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  1. Prikvačeni tweet

    Welcome to the Database Internals Advent Calendar! Over the course of this month, you'll see an excerpt from a book section, paired with a paper you can find and read to learn about more about how databases work.

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    17. sij

    📚 My to-read list for 2020 (so far): - 💡Deep Work - 🧬 Database Internals - 📈 Modern Value Investing - 📨 Streaming Systems - 🚨 Designing Event-Driven Systems - 📊 Capital in the Twenty-First Century Any suggestions?

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    19. sij

    Now available in India too, order from Shroff Publishers website and avail 10% discount with free shipping.

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    Database Internals輪読会#1の募集を開始しました。2/20(木)、田町で開催です。輪読形式ではありますが、DB利用者やDBA、研究者の方、皆がざっくばらんに議論できるように進めたいと思います。 第1章の発表者も引き続き募集中です。

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    Here are the books for the raffle! I picked a selection from the latest published books - with topics ranging from distributed systems, ML/AI and career development (managers). All of these (and more) are also available for online reading via Safari.

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    の輪読会したいなあ。つよつよなDBエンジニア集めてワイワイ盛り上がりたい。

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    25. pro 2019.

    2019 version of the same question: what technical (soft or ‘hard’ skills equally relevant this time) book published in the last year or so would you recommend to everyone? Slack still has the professional development funds, we’re still hiring!

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    24. pro 2019.

    I’m reading by Worth the e-book price for Appendix A alone: fantastic survey of seminal distributed system and database papers. Concepts are presented clearly with extensive references to source papers.

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  9. Happy Holidays everyone, and best wishes! I’m reading a bunch of new papers over the holidays and slowly getting ready for 2020. Stay tuned for more stuff from the real Databass next year!

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  10. Day 24: I can’t describe how hard it was to choose a final paper, since all of them are so great! Let’s finish with Main Memory Database Systems, both because it’s a trend, and since there’s a lot to learn about distinction with disk based stores:

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  11. Day 23: This paper might be old, but it is still relevant: among others, PostgreSQL implements concurrent splits using Blink-Trees; Bw-Trees use a similar “half-split” node condition, too. Check out the Efficient Locking for Concurrent Operations paper:

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  12. Day 22: Everyone knows about traditional, or “ordered” LSM Trees, but what if we were to keep order only for the keys in the LSM Tree, and not for the values? Check out WiscKey paper to learn more about this approach:

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    21. pro 2019.

    Finished reading by on database internals. This is an unusually in-depth and precise book on data structures and algorithms from a non-academic publisher, but extremely readable and compact. I feel like I learned a lot here efficiently.

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  14. Day 21: To isolate concurrently running transactions from one another, storage engines use a wide spectrum of tools. Check out this paper if you'd like to learn more about concurrency control and B-Tree lock-based techniques:

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  15. Day 20: Ever wondered how linearizability can be implemented in a distributed system? Check out the RIFL (reusable infrastructure for linearizability) paper, Implementing Linearizability at Large Scale and Low Latency, to learn more about that:

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  16. Day 19: From FLP and paper about unreliable failure detectors for reliable distributed systems, you know that FDs play an important role in solving Consensus. A great overview of sources: "A literature review of failure detection":

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  17. Day 18: From the team that has participated in development of Bw-Tree follow-up paper from CMU, a great paper on in-memory transactional processing. Cicada, transactions executed at a pace that wraps a 64-bit counter around in 10 days:

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  18. Day 17: This one is probably closer to a book than a paper, but it's a great source of information on disk-based data structures. Vitter, Jeffrey Scott. 2008. “Algorithms and data structures for external memory”:

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  19. Day 16: A great overview paper about specifics of design and implementation of coulmn-oriented database systems:

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  21. Day 15: Another awesome paper from HASLab (you're heard of them if you heard of CRDTs) is an anti-entropy technique, an alternative to Merkle Trees: Bitmap Version Vectors and Dotted Causal Containers: and an Erlang implementation:

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