Blink-hash: An Adaptive Hybrid Index for In-Memory Time-Series Databases

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Abstract

High-speed data ingestion is critical in time-series workloads that are driven by the growth of Internet of Things (IoT) applications. We observe that traditional tree-based indexes encounter severe scalability bottlenecks for time-series workloads that insert monotonically increasing timestamp keys into an index; all insertions go to a small memory region that sees extremely high contention. In this work, we present a new index design, 𝐵link-hash, that enhances a tree-based index with hash leaf nodes to mitigate the contention of monotonic insertions — insertions go to random locations within a hash node (which is much larger than a B+-tree node) to reduce conflicts. We develop further optimizations (median approximation and lazy split) to accelerate hash node splits. We also develop structure adaptation optimizations to dynamically convert a hash node to B+-tree nodes for good scan performance. Our evaluation shows that 𝐵link-hash achieves up to 91.3× higher throughput than conventional indexes in a time-series workload that monotonically inserts timestamps into an index, while showing comparable scan performance to a well-optimized B+-tree.