"Transactions: myths, surprises and opportunities" by Martin Kleppmann on HackerTube: recommended by 2 Hacker News commenters across 2 separate months, 2018–2023.
talk · intermediate · evergreen
Martin Kleppmann's Strange Loop talk on database transactions, covering why they were dropped by NoSQL, their reputation for being slow, and newer algorithms that make serializable transactions perform and scale. It touches on consistency, linearizability and the CAP theorem.
Recommended as a compact preview of Kleppmann's data systems material, including from his book on the subject.
“I recommend watching this talk from him to give a little glimpse of the book”
4 Hacker News comments link this video. Each one links back to its thread.
It's great. Incredibly dense with useful information and it just blows my mind how much knowledge Martin has about the topic. I recommend watching this talk from him to give a little glimpse of the book: ▶ this video This is just about a little part of one of the chapters.
in “Ask HN: What overlooked class of tools should a self-taught programmer look into” · View on HN
Rich Hickey - "simple made easy" [link] Rob Pike - "concurrency is not parallelism" [link] Uncle Bob Martin - "future of programming" [link] Martin Kleppmann - "transactions: myths, surprises and opportunities" ▶ this video Simon Brown - "software architecture vs code" [link]
in “Ask HN: What is your favourite tech talk?” · View on HN
jepsen.io has a neat diagram of various consistency models - [link] also, you can find talks of Martin Kleppmann on youtube about consistency guarantees, e. g., ▶ this video
in “UC Berkeley researchers set new visions for making CRDT data stores practical [pdf]” · View on HN
I mean, I hear you that people do that and it does allow you to avoid needing distributed transactions. When you're stuck with an underlying NoSQL database without transactions, this is the best thing you can do. This is the whole "invert the database" idea that ends up with [1]. I'm arguing that that usage pattern was painful and that if you can have horizontally scalable transaction processing and a stream of events due to committed transactions downstream, you can use that to power correct and easy to reason about materializations as well as asynchronous task processing. Transact to change state. React to state changes. Prosper. [1] ▶ this video
in “Kafka Is Not a Database” · View on HN