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Rose is a database storage engine for high-throughput replication. It targets seek-limited, write-intensive transaction processing workloads that perform near real-time decision support and analytical processing queries. Rose uses log structured merge (LSM) trees to create full database replicas using purely sequential I/O, allowing it to provide orders of magnitude more write throughput than B-tree based replicas. Also, LSM-trees cannot become fragmented and provide fast, predictable index scans. Rose's write performance relies on replicas' ability to perform writes without looking up old values. LSM-tree lookups have performance comparable to B-tree lookups. If Rose read each value that it updated then its write throughput would also be comparable to a B-tree. Although we target replication, Rose provides high write throughput to any application that updates tuples without reading existing data, such as append-only, streaming and versioning databases. We introduce a page compression format that takes advantage of LSM-tree's sequential, sorted data layout. It increases replication throughput by reducing sequential I/O, and enables efficient tree lookups by supporting small page sizes and doubling as an index of the values it stores. Any scheme that can compress data in a single pass and provide random access to compressed values could be used by Rose. Replication environments have multiple readers but only one writer. This allows Rose to provide atomicity, consistency and isolation to concurrent transactions without resorting to rollback, blocking index requests or interfering with maintenance tasks. Rose avoids random I/O during replication and scans, leaving more I/O capacity for queries than existing systems, and providing scalable, real-time replication of seek-bound workloads. Analytical models and experiments show that Rose provides orders of magnitude greater replication bandwidth over larger databases than conventional techniques.