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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Accelerating LSM-Tree with the Dentry Management of File ...
Yanpeng Hu, Chundong Wang · 2021-09-27 · via cs.DS updates on arXiv.org

The log-structured merge tree (LSM-tree) gains wide popularity in building key-value (KV) stores. It employs logs to back up arriving KV pairs and maintains a few on-disk levels with exponentially increasing capacity limits, resembling a tiered tree-like structure. A level comprises SST files, each of which holds a sequence of sorted KV pairs. From time to time, LSM-tree redeploys KV pairs from a full level to the lower level by compaction, which merge-sorts and moves KV pairs among SST files, thereby incurring substantial disk I/Os. In this paper, we revisit the design of LSM-tree and find that organizing multiple KV pairs in an SST file entails the heavyweight redeployment of actual KV pairs in a compaction. Accordingly we revolutionize the organization of KV pairs by transforming an SST file of KV pairs to an SST directory, in which each KV pair makes into an independent KV file with the key and value as filename and main file contents, respectively. Moving KV pairs in a compaction converts to transferring directory entries (dentrys), which causes concretely fewer disk I/Os. This is the essence of our design named DeLSM. We build a prototype of DeLSM on LevelDB and evaluation results show that it significantly outperforms the state-of-the-art LSM-tree variants in different dimensions.