Efficient key-value (KV) store becomes important for concurrent and distributed systems to deliver high performance. The promising learned indexes leverage deep-learning models to complement existing KV stores and obtain significant performance improvements. However, existing schemes show limited scalability in concurrent systems due to containing high dependency among data. The practical system performance decreases when inserting a large amount of new data due to triggering frequent and inefficient retraining operations. Moreover, existing learned indexes become inefficient in distributed systems, since different machines incur high overheads to guarantee the data consistency when the index structures dynamically change. To address these problems in concurrent and distributed systems, we propose a fine-grained learned index scheme with high scalability, called FineStore, which constructs independent models with a flattened data structure under the trained data array to concurrently process the requests with low overheads. FineStore processes the new requests in-place with the support of non-blocking retraining, hence adapting to the new distributions without blocking the systems. In the distributed systems, different machines efficiently leverage the extended RCU barrier to guarantee the data consistency. We evaluate FineStore via YCSB and real-world datasets, and extensive experimental results demonstrate that FineStore improves the performance respectively by up to 1.8× and 2.5× than state-of-the-art XIndex and Masstree. We have released the open-source codes of FineStore for public use in GitHub.

Index structures in memory systems become important to improve the entire system performance. The promising learned indexes leverage deep-learning models to complement existing index structures and obtain significant performance improvements. Existing schemes rely on a delta-buffer to support the scalability, which however incurs high overheads when a large number of data are inserted, due to the needs of checking both learned indexes and extra delta-buffer. The practical system performance also decreases since the shared delta-buffer quickly becomes large and requires frequent retraining due to high data dependency. To address the problems of limited scalability and frequent retraining, we propose a FINE-grained learned index scheme with high scalability, called FINEdex, which constructs independent models with a flattened data structure (i.e., the data arrays with low data dependency) under the trained data array to concurrently process the requests with low overheads. By further efficiently exploring and exploiting the characteristics of the workloads, FINEdex processes the new requests in-place with the support of non-blocking retraining, hence adapting to the new distributions without blocking the systems. We evaluate FINEdex via YCSB and real-world datasets, and extensive experimental results demonstrate that FINEdex improves the performance respectively by up to 1.8× and 2.5× than state-of-the-art XIndex and Masstree. We have released the open-source codes of FINEdex for public use in GitHub.

The extreme volume and staggeringly increasing rate inevitably produce unprecedented pressure on any large scale video sharing and hosting systems. Among the efforts to mitigate this pressure, content-based video similarity search is becoming more and more important with the exponential growth of the data size. Though various approaches have been proposed to address this problem, they are mainly focusing on the retrieval accuracy thus bringing video features with high complexity. Due to the complexity of the feature, these systems are based on the assumption that features representing videos have been obtained offline and stored in the database statically. However, the on-call efforts to move the feature extraction and similarity search from offline to online have been ignored in previous work. In this paper, we propose Marlin, a streaming data processing pipeline that efficiently extracts video features and retrieves video similarity information in a large scale video data system. We design a streaming feature extractor to handle the videos streaming into the system and establish the fined-grained resource allocation with a resource-aware data abstraction layer over streaming data to allocate computing resources among the videos with various resource demands. Besides that, we are pipelining the feature extraction and similarity search process with a distributed feature index, which supports real-time query and incremental index update. The experimental and the extensive real-world workload driven simulation results show that the proposed stream processing architecture achieves 25X speedup against the sequential feature extraction algorithm and 23X speedup against the sequential similarity search with a subsecond similarity query latency for a single request.