<i> B <sup>link</sup> </i> -hash: An Adaptive Hybrid Index for In-Memory Time-Series Databases
Bibliographic record
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, B link -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 B link -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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".