Readings in Database Systems (5th Edition) (2015)
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The "Red Book," a revered collection of database research papers, has resurfaced, sparking nostalgia and curiosity among tech enthusiasts. Commenters noted that the book's links to research papers rely on Google Scholar, which may not be accessible everywhere, prompting suggestions to use Sci-Hub, a controversial repository of academic papers. As some users reported being blocked while trying to access the site, speculation arose that certain providers or regions might be restricting access, with one user jokingly attributing it to a possible mix-up with Maoist literature due to the book's nickname. Amidst the chatter, the thread highlighted the ongoing relevance of the "Red Book" and the challenges of accessing academic research.
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“Rather than secure rights to the recommended papers, we have simply provided links to Google Scholar searches that should help the reader locate the relevant papers.”
https://ibb.co/BVrzQRWH
I just switched networks (wifi/mobile) and it worked, only that provider seems to block it
Readings in Database Systems (commonly known as the "Red Book") has offered readers an opinionated take on both classic and cutting-edge research in the field of data management since 1988. Here, we present the Fifth Edition of the Red Book — the first in over ten years. CHAPTERS Preface [HTML] [PDF] Background introduced by Michael Stonebraker [HTML] [PDF] Traditional RDBMS Systems introduced by Michael Stonebraker [HTML] [PDF] Techniques Everyone Should Know introduced by Peter Bailis [HTML] [PDF] New DBMS Architectures introduced by Michael Stonebraker [HTML] [PDF] Large-Scale Dataflow Engines introduced by Peter Bailis [HTML] [PDF] Weak Isolation and Distribution introduced by Peter Bailis [HTML] [PDF] Query Optimization introduced by Joe Hellerstein [HTML] [PDF] Interactive Analytics introduced by Joe Hellerstein [HTML] [PDF] Languages introduced by Joe Hellerstein [HTML] [PDF] Web Data introduced by Peter Bailis [HTML] [PDF] A Biased Take on a Moving Target: Complex Analytics by Michael Stonebraker [HTML] [PDF] A Biased Take on a Moving Target: Data Integration by Michael Stonebraker [HTML] [PDF] Complete Book: [HTML] [PDF] Readings Only: [HTML] [PDF] Previous Editions: [HTML]
Some might argue the Red Book to be “NSA Trusted Networks” a.k.a ugly red book that won't fit on a shelf.
Some might argue the Red Book to be “NSA Trusted Networks” a.k.a the ugly red book that won't fit on the shelf.
Crash & Burn <3
- Vector databases and hybrid search?
- Object storage for all the things? Lake houses. Parquet and beyond.
- Continuously materialized views? I'm not sure this one has made the splash but I think about Naiad (Materialize) and Noria (Readyset)
- NewSQL went mostly mainstream (Spanner wasn't included in the last one, but there's been more here with things like CockroachDB, TiDB, etc)
Not only D4M can cater for structured relational data, it's also suitable for non-structured and sparse data in spreadsheet, matrices and graph. It's essentially a generalization of SQL but for all things data.
There's also integration of D4M with SciDB [3].
[1] D4M: Dynamic Distributed Dimensional Data Model:
https://d4m.mit.edu/
[2] GraphQL:
https://graphql.org/
[3] D4M: Bringing associative arrays to database engines:
https://arxiv.org/abs/1508.07371
Parquet, Iceberg, and other open formats seem good, but they may hit a complexity wall. There's already some inconsistency between platforms, eg with delete vectors.
Incremental view maintenance interests me as well, and I would like to see it more available on different platforms. It's ironic that people use dbt etc. to test every little edit of their manually coded delta pipelines, but don't look at IVM.
2020 (225 points, 30 comments) https://news.ycombinator.com/item?id=15436647
2017 (247 points, 44 comments) https://news.ycombinator.com/item?id=15436647
2015 (189 points, 37 comments) https://news.ycombinator.com/item?id=10694538