內容大鋼
數據是當今系統設計中諸多挑戰的核心。可擴展性、一致性、可靠性、效率、可維護性等棘手問題都亟待解決。此外，系統種類繁多，包括關係型資料庫、NoSQL數據存儲、數據倉庫以及數據湖，同時還涉及雲服務、本地部署服務、嵌入式資料庫。面對如此多的選擇，你的應用應當如何決策？又該如何釐清這些流行術語的頭緒？
作者Martin Kleppmann與Chris Riccomini在廣受好評的第1版基礎上進行了全面擴展，將新技術與新興趨勢融入本書第2版。新版將引導你穿越構建現代數據系統過程中錯綜複雜的決策與權衡迷宮，學習如何根據需求選擇合適的工具，並掌握分散式系統的核心原理。
本書涵蓋：
深入剖析你正在使用的系統內部機制，學習更高效的使用方法。
識別不同工具的優勢與局限，做出有依據的技術決策。
理解主流雲服務在可擴展性、容錯性、一致性方面的設計原理。
掌握現代資料庫構建所依賴的核心原則。
目錄
Preface
1.Trade-Offs in Data Systems Architecture
Operational Versus Analytical Systems
Characterizing Transaction Processing and Analytics
Data Warehousing
Systems of Record and Derived Data
Cloud Versus Self-Hosting
Pros and Cons of Cloud Services
Cloud Native System Architecture
Operations in the Cloud Era
Distributed Versus Single-Node Systems
Problems with Distributed Systems
Microservices and Serverless
Cloud Computing Versus Supercomputing
Data Systems, Law, and Society
Summary
2.Defining Nonfunctional Requirements
Case Study: Social Network Home Timelines
Representing Users, Posts, and Follows
Materializing and Updating Timelines
Describing Performance
Latency and Response Time
Average, Median, and Percentiles
Use of Response Time Metrics
Reliability and Fault Tolerance
Fault Tolerance
Hardware and Software Faults
Humans and Reliability
Scalability
Understanding Load
Shared-Memory, Shared-Disk, and Shared-Nothing Architectures
Principles for Scalability
Maintainability
Operability: Making Life Easy for Operations
Simplicity: Managing Complexity
Evolvability: Making Change Easy
Summary
3.Data Models and Query Languages
Relational Versus Document Models
The Object-Relational Mismatch
Normalization, Denormalization, and Joins
Many-to-One and Many-to-Many Relationships
Stars and Snowflakes: Schemas for Analytics
When to Use Which Model
Graph-Like Data Models
Property Graphs
The Cypher Query Language
Graph Queries in SQL
Triple Stores and SPARQL
Datalog: Recursive Relational Queries
GraphQL
Event Sourcing and CQRS
DataFrames, Matrices, and Arrays
Summary
4.Storage and Retrieval
Storage and Indexing for OLTP
Log-Structured Storage
B-Trees
Comparing B-Trees and LSM-Trees
Multicolumn and Secondary Indexes
Storing Values Within the Index
Keeping Everything in Memory
Data Storage for Analytics
Cloud Data Warehouses
Column-Oriented Storage
Query Execution: Compilation and Vectorization
Materialized Views and Data Cubes
Multidimensional and Full-Text Indexes
Full-Text Search
Vector Embeddings
Summary
5.Encoding and Evolution
Formats for Encoding Data
Language-Specific Formats
JSON, XML, and Binary Variants
Protocol Buffers
Avro
The Merits of Schemas
Modes of Dataflow
Dataflow Through Databases
Dataflow Through Services: REST and RPC
Durable Execution and Workflows
Event-Driven Architectures
Summary
6.Replication
Single-Leader Replication
Synchronous Versus Asynchronous Replication
Setting Up New Followers
Handling Node Outages
Implementation of Replication Logs
Problems with Replication Lag
Solutions for Replication Lag
Multi-Leader Replication
Geographically Distributed Operation
Sync Engines and Local-First Software
Dealing with Conflicting Writes
Leaderless Replication
Writing to the Database When a Node Is Down
Single-Leader Versus Leaderless Replication Performance
Multi-Region Operation
Detecting Concurrent Writes
Summary
7.Sharding
Pros and Cons of Sharding
Sharding for Multitenancy
Sharding of Key-Value Data
Sharding by Key Range
Sharding by Hash of Key
Skewed Workloads and Relieving Hot Spots
Operations: Automatic Versus Manual Rebalancing
Request Routing
Sharding and Secondary Indexes
Local Secondary Indexes
Global Secondary Indexes
Summary
8.Transactions
What Exactly Is a Transaction?
The Meaning of ACID
Single-Object and Multi-Object Operations
Weak Isolation Levels
Read Committed
Snapshot Isolation and Repeatable Read
Preventing Lost Updates
Write Skew and Phantoms
Serializability
Actual Serial Execution
Two-Phase Locking
Serializable Snapshot Isolation
Distributed Transactions
Two-Phase Commit
Distributed Transactions Across Different Systems
Database-Internal Distributed Transactions
Exactly-Once Message Processing Revisited
Summary
9.The Trouble with Distributed Systems
Faults and Partial Failures
Unreliable Networks
The Limitations of TCP
Network Faults in Practice
Fault Detection
Timeouts and Unbounded Delays
Synchronous Versus Asynchronous Networks
Unreliable Clocks
Monotonic Versus Time-of-Day Clocks
Clock Synchronization and Accuracy
Relying on Synchronized Clocks
Process Pauses
Knowledge, Truth, and Lies
The Majority Rules
Distributed Locks and Leases
Byzantine Faults
System Model and Reality
Formal Methods and Randomized Testing
Summary
10.Consistency and Consensus
Linearizability
What Makes a System Linearizable?
Relying on Linearizability
Implementing Linearizable Systems
The Cost of Linearizability
ID Generators and Logical Clocks
Logical Clocks
Linearizable ID Generators
Consensus
The Many Faces of Consensus
Consensus in Practice
Coordination Services
Summary
11.Batch Processing
Batch Processing with Unix Tools
Simple Log Analysis
Chain of Commands Versus Custom Program
Sorting Versus In-Memory Aggregation
Batch Processing in Distributed Systems
Distributed Filesystems
Object Stores
Distributed Job Orchestration
Batch Processing Models
MapReduce
Dataflow Engines
Shuffling Data
Joins and Grouping
Query Languages
DataFrames
Batch Use Cases
Extract-Transform-Load
Analytics
Machine Learning
Serving Derived Data
Summary
12.Stream Processing
Transmitting Event Streams
Messaging Systems
Log-Based Message Brokers
Databases and Streams
Keeping Systems in Sync
Change Data Capture
State, Streams, and Immutability
Processing Streams
Uses of Stream Processing
Reasoning About Time
Stream Joins
Fault Tolerance
Summary
13.A Philosophy of Streaming Systems
Data Integration
Combining Specialized Tools by Deriving Data
Batch and Stream Processing
Unbundling Databases
Composing Data Storage Technologies
Designing Applications Around Dataflow
Observing Derived State
Aiming for Correctness
The End-to-End Argument for Databases
Enforcing Constraints
Timeliness and Integrity
Trust, but Verify
Summary
14.Doing the Right Thing
Predictive Analytics
Bias and Discrimination
Responsibility and Accountability
Feedback Loops
Privacy and Tracking
Surveillance
Consent and Freedom of Choice
Privacy and Use of Data
Data as Assets and Power
Remembering the Industrial Revolution
Legislation and Self-Regulation
Summary
Glossary
Index
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