Distributed Systems Reading List
Introduction I often argue that the toughest thing about distributed systems is changing the way you think. The below is a collection of material I've found useful for motivating these changes. Thought Provokers Ramblings that make you think about the way you design. Not everything can be solved with big servers, databases and transactions. Harvest, Yield and Scalable Tolerant Systems - Real world
[#JTF2020 解説版]理解して拡げる 分散システムの基礎知識 - Qiita
はじめに July Tech Festa 2020において、「マイクロサービスの今だからこそ！理解して拡げる 分散システムの基礎知識」のタイトルで登壇をしてきました。スライドはこちらにありますが、資料内や当日のトークで話せていない部分を含めて、こちらでblogとして解説をしておきたいと思います。 1. セッションの導入 - 新たなムチャブリ - 今回は昨年の#JTF2019で私が話した、「Cloud Native開発者のためのDatabase with Kubernetes」からの続編という形にしてみました。 昨年は、 「せっかくKubernetesを使うのにアプリケーションだけじゃもったいない。 DB、そしてステートフルなワークロードにも適用していきましょう」 という話をしましたが、Kubernetes-native Testbedなど、そうした取り組みが増えつつある傾向にはとても興味を
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GitHub - erikgrinaker/toydb: Distributed SQL database in Rust, written as a learning project
Distributed SQL database in Rust, written as a learning project. Most components are built from scratch, including: Raft-based distributed consensus engine for linearizable state machine replication. ACID-compliant transaction engine with MVCC-based snapshot isolation. Pluggable storage engine with BitCask and in-memory backends. Iterator-based query engine with heuristic optimization and time-tra
No more DSLs: Implement and deploy a distributed system with a single program
No more DSLs: Implement and deploy a distributed system with a single program If you want to write a distributed system, then instead of writing a thousand different programs and configuration files in many different DSLs, you can use an approach I call "single-program systems", and write just one program. In this article, I describe how we do this at $DAYJOB, and I show some example programs. The
CSE 138: Distributed Systems
From the beginning of this year, I started to take lecture courses of undergrad distributed systems course at UC Santa Cruz (CSE 138) by Lindsey Kuper. It consists of 23 lectures (you can see the schedule of topics from here) and recently I’ve finished all of them. I’m not a student at UCSC but due to the COVID-19 situation, all these lectures were delivered online and are available on YouTube. So
Understanding laws of scalability and the effects on a distributed system
A reactive system primarily focuses on responsiveness, elasticity, message-driven, and resiliency as its core features. Elasticity is one of the main components in the reactive manifesto. An elastic system has the ability to scale up or scale down when there is an increase/decrease in demand while remaining responsive. Scenarios where a system needs to improve the throughput or needs to handle mor
PigPaxos: Devouring the communication bottlenecks in distributed consensus
On distributed systems broadly defined and other curiosities. The opinions on this site are my own. This is our most recent work, started and led by Aleksey Charapko. (This is a joint post with him.) You can get the paper at arxiv.org. The paper is currently under submission to a journal. The story One day I challenged Aleksey to give me a ballpark number on how much he thinks we can scale Paxos v
分散システムの課題
Amazon が 2 台目のサーバーを追加した時から、分散システムは Amazon で馴染み深いものになりました。私が 1999 年に Amazon に入社したとき、サーバーの数が非常に少なかったため、「fishy」や「online-01」などのわかりやすい名前を付けることができました。けれども、1999 年であっても、分散コンピューティングは容易ではありませんでした。また現時点で、分散システムの課題には、レイテンシー、スケーリング、ネットワーキング API の理解、データのマーシャリングとアンマーシャリング、および Paxos などのアルゴリズムの複雑さが含まれます。システムが急速に大きくなり、分散するにつれて、理論的なエッジケースであったものが定期的に発生しました。 信頼できる長距離電話ネットワークやアマゾン ウェブ サービス (AWS) のサービスといった分散ユーティリティコンピュー
Implementing Raft: Part 0 - Introduction - Eli Bendersky's website
This is the first post in a multi-part series describing the Raft distributed consensus algorithm and its complete implementation in Go. Here is a complete list: Part 0: Introduction (this post) Part 1: Elections Part 2: Commands and log replication Part 3: Persistence and optimizations Raft is a relatively new algorithm (2014), but it's already being used quite a bit in industry. The best known e
Jepsen: etcd 3.4.3
The etcd key-value store is a distributed database based on the Raft consensus algorithm. In our 2014 analysis, we found that etcd 0.4.1 exhibited stale reads by default. We returned to etcd, now at version 3.4.3, to investigate its safety properties in detail. We found that key-value operations appear to be strict serializable, and that watches deliver every change to a key in order. However, etc
Distributed Systems Safety Research
About Jepsen Jepsen is an effort to improve the safety of distributed databases, queues, consensus systems, etc. We maintain an open source software library for systems testing, as well as blog posts and conference talks exploring particular systems’ failure modes. In each analysis we explore whether the system lives up to its documentation’s claims, file new bugs, and suggest recommendations for
Mergeable replicated data types – Part I | the morning paper
the morning paper a random walk through Computer Science research, by Adrian Colyer Made delightfully fast by strattic Mergeable replicated data types Kaki et al., OOPSLA’19 This paper was published at OOPSLA, but perhaps it’s amongst the distributed systems community that I expect there to be the greatest interest. Mergeable Replicated Data Types (MRDTs) are in the same spirit as CRDTs but with t
Parallel Commits: An atomic commit protocol for globally distributed transactions
Distributed ACID transactions form the beating heart of CockroachDB. They allow users to manipulate any and all of their data transactionally, no matter where it physically resides. Distributed transactions are so important to CockroachDB’s goal to “Make Data Easy” that we spend a lot of time thinking about how to make them as fast as possible. Specifically, CockroachDB specializes in globally dis
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GitHub - jepsen-io/elle: Black-box transactional safety checker based on cycle detection
GitHub - DistributedSystemsWiki/wiki
GitHub - bastion-rs/bastion: Highly-available Distributed Fault-tolerant Runtime
https://www.semanticscholar.org/paper/A-distributed-execution-engine-supporting-control-Murray/5f52a841c93054e63e1cab45fd5a71d777996411?p2df
Time, clocks, and the ordering of events in a distributed system
SOSP19 File Systems Unfit as Distributed Storage Backends: Lessons from 10 Years of Ceph Evolution
File systems unfit as distributed storage backends: lessons from ten years of Ceph evolution | the morning paper
