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RDMA ( Remote Direct Memory Access ) has gained considerable interests in network-attached in-memory key-value stores. However, traversing the remote tree-based index in ordered key-value stores with RDMA becomes a critical obstacle, causing an order-of-magnitude slowdown and limited scalability due to multiple round trips. Using index cache with conventional wisdom—caching partial data and traversing them locally—usually leads to limited effect because of unavoidable capacity misses, massive random accesses, and costly cache invalidations. We argue that the machine learning (ML) model is a perfect cache structure for the tree-based index, termed learned cache . Based on it, we design and implement XStore , an RDMA-based ordered key-value store with a new hybrid architecture that retains a tree-based index at the server to perform dynamic workloads (e.g., inserts) and leverages a learned cache at the client to perform static workloads (e.g., gets and scans). The key idea is to decouple ML model retraining from index updating by maintaining a layer of indirection from logical to actual positions of key-value pairs. It allows a stale learned cache to continue predicting a correct position for a lookup key. XStore ensures correctness using a validation mechanism with a fallback path and further uses speculative execution to minimize the cost of cache misses. Evaluations with YCSB benchmarks and production workloads show that a single XStore server can achieve over 80 million read-only requests per second. This number outperforms state-of-the-art RDMA-based ordered key-value stores (namely, DrTM-Tree, Cell, and eRPC+Masstree) by up to 5.9× (from 3.7×). For workloads with inserts, XStore still provides up to 3.5× (from 2.7×) throughput speedup, achieving 53M reqs/s. The learned cache can also reduce client-side memory usage and further provides an efficient memory-performance tradeoff, e.g., saving 99% memory at the cost of 20% peak throughput.

DrTM is a fast in-memory transaction processing system that exploits advanced hardware features such as remote direct memory access (RDMA) and hardware transactional memory (HTM). To achieve high efficiency, it mostly offloads concurrency control such as tracking read/write accesses and conflict detection into HTM in a local machine and leverages the strong consistency between RDMA and HTM to ensure serializability among concurrent transactions across machines. To mitigate the high probability of HTM aborts for large transactions, we design and implement an optimized transaction chopping algorithm to decompose a set of large transactions into smaller pieces such that HTM is only required to protect each piece. We further build an efficient hash table for DrTM by leveraging HTM and RDMA to simplify the design and notably improve the performance. We describe how DrTM supports common database features like read-only transactions and logging for durability. Evaluation using typical OLTP workloads including TPC-C and SmallBank shows that DrTM has better single-node efficiency and scales well on a six-node cluster; it achieves greater than 1.51, 34 and 5.24, 138 million transactions per second for TPC-C and SmallBank on a single node and the cluster, respectively. Such numbers outperform a state-of-the-art single-node system (i.e., Silo) and a distributed transaction system (i.e., Calvin) by at least 1.9X and 29.6X for TPC-C.

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