SCHEDULE: NOV 15-20, 2015

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HydraDB: A Resilient RDMA-Driven Key-Value Middleware for In-Memory Cluster Computing

SESSION: Scalable Storage Systems

EVENT TYPE: Papers

EVENT TAG(S): Storage, Data-Intensive Computing

TIME: 2:30PM - 3:00PM

SESSION CHAIR(S): Gabriel Antoniu

AUTHOR(S):Yandong Wang, Li Zhang, Jian Tan, Min Li, Yuqing Gao, Xavier Guerin, Xiaoqiao Meng, Shicong Meng

ROOM:18CD

ABSTRACT:

In this paper, we describe our experiences and lessons learned from building an in-memory key-value middleware, called HydraDB. HydraDB synthesizes a collection of state-of-the-art techniques, including high-availability, RDMA, as well as multicore-awareness, etc, to deliver a high-throughput, low-latency access service in a reliable manner for cluster computing applications.

The uniqueness of HydraDB lies in its design commitment to exploit RDMA to comprehensively optimize various aspects of a general-purpose key-value store, including latency-critical operations, read enhancement, and replications for high-availability service, etc. Meanwhile, HydraDB strives to efficiently utilize multicore systems to prevent data manipulation from curbing the performance of RDMA.

Many teams in our organization have adopted HydraDB to improve the execution of their cluster computing frameworks, including MapReduce, Sensemaking analytics and Call Record Processing. In addition, performance evaluation with a variety of YCSB workloads also shows that HydraDB substantially outperforms several existing in-memory key-value stores by an order of magnitude.

Chair/Author Details:

Gabriel Antoniu (Chair) - French Institute for Research in Computer Science and Automation|

Yandong Wang - IBM Corporation

Li Zhang - IBM Corporation

Jian Tan - IBM Corporation

Min Li - IBM Corporation

Yuqing Gao - Microsoft Corporation

Xavier Guerin - Tower Research Capital LLC

Xiaoqiao Meng - Pinterest, Inc.

Shicong Meng - Facebook

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society