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1 | Designing Data-Intensive Applications | Designing Data-Intensive Applications
The Big Ideas Behind Reliable, Scalable,
and Maintainable Systems
Martin Kleppmann | text00000.html |
2 | Designing Data-Intensive Applications | 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 promoti... | text00001.html |
3 | Dedication | Dedication
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 fo... | text00002.html |
4 | Chapter 4 | 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 ... | text00003.html |
5 | Preface | Preface
If you have worked in software engineering in recent years, especially in server-side and backend systems, you have probably been bombarded with a plethora of buzzwords relating to storage and processing of data. NoSQL! Big Data! Web-scale! Sharding! Eventual consistency! ACID! CAP theorem! Cloud services! MapR... | text00004.html |
6 | Part I.Foundations of Data Systems | Part I.
Foundations of Data Systems
The first four chapters go through the fundamental ideas that apply to all data systems, whether running on a single machine or distributed across a cluster of machines:
Chapter 1
introduces the terminology and approach that we’re going to use throughout this book. It examines what w... | text00005.html |
7 | Chapter 1.Reliable, Scalable, andMaintainable Applications | Chapter 1.
Reliable, Scalable, and
Maintainable Applications
The Internet was done so well that most people think of it as a natural resource like the Pacific Ocean, rather than something that was man-made. When was the last time a technology with a scale like that was so error-free?
Alan Kay
, in interview with
Dr Dob... | text00006.html |
8 | Chapter 2.Data Models and Query Languages | Chapter 2.
Data Models and Query Languages
The limits of my language mean the limits of my world.
Ludwig Wittgenstein,
Tractatus Logico-Philosophicus
(1922)
Data models are perhaps the most important part of developing software, because they have such a profound effect: not only on how the software is written, but also... | text00007.html |
9 | Chapter 3.Storage and Retrieval | Chapter 3.
Storage and Retrieval
Wer Ordnung hält, ist nur zu faul zum Suchen.
(If you keep things tidily ordered, you’re just too lazy to go searching.)
German proverb
On the most fundamental level, a database needs to do two things: when you give it some data, it should store the data, and when you ask it again later... | text00008.html |
10 | Chapter 4.Encoding and Evolution | Chapter 4.
Encoding and Evolution
Everything changes and nothing stands still.
Heraclitus of Ephesus, as quoted by Plato in
Cratylus
(360 BCE)
Applications inevitably change over time. Features are added or modified as new products are launched, user requirements become better understood, or business circumstances chan... | text00009.html |
11 | Part II.Distributed Data | Part II.
Distributed Data
For a successful technology, reality must take precedence over public relations, for nature cannot be fooled.
Richard Feynman,
Rogers Commission Report
(1986)
In
Part I
of this book, we discussed aspects of data systems that apply when data is stored on a single machine. Now, in
Part II
, we m... | text00010.html |
12 | Chapter 5.Replication | Chapter 5.
Replication
The major difference between a thing that might go wrong and a thing that cannot possibly go wrong is that when a thing that cannot possibly go wrong goes wrong it usually turns out to be impossible to get at or repair.
Douglas Adams,
Mostly Harmless
(1992)
Replication
means keeping a copy of the... | text00011.html |
13 | Chapter 6.Partitioning | Chapter 6.
Partitioning
Clearly, we must break away from the sequential and not limit the computers. We must state definitions and provide for priorities and descriptions of data. We must state relationships, not procedures.
Grace Murray Hopper,
Management and the Computer of the Future
(1962)
In
Chapter 5
we discussed... | text00012.html |
14 | Chapter 7.Transactions | Chapter 7.
Transactions
Some authors have claimed that general two-phase commit is too expensive to support, because of the performance or availability problems that it brings. We believe it is better to have application programmers deal with performance problems due to overuse of transactions as bottlenecks arise, rat... | text00013.html |
15 | Chapter 8.The Trouble with Distributed Systems | Chapter 8.
The Trouble with Distributed Systems
Hey I just met you
The network’s laggy
But here’s my data
So store it maybe
Kyle Kingsbury,
Carly Rae Jepsen and the Perils of Network Partitions
(2013)
A recurring theme in the last few chapters has been how systems handle things going wrong. For example, we discussed re... | text00014.html |
16 | Chapter 9.Consistency and Consensus | Chapter 9.
Consistency and Consensus
Is it better to be alive and wrong or right and dead?
Jay Kreps,
A Few Notes on Kafka and Jepsen
(2013)
Lots of things can go wrong in distributed systems, as discussed in
Chapter 8
. The simplest way of handling such faults is to simply let the entire service fail, and show the use... | text00015.html |
17 | Part III.Derived Data | Part III.
Derived Data
In Parts
I
and
II
of this book, we assembled from the ground up all the major considerations that go into a distributed database, from the layout of data on disk all the way to the limits of distributed consistency in the presence of faults. However, this discussion assumed that there was only on... | text00016.html |
18 | Chapter 10.Batch Processing | Chapter 10.
Batch Processing
A system cannot be successful if it is too strongly influenced by a single person. Once the initial design is complete and fairly robust, the real test begins as people with many different viewpoints undertake their own experiments.
Donald Knuth
In the first two parts of this book we talked... | text00017.html |
19 | Chapter 11.Stream Processing | Chapter 11.
Stream Processing
A complex system that works is invariably found to have evolved from a simple system that works. The inverse proposition also appears to be true: A complex system designed from scratch never works and cannot be made to work.
John Gall,
Systemantics
(1975)
In
Chapter 10
we discussed batch p... | text00018.html |
20 | Chapter 12.The Future of Data Systems | Chapter 12.
The Future of Data Systems
If a thing be ordained to another as to its end, its last end cannot consist in the preservation of its being. Hence a captain does not intend as a last end, the preservation of the ship entrusted to him, since a ship is ordained to something else as its end, viz. to navigation.
(... | text00019.html |
21 | Glossary | Glossary
Note
Please note that the definitions in this glossary are short and simple, intended to convey the core idea but not the full subtleties of a term. For more detail, please follow the references into the main text.
asynchronous
Not waiting for something to complete (e.g., sending data over the network to anoth... | text00020.html |
22 | Index | Index
A
aborts (transactions)
,
Transactions
,
Atomicity
in two-phase commit
,
Introduction to two-phase commit
performance of optimistic concurrency control
,
Performance of serializable snapshot isolation
retrying aborted transactions
,
Handling errors and aborts
abstraction
,
Simplicity: Managing Complexity
,
Data M... | text00021.html |
23 | About the Author | About the Author
Martin Kleppmann
is a researcher in distributed systems at the University of Cambridge, UK. Previously he was a software engineer and entrepreneur at internet companies including LinkedIn and Rapportive, where he worked on large-scale data infrastructure. In the process he learned a few things the hard... | text00022.html |
24 | Colophon | Colophon
The animal on the cover of
Designing Data-Intensive Applications
is an Indian wild boar (
Sus scrofa cristatus
), a subspecies of wild boar found in India, Myanmar, Nepal, Sri Lanka, and Thailand. They are distinctive from European boars in that they have higher back bristles, no woolly undercoat, and a larger... | text00023.html |
25 | Chapter 25 | 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
I. Foundations of Data Systems
1. Reliable, Scalable, and Maintainable Applications
Thinking About Data Systems
Reliability
Hardware Faults
Software Errors
Human Er... | text00024.html |
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