The learned index technique has been widely explored as a strong competitor to traditional indexes. It adopts static learning-based models to fit the distribution of sorted data and locate keys through predictions, which shows outstanding query speed. However, frequent retraining is required when it comes to concurrent insertion scenarios. Despite existing studies introducing sparse slots and delta buffers to mitigate this effect, the read-write performance of the learned index still falls short of expectations, especially in concurrent conditions. In this paper, we first propose a novel hybrid index scheme that combines a read-efficient learned index with an insert-efficient Adaptive Radix Tree (ART) to realize high performance for read-write scenarios. However, it is not trivial due to expensive model prediction errors, complicated model hierarchy, and redundant node traversals. Therefore, we then introduce ALT-index, an efficient hybrid learned index with high concurrency for memory database systems. ALT-index highlights a delicate two-tier architecture where linear data are stored in the learned index without prediction errors and conflict data are hosted in the lower layer as an optimized ART. Besides, we develop a Greedy Pessimistic Linear (GPL) algorithm to support flattened data structures for concurrency. In the optimized ART layer, we introduce a fast and compact pointer buffer to further improve the overall performance. Experimental results conducted on various real-world datasets with 32 threads illustrate that ALT-index improves performance by up to 1.9x, 2.1x, and 2.3x compared with ALEX+, FINEdex, and XIndex in read-write-balanced scenarios, respectively.

Distributed file systems (DFS) are the cornerstone of modern mass data processing systems. In DFS, the metadata service, as the core component, often becomes a performance bottleneck. Existing metadata service solutions have implemented flattened metadata management and full-path indexing to achieve high scalability in terms of capacity and throughput. However, these approaches have limitations, such as conflicts with POSIX-style permission verification and flawed support for super directories, leading to high and unstable latency that cannot provide reliable service for latency-sensitive applications. To overcome these limitations, we propose Duplex, a scalable DFS metadata service based on full-path indexing, which aims for low and stable latency. Duplex incorporates three key designs: a fast access path featuring a centralized permission server for efficient permission verification, a permission merging algorithm to reduce the PMS's space footprint, and flattened metadata management based on double consistent hashing that enables low-latency access to super directories. Our evaluations demonstrate that, compared to state-of-the-art metadata solutions, Duplex significantly reduces the average lookup latency by up to 84% and the 99th percentile tail latency by up to 88.2% for metadataintensive benchmarks. Additionally, Duplex improves the lookup IOPS by up to 7.6 × /2.3× compared to CephFS and BeeGFS.