Foundations of Scalable Systems Designing Distributed Architectures 1st Edition by Ian Gorton ISBN 1098106067 978-1098106065

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Product details:

ISBN 10: 1098106067 
ISBN 13:  978-1098106065
Author: Ian Gorton

In many systems, scalability becomes the primary driver as the user base grows. Attractive features and high utility breed success, which brings more requests to handle and more data to manage. But organizations reach a tipping point when design decisions that made sense under light loads suddenly become technical debt. This practical book covers design approaches and technologies that make it possible to scale an application quickly and cost-effectively.

Author Ian Gorton takes software architects and developers through the foundational principles of distributed systems. You’ll explore the essential ingredients of scalable solutions, including replication, state management, load balancing, and caching. Specific chapters focus on the implications of scalability for databases, microservices, and event-based streaming systems.

You will focus on:

• Foundations of scalable systems: Learn basic design principles of scalability, its costs, and architectural tradeoffs
• Designing scalable services: Dive into service design, caching, asynchronous messaging, serverless processing, and microservices
• Designing scalable data systems: Learn data system fundamentals, NoSQL databases, and eventual consistency versus strong consistency
• Designing scalable streaming systems: Explore stream processing systems and scalable event-driven processing

Foundations of Scalable Systems: Designing Distributed Architectures 1st Table of contents:

I. The Basics

1. Introduction to Scalable Systems

What Is Scalability?

Examples of System Scale in the Early 2000s

How Did We Get Here? A Brief History of System Growth

Scalability Basic Design Principles

Scalability and Costs

Scalability and Architecture Trade-Offs

Performance

Availability

Security

Manageability

Summary and Further Reading

2. Distributed Systems Architectures: An Introduction

Basic System Architecture

Scale Out

Scaling the Database with Caching

Distributing the Database

Multiple Processing Tiers

Increasing Responsiveness

Systems and Hardware Scalability

Summary and Further Reading

3. Distributed Systems Essentials

Communications Basics

Communications Hardware

Communications Software

Remote Method Invocation

Partial Failures

Consensus in Distributed Systems

Time in Distributed Systems

Summary and Further Reading

4. An Overview of Concurrent Systems

Why Concurrency?

Threads

Order of Thread Execution

Problems with Threads

Race Conditions

Deadlocks

Thread States

Thread Coordination

Thread Pools

Barrier Synchronization

Thread-Safe Collections

Summary and Further Reading

II. Scalable Systems

5. Application Services

Service Design

Application Programming Interface (API)

Designing Services

State Management

Applications Servers

Horizontal Scaling

Load Balancing

Load Distribution Policies

Health Monitoring

Elasticity

Session Affinity

Summary and Further Reading

6. Distributed Caching

Application Caching

Web Caching

Cache-Control

Expires and Last-Modified

Etag

Summary and Further Reading

7. Asynchronous Messaging

Introduction to Messaging

Messaging Primitives

Message Persistence

Publish–Subscribe

Message Replication

Example: RabbitMQ

Messages, Exchanges, and Queues

Distribution and Concurrency

Data Safety and Performance Trade-offs

Availability and Performance Trade-Offs

Messaging Patterns

Competing Consumers

Exactly-Once Processing

Poison Messages

Summary and Further Reading

8. Serverless Processing Systems

The Attractions of Serverless

Google App Engine

The Basics

GAE Standard Environment

Autoscaling

AWS Lambda

Lambda Function Life Cycle

Execution Considerations

Scalability

Case Study: Balancing Throughput and Costs

Choosing Parameter Values

GAE Autoscaling Parameter Study Design

Results

Summary and Further Reading

9. Microservices

The Movement to Microservices

Monolithic Applications

Breaking Up the Monolith

Deploying Microservices

Principles of Microservices

Resilience in Microservices

Cascading Failures

Bulkhead Pattern

Summary and Further Reading

III. Scalable Distributed Databases

10. Scalable Database Fundamentals

Distributed Databases

Scaling Relational Databases

Scaling Up

Scaling Out: Read Replicas

Scale Out: Partitioning Data

Example: Oracle RAC

The Movement to NoSQL

NoSQL Data Models

Query Languages

Data Distribution

The CAP Theorem

Summary and Further Reading

11. Eventual Consistency

What Is Eventual Consistency?

Inconsistency Window

Read Your Own Writes

Tunable Consistency

Quorum Reads and Writes

Replica Repair

Active Repair

Passive Repair

Handling Conflicts

Last Writer Wins

Version Vectors

Summary and Further Reading

12. Strong Consistency

Introduction to Strong Consistency

Consistency Models

Distributed Transactions

Two-Phase Commit

2PC Failure Modes

Distributed Consensus Algorithms

Raft

Leader Election

Strong Consistency in Practice

VoltDB

Google Cloud Spanner

Summary and Further Reading

13. Distributed Database Implementations

Redis

Data Model and API

Distribution and Replication

Strengths and Weaknesses

MongoDB

Data Model and API

Distribution and Replication

Strengths and Weaknesses

Amazon DynamoDB

Data Model and API

Distribution and Replication

Strengths and Weaknesses

Summary and Further Reading

IV. Event and Stream Processing

14. Scalable Event-Driven Processing

Event-Driven Architectures

Apache Kafka

Topics

Producers and Consumers

Scalability

Availability

Summary and Further Reading

15. Stream Processing Systems

Introduction to Stream Processing

Stream Processing Platforms

Case Study: Apache Flink

DataStream API

Scalability

Data Safety

Conclusions and Further Reading

16. Final Tips for Success

Automation

Observability

Deployment Platforms

Data Lakes

Further Reading and Conclusions

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