Materialized views for eventually consistent record stores
Bibliographic record
Abstract
Distributed, replicated keyed-record stores are often used by applications that place a premium on high availability and scalability. Such systems provide fast access to stored records given a primary key value, but access without the primary key may be very slow and expensive. This problem can be addressed using materialized views. Materialized views redundantly store records, or parts of records, and the redundant copies can be organized and distributed differently than the originals, e.g, according to the value of a secondary key. In this paper, we consider the problem of supporting materialized views in multi-master, eventually consistent keyed-record stores. Incremental maintenance of materialized views is challenging in such systems because there no single master server responsible for serializing the updates to each record. We present a decentralized technique for incrementally maintaining materialized views in multi-master systems. We have implemented a prototype of our technique using Cassandra, a widely used system of this type. Using the prototype, we show that secondary-key-based access is much faster using materialized views than using Cassandra's native secondary indexes, but maintaining the views in the face of updates may be more expensive than maintaining indexes.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".