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 8 discovers non-database related problems of distributed systems. DEV teams should consider them when designing distributed software.
Faults and Partial Failures. The need to build a reliable system from unreliable components.
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 7 is all your DEV team should know about Transactions:
The purpose of transactions The concept of transaction: ACID, BASE, single-object and multi-object transactions Weak Isolation Levels: Read Committed, Snapshot Isolation and Repeatable Read.
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).
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)
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:
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.
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.
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 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:
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(!
