ACM Computing Surveys (CSUR)
Split-ordered lists: lock-free extensible hash tables
Proceedings of the twenty-second annual symposium on Principles of distributed computing
The case for RAMClouds: scalable high-performance storage entirely in DRAM
ACM SIGOPS Operating Systems Review
Expandable open addressing hash table storage and retrieval
SIGFIDET '71 Proceedings of the 1971 ACM SIGFIDET (now SIGMOD) Workshop on Data Description, Access and Control
Benchmarking cloud serving systems with YCSB
Proceedings of the 1st ACM symposium on Cloud computing
An analysis of Linux scalability to many cores
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
SILT: a memory-efficient, high-performance key-value store
SOSP '11 Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles
LHlf: lock-free linear hashing (poster paper)
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
CPHASH: a cache-partitioned hash table
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
Cache craftiness for fast multicore key-value storage
Proceedings of the 7th ACM european conference on Computer Systems
Workload analysis of a large-scale key-value store
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
MemC3: compact and concurrent MemCache with dumber caching and smarter hashing
nsdi'13 Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
nsdi'13 Proceedings of the 10th USENIX conference on Networked Systems Design and Implementation
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While the initial wave of in-memory key-value stores has been optimized for serving relatively fixed content to a very large number of users, an emerging class of enterprise-scale data analytics workloads focuses on capturing, analyzing, and reacting to data in real-time. At the same time, advances in network technologies are shifting the performance bottleneck from the network to the memory subsystem. To address these new trends, we present a bottom-up approach to building a high performance in-memory key-value store, Mercury, for both traditional, read-intensive as well as emerging workloads with high write-to-read ratio. Mercury's architecture is based on two key design principles: (i) economizing the number of DRAM accesses per operation, and (ii) reducing synchronization overheads. We implement these principles with a simple hash table with linked-list based chaining, and provide high concurrency with a fine-grained, cache-friendly locking scheme. On a commodity single-socket server with 12 cores, Mercury scales with number of cores and executes 14 times more queries/second than a popular hash-based key-value system, Memcached, for both read and write-heavy workloads.