Data items are usually replicated in modern distributed data stores to obtain high performance and availability. However, the availability-consistency and latencyconsistency trade-offs exist in data replication, thus system designers intend to choose weak consistency models, such as eventual consistency, which may result in stale reads. Since stale data items may lead to serious application semantic problems, we consider how to increase the probability of data recency which provides a uniform view on recent versions of data items for all clients. In this work, we propose HARP, a framework that can enhance data recency of eventually consistent distributed data stores in an efficient and highly available way. Through detecting possible stale reads under failures or not, HARP can perform reread operations to eliminate stale results only when needed based on our analysis on write/read processes. We also present solutions on how to deal with some practical anomalies in HARP, including delayed, reordered and dropped messages and clock drift, and show how to extend HARP to multiple datacenters. Finally we implement HARP based on Cassandra, and the experiments show that HARP can effectively eliminate stale reads, with a low overhead (less than 6.9%) compared with original eventually consistent Cassandra.