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Modern online services rely on data stores that replicate their data acrossgeographically distributed data centers. Providing strong consistency in suchdata stores results in high latencies and makes the system vulnerable tonetwork partitions. The alternative of relaxing consistency violates crucialcorrectness properties. A compromise is to allow multiple consistency levels tocoexist in the data store. In this paper we present UniStore, the firstfault-tolerant and scalable data store that combines causal and strongconsistency. The key challenge we address in UniStore is to maintain livenessdespite data center failures: this could be compromised if a strong transactiontakes a dependency on a causal transaction that is later lost because of afailure. UniStore ensures that such situations do not arise while paying thecost of durability for causal transactions only when necessary. We evaluateUniStore on Amazon EC2 using both microbenchmarks and a sample application. Ourresults show that UniStore effectively and scalably combines causal and strongconsistency.

UniStore: A fault-tolerant marriage of causal and strong consistency (extended version)