ScaleDB: A Scalable, Asynchronous In-Memory Database
Syed Akbar Mehdi, Deukyeon Hwang, Simon Peter, Lorenzo Alvisi
Abstract
ScaleDB is a serializable in-memory transactional database that achieves excellent scalability on multi-core machines by asynchronously updating range indexes. We find that asynchronous range index updates can significantly improve database scalability by applying updates in batches, reducing contention on critical sections. To avoid stale reads, ScaleDB uses small hash indexlets to hold delayed updates. We use indexlets to design ACC, an asynchronous concurrency control protocol providing serializability. With ACC, it is possible to delay range index updates without adverse performance effects on transaction execution in the common case. ACC delivers scalable serializable isolation for transactions, with high throughput and low abort rate. Evaluation on a dualsocket server with 36 cores shows that ScaleDB achieves 9.5× better query throughput than Peloton on the YCSB benchmark and 1.8× better transaction throughput than Cicada on the TPC-C benchmark.
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