDFS (Hadoop distributed file system), and process queries by the Map/Reduce computing framework or distributed database query engine. Benefitting from the fault tolerance and scalability provided by Map/Reduce and HDFS, SOH systems perform well in processing analytical queries over big data. However, the efficiency of such systems is too low to meet the requirement of selective queries or interactive queries which have strict limit on the query response time. This paper proposes a HDFS-based index, called HIndex, for SOH systems. HIndex can easily be integrated into the existing SOH systems to improve the efficiency of query evaluation. The process that SOH systems access data stored in HDFS is analyzed, and the important factors affecting the time cost is highlighted, a two-layer index structure is proposed, and both aggregated and non-aggregated index techniques are implemented. According to the experiments conducted on standard datasets, HIndex performs much better than Hadoop++, a state-of-the-art HDFS-based index.