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LogECMem: Coupling Erasure-Coded In-Memory Key-Value Stores with Parity Logging
Event Type
Big Data
Data Analytics
Data Management
File Systems and I/O
Memory Systems
Reproducibility Badges
Registration Categories
TimeThursday, 18 November 20212pm - 2:30pm CST
DescriptionIn-memory key-value stores are often used to speed up Big Data workloads on modern HPC clusters. To maintain their high availability, erasure coding has been recently adopted as a low-cost redundancy scheme instead of replication. Existing erasure-coded update schemes, however, have either low performance or high memory overhead. In this paper, we propose a novel parity logging-based architecture, HybridPL, which creates a hybrid of in-place update (for data and XOR parity chunks) and log-based update (for the remaining parity chunks), so as to balance the update performance and memory cost, while maintaining efficient single-failure repairs. We realize HybridPL as an in-memory key-value store called LogECMem, and further design efficient repair schemes for multiple failures. We prototype LogECMem and conduct experiments on different workloads. We show that LogECMem achieves better update performance over existing erasure-coded update schemes with low memory overhead, while maintaining high basic I/O and repair performance.
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