The Art of Latency Hiding in Modern Database Engines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern database engines must well use multicore CPUs, large main memory and fast storage devices to achieve high performance. A common theme is hiding latencies such that more CPU cycles can be dedicated to "real" work, improving overall throughput. Yet existing systems are only able to mitigate the impact of individual latencies, e.g., by interleaving memory accesses with computation to hide CPU cache misses. They still lack the joint optimization of hiding the impact of multiple latency sources. This paper presents MosaicDB, a set of latency-hiding techniques to solve this problem. With stackless coroutines and carefully crafted scheduling policies, we explore how I/O and synchronization latencies can be hidden in a well-crafted OLTP engine that already hides memory access latency, without hurting the performance of memory-resident workloads. MosaicDB also avoids oversubscription and reduces contention using the coroutine-to-transaction paradigm. Our evaluation shows MosaicDB can achieve these goals and up to 33x speedup over prior state-of-the-art.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it