Bibliographic record
Abstract
Hot standby systems often have to trade safety (i.e., not losing committed work) and freshness (i.e., having access to recent updates) for performance. Guaranteeing safety requires synchronous log shipping that blocks the primary until the log records are durably replicated in one or multiple backups; maintaining freshness necessitates fast log replay on backups, but is often defeated by the dual-copy architecture and serial replay: a backup must generate the "real" data from the log to make recent updates accessible to read-only queries. This paper proposes Query Fresh , a hot standby system that provides both safety and freshness while maintaining high performance on the primary. The crux is an append-only storage architecture used in conjunction with fast networks (e.g., InfiniBand) and byte-addressable, non-volatile memory (NVRAM). Query Fresh avoids the dual-copy design and treats the log as the database, enabling lightweight, parallel log replay that does not block the primary. Experimental results using the TPC-C benchmark show that under Query Fresh, backup servers can replay log records faster than they are generated by the primary server, using one quarter of the available compute resources. With a 56Gbps network, Query Fresh can support up to 4--5 synchronous replicas, each of which receives and replays ∼1.4GB of log records per second, with up to 4--6% overhead on the primary compared to a standalone server that achieves 620kTPS without replication.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.122 | 0.060 |
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".