Designing Data-Intensive Applications. The Big Ideas Behind by Martin Kleppmann

By Martin Kleppmann

Data is on the heart of many demanding situations in approach layout this day. tricky matters have to be discovered, equivalent to scalability, consistency, reliability, potency, and maintainability. moreover, we've an overpowering number of instruments, together with relational databases, NoSQL datastores, circulation or batch processors, and message agents. What are the proper offerings to your program? How do you're making feel of these kinds of buzzwords?

In this functional and complete advisor, writer Martin Kleppmann is helping you navigate this different panorama through studying the professionals and cons of assorted applied sciences for processing and storing info. software program retains altering, however the basic ideas stay an identical. With this booklet, software program engineers and designers will the best way to observe these rules in perform, and the way to make complete use of information in sleek applications.

  • Peer below the hood of the platforms you already use, and easy methods to use and function them extra effectively
  • Make knowledgeable judgements via making a choice on the strengths and weaknesses of alternative tools
  • Navigate the trade-offs round consistency, scalability, fault tolerance, and complexity
  • Understand the dispensed platforms learn upon which glossy databases are built
  • Peek behind the curtain of significant on-line providers, and examine from their architectures

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17] Martin Fowler: Patterns of Enterprise Application Architecture. Addison Wesley, 2002. com, November 13, 2014. : “Dynamo: Ama‐ zon’s Highly Available Key-Value Store,” at 21st ACM Symposium on Operating Sys‐ tems Principles (SOSP), October 2007. [20] Greg Linden: “Make Data Useful,” slides from presentation at Stanford Univer‐ sity Data Mining class (CS345), December 2006. com, November 12, 2014. uk, June 22, 2009. com, December 12, 2015. 24 | Chapter 1: Reliable, Scalable, and Maintainable Applications [24] Jeffrey Dean and Luiz André Barroso: “The Tail at Scale,” Communications of the ACM, volume 56, number 2, pages 74–80, February 2013.

The two most prominent were the relational model (which became SQL, and took over the world) and the network model (which initially had a large following but eventually faded into obscurity). The “great debate” between these two camps lasted for much of the 1970s [2]. Since the problem that the two models were solving is still so relevant today, it’s worth briefly revisiting this debate in today’s light. The network model The network model was standardized by a committee called the Conference on Data Systems Languages (CODASYL) and implemented by several different database ven‐ dors; it is also known as the CODASYL model [16].

A software project mired in complexity is sometimes described as a big ball of mud [30]. 20 | Chapter 1: Reliable, Scalable, and Maintainable Applications There are various possible symptoms of complexity: explosion of the state space, tight coupling of modules, tangled dependencies, inconsistent naming and terminology, hacks aimed at solving performance problems, special-casing to work around issues elsewhere, and many more. Much has been said on this topic already [31, 32, 33]. When complexity makes maintenance hard, budgets and schedules are often over‐ run.

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