Fast In-Memory Transaction Processing Using RDMA and HTM 6

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Contributor(s): ACM Transactions on Computer Systems.35:1 (2017). pp. 3 5 6 [] |
Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; 46Edition: Description: Content type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 Haibo Chen, Rong Chen 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- IN-MEMORY TRANSACTIONS;HARDWARE TRANSACTIONAL MEMORY -- DISTRIBUTED TRANSACTIONS;REMOTE DIRECT MEMORY ACCESS -- KEY-VALUE STORES -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:
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ABSTRACT: 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 oflarge 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 simply the design and notably improve the performance. We describe how DrTM supports common database features like read - only transactionsand logging for durability. Evaluation using typical OLTP workloads including TPC-C and Small Bank shows that DrTM has better single-node efficiency and scales well on a six-node cluster, it achieves greater than1.51,34 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-art single-node system (i.e.,Silo) and adistributed transaction system (i.e.,Calvin) by at least 1.9X and 29.6X for TPC-C. 56

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