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designing-data-intensive-applications-toc.md

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Designing Data-Intensive Applications ( TOC )

Table of Contents

References

  • Book "Designing Data-Intensive Applications"
    • ZH Ver. :《 数据密集型应用系统设计 》

Part I. Foundations of Data Systems

1. Reliable, Scalable, and Maintainable Applications

Thinking About Data Systems

Reliability ( 可靠性 )

  • Hardware Faults
  • Software Errors
  • Human Errors
  • How Important Is Reliability?

Scalability ( 可伸缩性 )

  • Describing Load
  • Describing Performance
  • Approaches for Coping with Load

Maintainability ( 可维护性 )

  • Operability: Making Life Easy for Operations ( 可运维性 )
  • Simplicity: Managing Complexity ( 简单性 )
  • Evolvability: Making Change Easy ( 可演化性 )

2. Data Models and Query Languages

Relational Model Versus Document Model ( 关系模型 / 文档模型 )

  • The Birth of NoSQL
  • The Object-Relational Mismatch
  • Many-to-One and Many-to-Many Relationships
  • Are Document Databases Repeating History?
  • Relational Versus Document Databases Today

Query Languages for Data

  • Declarative Queries on the Web ( 声明式查询 )
  • MapReduce Querying

Graph-Like Data Models ( 图模型 )

  • Property Graphs
  • The Cypher Query Language
  • Graph Queries in SQL
  • Triple-Stores and SPARQL
  • The Foundation: Datalog

3. Storage and Retrieval

Data Structures That Power Your Database

  • Hash Indexes
  • SSTables and LSM-Trees ( 日志结构合并树 )
  • B-Trees
  • Comparing B-Trees and LSM-Trees
  • Other Indexing Structures

Transaction Processing or Analytics?

  • Data Warehousing ( 数据仓库 )
  • Stars and Snowflakes: Schemas for Analytics Column-Oriented Storage

Column-Oriented Storage ( 列式存储 )

  • Column Compression
  • Sort Order in Column Storage
  • Writing to Column-Oriented Storage
  • Aggregation: Data Cubes and Materialized Views

4. Encoding and Evolution

Formats for Encoding Data

  • Language-Specific Formats
  • JSON, XML, and Binary Variants
  • Thrift and Protocol Buffers
  • Avro
  • The Merits of Schemas

Modes of Dataflow

  • Dataflow Through Databases
  • Dataflow Through Services: REST and RPC
  • Message-Passing Dataflow

Part II. Distributed Data

5. Replication

Leaders and Followers

  • Synchronous Versus Asynchronous Replication
  • Setting Up New Followers
  • Handling Node Outages
  • Implementation of Replication Logs

Problems with Replication Lag

  • Reading Your Own Writes
  • Monotonic Reads
  • Consistent Prefix Reads

Solutions for Replication Lag

  • Multi-Leader Replication
  • Use Cases for Multi-Leader Replication
  • Handling Write Conflicts

Multi-Leader Replication Topologies

  • Leaderless Replication
  • Writing to the Database When a Node Is Down
  • Limitations of Quorum Consistency
  • Sloppy Quorums and Hinted Handoff
  • Detecting Concurrent Writes

6. Partitioning

Partitioning and Replication

Partitioning of Key-Value Data

  • Partitioning by Key Range
  • Partitioning by Hash of Key
  • Skewed Workloads and Relieving Hot Spots

Partitioning and Secondary Indexes

  • Partitioning Secondary Indexes by Document
  • Partitioning Secondary Indexes by Term

Rebalancing Partitions

  • Strategies for Rebalancing
  • Operations: Automatic or Manual Rebalancing

Request Routing

  • Parallel Query Execution

7. Transactions

The Slippery Concept of 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 (2PL)
  • Serializable Snapshot Isolation (SSI)

8. The Trouble with Distributed Systems

Faults and Partial Failures

  • Cloud Computing and Supercomputing

Unreliable Networks

  • Network Faults in Practice
  • Detecting Faults
  • 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 Truth Is Defined by the Majority
  • Byzantine Faults
  • System Model and Reality

9. Consistency and Consensus

Consistency Guarantees

Linearizability

  • What Makes a System Linearizable?
  • Relying on Linearizability
  • Implementing Linearizable Systems
  • The Cost of Linearizability

Ordering Guarantees

  • Ordering and Causality
  • Sequence Number Ordering
  • Total Order Broadcast

Distributed Transactions and Consensus

  • Atomic Commit and Two-Phase Commit (2PC)
  • Distributed Transactions in Practice
  • Fault-Tolerant Consensus
  • Membership and Coordination Services

Part III. Derived Data

10. Batch Processing

Batch Processing with Unix Tools

  • Simple Log Analysis
  • The Unix Philosophy

MapReduce and Distributed Filesystems

  • MapReduce Job Execution
  • Reduce-Side Joins and Grouping
  • Map-Side Joins
  • The Output of Batch Workflows
  • Comparing Hadoop to Distributed Databases

Beyond MapReduce

  • Materialization of Intermediate State
  • Graphs and Iterative Processing
  • High-Level APIs and Languages

11. Stream Processing

Transmitting Event Streams

  • Messaging Systems
  • Partitioned Logs

Databases and Streams

  • Keeping Systems in Sync
  • Change Data Capture
  • Event Sourcing
  • State, Streams, and Immutability

Processing Streams

  • Uses of Stream Processing
  • Reasoning About Time
  • Stream Joins
  • Fault Tolerance

12. The Future of Data 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

Doing the Right Thing

  • Predictive Analytics
  • Privacy and Tracking 536 Summary

Ending

  • Glossary
  • 读者跋