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Designing Data-Intensive Applications - Chapter 6 - Partitioning

Designing Data-Intensive Applications - Chapter 6 - Partitioning

Translations: RU
Earlier the book club of our company has studied excellent book: Martin Kleppmann - Designing Data-Intensive Applications This is the best book I have read about building complex scalable software systems. 💪 As usually I prepared an overview and mind-map. Chapter 6 contains everything the DEV team should consider when designing storage for big data: Partition aka Shard aka Region aka Tablet aka vNode aka vBucket. It is another approach for storing the data in addition to Replication (reviewed in the previous chapter) How to partition key-value data (primary index).
Designing Data-Intensive Applications - Chapter 5 - Replication

Designing Data-Intensive Applications - Chapter 5 - Replication

Translations: RU
Earlier the book club of our company has studied excellent book: Martin Kleppmann - Designing Data-Intensive Applications This is the best book I have read about building complex scalable software systems. 💪 As usually I prepared an overview and mind-map. Chapter 5: Intro. How to scale apps. Replicating and partitioning. Three algos of replicating Single-leader Replication Leaders and Followers Sync and async replication Adding new Followers Handling node outages Technical implementations and all potential problems Multi-Leader Replication Use-cases when it is good Handling write conflicts Three topologies and potential problems Leaderless Replication Writing to the database when a node is down Quorums and problems with them Detecting concurrent writes and how to resolve them Download full mind map (PDF)
Designing Data-Intensive Applications - Chapter 4 - Encoding and Evolution

Designing Data-Intensive Applications - Chapter 4 - Encoding and Evolution

Translations: RU
Earlier this year the book club of our company has studied excellent book: Martin Kleppmann - Designing Data-Intensive Applications This is the best book I have read about building complex scalable software systems. 💪 As usually I prepared an overview and mind-map. Chapter 4: What is evolvability. Backward and Forward compatibility Approaches to encode data: JSON, XML, and their binary variants Thrift and Protobuf Apache Avro Models of data flow Through databases Through services: REST, SOAP, RPC and the future Through message brokers - when they are better and when they are not Much more details in the mind-map:
Designing Data-Intensive Applications - Chapter 3 - Storage and Retrieval

Designing Data-Intensive Applications - Chapter 3 - Storage and Retrieval

Translations: RU
Earlier this year the book club of our company has studied excellent book: Martin Kleppmann - Designing Data-Intensive Applications This is the best book I have read about building complex scalable software systems. 💪 As usually I prepared an overview and mind-map. Chapter 3: Data structures: Log-structured. SSTables / LSM-trees (when we don’t update anything but write to the end). A very cool idea of how to store data.
Designing Data-Intensive Applications - Chapter 2 - Data Models and Query Languages

Designing Data-Intensive Applications - Chapter 2 - Data Models and Query Languages

Translations: RU
Earlier this year the book club of our company has studied excellent book: Martin Kleppmann - Designing Data-Intensive Applications This is the best book I have read about building complex scalable software systems. 💪 As usually I prepared an overview and mind-map. Chapter 2: What is data model. Different relations between the data. Relational, Document, Graph data models. Which one is better and when. Schema-on-write, schema-on-read (schemaless). Data locality.
Designing Data-Intensive Applications - Chapter 1 - Reliable, Scalable, and Maintainable Applications

Designing Data-Intensive Applications - Chapter 1 - Reliable, Scalable, and Maintainable Applications

Translations: RU
Earlier this year the book club of our company has studied excellent book: Martin Kleppmann - Designing Data-Intensive Applications This is the best book I have read about building complex scalable software systems. 💪 As usually (to better learn) I prepared an overview and mind-map. Chapter 1: Building blocks of the apps What is Reliability, Scalability and Maintainability. Examples and definitions. Faults and Failures Performance, Load, Latency and Response Time Operability, Simplicity, Evolvability Why you should randomly kill your servers 😅 How Twitter delivers 12,000 tweets per second to 300,000 readers per second.
Golang introduces generics

Golang introduces generics

Translations: RU
Golang FINALLY introduces GENERICS (aka templates, aka type parameters) in release 1.18 (in Feb 2022) I remember the early 2000s when generics where added to C#, and how they were awaited… These days Go is my favourite language for writing highly-scalable solutions and generics are the key thing I’ve been waiting for. They should significantly simplify design of the apps in some cases. My mind map with key things you should know:
Daily Stand-up: You're Doing It Wrong!

Daily Stand-up: You're Doing It Wrong!

Translations: RU
Do you use daily standup meetings? How standard daily standups are organized Everyone (one-by-one) is asked about: yesterday activities, today activities, blockers. But this approach has common problem: people are not always listen to others! Because the next person thinks what to say when it’s their turn. There is a better model - “Walking the Board” Discuss every ticket on the board one-by-one. The person who worked on the ticket says a few words, can raise any problems that are immediately addressed.
Clean Architecture - PART IV - Component Principles

Clean Architecture - PART IV - Component Principles

Translations: RU
The book club of our company has chosen a new wonderful book for reading: Robert Martin - Clean Architecture - a Craftsman’s Guide to Software Structure and Design Fourth part of the book is about principles of combining components into software systems. This part is more interesting. It contains: Overview of components history: Relocatability, Linkers Three principles of Component Cohesion REP: The Reuse/Release Equivalence Principle CCP: The Common Closure Principle CRP: The Common Reuse Principle Three principles of Components Coupling ADP: The Acyclic Dependencies Principle SDP: The Stable Dependencies Principle SAP: The Stable Abstractions Principle I especially enjoyed this chapter because of presented metrics that could be used to measure(!