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Modern transactional processing systems need to be fast and scalable, but this means many such systems settled for weak consistency models. It is however possible to achieve all of strong consistency, high scalability and high performance, by using fine-grained partitions and light-weight concurrency control that avoids superfluous synchronization and other overheads such as lock management. Independent transactions are one such mechanism, that rely on good partitions and appropriately defined transactions. On the downside, it is not usually straightforward to determine optimal partitioning schemes, especially when dealing with non-trivial amounts of data. Our work attempts to solve this problem by automating the partitioning process, choosing the correct transactional primitive, and routing transactions appropriately.

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We present Archie, a high performance fault-tolerant transactional system. Archie complies with the State Machine Approach, where the transactional state is fully replicated and total ordered transactions are executed on the replicas. Archie avoids the serial execution after transactions get ordered, which is the typical bottleneck of those protocols, by anticipating the work and using speculation to process transactions in parallel, enforcing a predefined order. The key feature of Archie is to avoid any non-trivial operation to perform post total order's notification, in case the sequencer node remains stable (only a single timestamp increment is needed for committing a transaction). This approach significantly shortens the transaction's critical path. The contention of speculative execution is always kept limited by activating a fixed number of transactions at a time. A comprehensive evaluation, using three competitors and three well known benchmarks, shows that Archie outperforms competitors in all medium/high contention scenarios.