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DESIGNING DATA-INTENSIVE APPLICATIONS
Título:
DESIGNING DATA-INTENSIVE APPLICATIONS
Subtítulo:
Autor:
KLEPPMANN, M
Editorial:
O´REILLY
Año de edición:
2017
Materia
DISEÑO DEL SOFTWARE
ISBN:
978-1-4493-7332-0
Páginas:
569
58,24 €

 

Sinopsis

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?

In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.

Peer under the hood of the systems you already use, and learn how to use and operate them more effectively
Make informed decisions by identifying the strengths and weaknesses of different tools
Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity
Understand the distributed systems research upon which modern databases are built
Peek behind the scenes of major online services, and learn from their architectures



Table of Contents
Foundations of Data Systems
Chapter 1 Reliable, Scalable, and Maintainable Applications
Thinking About Data Systems
Reliability
Scalability
Maintainability
Summary
Chapter 2 Data Models and Query Languages
Relational Model Versus Document Model
Query Languages for Data
Graph-Like Data Models
Summary
Chapter 3 Storage and Retrieval
Data Structures That Power Your Database
Transaction Processing or Analytics?
Column-Oriented Storage
Summary
Chapter 4 Encoding and Evolution
Formats for Encoding Data
Modes of Dataflow
Summary
Distributed Data
Chapter 5 Replication
Leaders and Followers
Problems with Replication Lag
Multi-Leader Replication
Leaderless Replication
Summary
Chapter 6 Partitioning
Partitioning and Replication
Partitioning of Key-Value Data
Partitioning and Secondary Indexes
Rebalancing Partitions
Request Routing
Summary
Chapter 7 Transactions
The Slippery Concept of a Transaction
Weak Isolation Levels
Serializability
Summary
Chapter 8 The Trouble with Distributed Systems
Faults and Partial Failures
Unreliable Networks
Unreliable Clocks
Knowledge, Truth, and Lies
Summary
Chapter 9 Consistency and Consensus
Consistency Guarantees
Linearizability
Ordering Guarantees
Distributed Transactions and Consensus
Summary
Derived Data
Chapter 10 Batch Processing
Batch Processing with Unix Tools
MapReduce and Distributed Filesystems
Beyond MapReduce
Summary
Chapter 11 Stream Processing
Transmitting Event Streams
Databases and Streams
Processing Streams
Summary
Chapter 12 The Future of Data Systems
Data Integration
Unbundling Databases
Aiming for Correctness
Doing the Right Thing
Summary