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Designing data-intensive applications : The big ideas behind reliable, scalable, and maintainable systems / Martin Kleppmann.

By: Publisher: Sebastopol, CA : O'Reilly Media, 2017Description: 590 pagesContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781449373320
Subject(s): Additional physical formats: Print version:: Designing Data-Intensive Applications : The Big Ideas Behind Reliable, Scalable, and Maintainable SystemsLOC classification:
  • QA76.76.A65
Contents:
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 EasySummary; 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 DatabaseHash 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 DataflowDataflow 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 ReplicationWriting 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
Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
BOOK BOOK NCAR Library Mesa Lab QA76.76 .A65 .K64 2017 1 Checked out 07/01/2024 50583020006296
Total holds: 0

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 EasySummary; 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 DatabaseHash 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 DataflowDataflow 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 ReplicationWriting 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

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