In this demonstration we present BRRL, a library for making distributed main-memory applications fault tolerant. BRRL is optimized for cloud applications with frequent points of consistency that use data-parallelism to avoid complex concurrency control mechanisms. BRRL differs from existing recovery libraries by providing a simple table abstraction and using schema information to optimize checkpointing. We will demonstrate the utility of BRRL using a distributed transaction processing system and a platform for scientific behavioral simulations.

Recent architectural trends — cheap, fast solid-state storage, inexpensive DRAM, and multi-core CPUs — provide an opportunity to rethink the interface between applications and persistent storage. To leverage these advances, we propose a new system architecture called Hathi that provides an in-memory transactional heap made persistent using high-speed flash drives. With Hathi, programmers can make consistent concurrent updates to in-memory data structures that survive system failures. Hathi focuses on three major design goals: ACID semantics, a simple programming interface, and fine-grained programmer control. Hathi relies on software transactional memory to provide a simple concurrent interface to in-memory data structures, and extends it with persistent logs and checkpoints to add durability. To reduce the cost of durability, Hathi uses two main techniques. First, it provides split-phase and partitioned commit interfaces, that allow programmers to overlap commit I/O with computation and to avoid unnecessary synchronization. Second, it uses partitioned logging, which reduces contention on in-memory log buffers and exploits internal SSD parallelism. We find that our implementation of Hathi can achieve 1.25 million txns/s with a single SSD. 1.