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    Copyright
    Table of Contents
    Preface
    Who Should Read This Book?
    Scope of This Book
    Outline of This Book
    References and Further Reading
    O’Reilly Safari
    How to Contact Us
    Acknowledgments
    Part I. Foundations of Data Systems
    Chapter 1. Reliable, Scalable, and Maintainable Applications
    Thinking About Data Systems
    Reliability
    Hardware Faults
    Software Errors
    Human Errors
    How Important Is Reliability?
    Scalability
    Describing Load
    Describing Performance
    Approaches for Coping with Load
    Maintainability
    Operability: Making Life Easy for Operations
    Simplicity: Managing Complexity
    Evolvability: Making Change Easy
    Summary
    Chapter 2. Data Models and Query Languages
    Relational Model Versus Document Model
    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
    MapReduce Querying
    Graph-Like Data Models
    Property Graphs
    The Cypher Query Language
    Graph Queries in SQL
    Triple-Stores and SPARQL
    The Foundation: Datalog
    Summary
    Chapter 3. Storage and Retrieval
    Data Structures That Power Your Database
    Hash Indexes
    SSTables and LSM-Trees
    B-Trees
    Comparing B-Trees and LSM-Trees
    Other Indexing Structures
    Transaction Processing or Analytics?
    Data Warehousing
    Stars and Snowflakes: Schemas for Analytics
    Column-Oriented Storage
    Column Compression
    Sort Order in Column Storage
    Writing to Column-Oriented Storage
    Aggregation: Data Cubes and Materialized Views
    Summary
    Chapter 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
    Summary
    Part II. Distributed Data
    Chapter 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
    Summary
    Chapter 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
    Summary
    Chapter 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)
    Summary
    Chapter 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
    Summary
    Chapter 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
    Summary
    Part III. Derived Data
    Chapter 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
    Summary
    Chapter 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
    Summary
    Chapter 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
    Summary
    Glossary
    Index
    About the Author
    Colophon
    Designing Data-Intensive Applications THE BIG IDEAS BEHIND RELIABLE, SCALABLE, AND MAINTAINABLE SYSTEMS Martin Kleppmann
    Designing Data-Intensive Applications The Big Ideas Behind Reliable, Scalable, and Maintainable Systems Martin Kleppmann Beijing Beijing Boston Boston Farnham Sebastopol Farnham Sebastopol Tokyo Tokyo
    Designing Data-Intensive Applications by Martin Kleppmann Copyright © 2017 Martin Kleppmann. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/insti‐ tutional sales department: 800-998-9938 or corporate@oreilly.com. Editors: Ann Spencer and Marie Beaugureau Production Editor: Kristen Brown Copyeditor: Rachel Head Proofreader: Amanda Kersey Indexer: Ellen Troutman-Zaig Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Rebecca Demarest March 2017: First Edition Revision History for the First Edition 2017-03-01: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781449373320 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Designing Data-Intensive Applications, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 978-1-449-37332-0 [LSI]
    Technology is a powerful force in our society. Data, software, and communication can be used for bad: to entrench unfair power structures, to undermine human rights, and to protect vested interests. But they can also be used for good: to make underrepresented people’s voices heard, to create opportunities for everyone, and to avert disasters. This book is dedicated to everyone working toward the good.
    Computing is pop culture. […] Pop culture holds a disdain for history. Pop culture is all about identity and feeling like you’re participating. It has nothing to do with cooperation, the past or the future—it’s living in the present. I think the same is true of most people who write code for money. They have no idea where [their culture came from]. —Alan Kay, in interview with Dr Dobb’s Journal (2012)
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