本文主要是介绍DDIA（数据密集型应用系统设计）第二版出了【part 1】，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
深受喜爱的DDIA终于出第二版了！
不过目前还属于 Early Release 版本阶段，只发布了前三章。
本书在GitHub 上的地址是：GitHub - ept/ddia2-feedback: Reader feedback on the early release of Designing Data-Intensive Applications, second edition
首先看看大体上和第一版有什么不同：
| 第一版 | 第二版 | |
| 书名 | Designing Data-Intensive Applications | Designing Data-Intensive Applications, 2nd Edition |
| 副标题 | The Big Ideas Behind Reliable, Scalable, and Maintainable Systems | The Big Ideas Behind Reliable, Scalable, and Maintainable Systems |
| 作者 | Martin Kleppmann | Martin Kleppmann, Chris Riccomini |
| 出版社 | O'Reilly Media, Inc. | O'Reilly Media, Inc. |
| 出版时间 | 2017年3月 | 2025年12月（预计？） |
| 修订历史 | 2019-08-19：第7次修订发布 …… 2021-11-12：第17次修订发布 | 2024-08-27：早期版本第一次发布，包括章节目录和前三章 |
| 勘误表 | https://www.oreilly.com/catalog/ errata.csp?isbn=9781449373320 | -- |
| ISBN | 978-1-449-37332-0 | 978-1-098-11900-3 |
主要的区别在于第二版多了一个第二作者，Chris Riccomini。
他是何许人？
上他的官网（https://cnr.sh/）可知，他是个有十多年经验的软件工程师，在WePay、LinkedIn 和PayPal 这些大公司工作过，是开源流处理框架Apache Samza的作者，还在2021年和别人合作出过一本指导新人程序员的书《The Missing README》。
虽然没用过他的框架也没看过他的书，但他能被Martin Kleppmann看中，参与第二版写作，可见还是很有实力的。
两个版本章节目录的对比：
| 第一版 | 第二版 | ||
| 1 | Reliable, Scalable, and Maintainable Applications | 1 | Tradeoffs in Data Systems Architecture |
| 2 | Defining NonFunctional Requirements | ||
| 2 | Data Models and Query Languages | 3 | Data Models and Query Languages |
| 3 | Storage and Retrieval | 4 | Storage and Retrieval |
| 4 | Encoding and Evolution | 5 | Encoding and Evolution |
| 5 | Replication | 6 | Replication |
| 6 | Partitioning | 7 | Partitioning |
| 7 | Transactions | 8 | Transactions |
| 8 | The Trouble with Distributed Systems | 9 | The Trouble with Distributed Systems |
| 9 | Consistency and Consensus | 10 | Consistency and Consensus |
| 10 | Batch Processing | 11 | Batch Processing |
| 11 | Stream Processing | 12 | Stream Processing |
| 12 | The Future of Data Systems | 13 | Doing the Right Thing |
可以看到，第二版比第一版增加了一个章节，不过并不是从后面增加的，而是原来的第一章变成了第一和第二章。
中间的章节名称都没有变化。
最后一章修改了名称，而实际上这个名称其实来自第一版最后一章的最后一节，所以可以得出这个结论：第一版的最后一章同时包含了对技术发展方向和伦理道德方面的讨论，而第二版的最后一章只包含了伦理道德方面的讨论，而把技术发展方向的讨论移到其他对应的章节了。
再看看已发布章节的单词数对比：
| 第一版 | 第二版 | ||
| 第一章 | 约 8 k | 第一章 | 约 11.7 k |
| 第二章 | 约 11.5 k | ||
| 第二章 | 约 13 k | 第三章 | 约 17 k |
从单词数量上看，可见第二版内容做了大幅度的扩充，希望能读到更多的干货。
前三章具体内容的对比，后续会进行阅读和总结，这里暂不包含。
第二版参考文献：
DDIA的一大特色就是其丰富的参考文献，第二版的参考文献也有大幅度刷新。
具体如下：
第一章参考文献（共56篇）
[1] Richard T. Kouzes, Gordon A. Anderson, Stephen T. Elbert, Ian Gorton, and Deborah K. Gracio. The Changing Paradigm of Data-Intensive Computing. IEEE Computer, volume 42, issue 1, January 2009. doi:10.1109/MC.2009.26
[2] Martin Kleppmann, Adam Wiggins, Peter van Hardenberg, and Mark McGranaghan. Local-first software: you own your data, in spite of the cloud. At 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!), October 2019. doi:10.1145/3359591.3359737
[3] Joe Reis and Matt Housley. Fundamentals of Data Engineering. O’Reilly Media, 2022. ISBN: 9781098108304
[4] Rui Pedro Machado and Helder Russa. Analytics Engineering with SQL and dbt. O’Reilly Media, 2023. ISBN: 9781098142384
[5] Edgar F. Codd, S. B. Codd, and C. T. Salley. Providing OLAP to User-Analysts: An IT Mandate. E. F. Codd Associates, 1993. Archived at perma.cc/RKX8-2GEE
[6] Surajit Chaudhuri and Umeshwar Dayal. An Overview of Data Warehousing and OLAP Technology. ACM SIGMOD Record, volume 26, issue 1, pages 65–74, March 1997. doi:10.1145/248603.248616
[7] Fatma Özcan, Yuanyuan Tian, and Pinar Tözün. Hybrid Transactional/Analytical Processing: A Survey. At ACM International Conference on Management of Data (SIGMOD), May 2017. doi:10.1145/3035918.3054784
[8] Adam Prout, Szu-Po Wang, Joseph Victor, Zhou Sun, Yongzhu Li, Jack Chen, Evan Bergeron, Eric Hanson, Robert Walzer, Rodrigo Gomes, and Nikita Shamgunov. Cloud-Native Transactions and Analytics in SingleStore. At International Conference on Management of Data (SIGMOD), June 2022. doi:10.1145/3514221.3526055
[9] Michael Stonebraker and Uğur Çetintemel. ‘One Size Fits All’: An Idea Whose Time Has Come and Gone. At 21st International Conference on Data Engineering (ICDE), April 2005. doi:10.1109/ICDE.2005.1
[10] Jeffrey Cohen, Brian Dolan, Mark Dunlap, Joseph M Hellerstein, and Caleb Welton. MAD Skills: New Analysis Practices for Big Data. Proceedings of the VLDB Endowment, volume 2, issue 2, pages 1481–1492, August 2009. doi:10.14778/1687553.1687576
[11] Dan Olteanu. The Relational Data Borg is Learning. Proceedings of the VLDB Endowment, volume 13, issue 12, August 2020. doi:10.14778/3415478.3415572
[12] Matt Bornstein, Martin Casado, and Jennifer Li. Emerging Architectures for Modern Data Infrastructure: 2020. future.a16z.com, October 2020. Archived at perma.cc/LF8W-KDCC
[13] Martin Fowler. DataLake. martinfowler.com, February 2015. Archived at perma.cc/4WKN-CZUK
[14] Bobby Johnson and Joseph Adler. The Sushi Principle: Raw Data Is Better. At Strata+Hadoop World, February 2015.
[15] Michael Armbrust, Ali Ghodsi, Reynold Xin, and Matei Zaharia. Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics. At 11th Annual Conference on Innovative Data Systems Research (CIDR), January 2021.
[16] DataKitchen, Inc. The DataOps Manifesto. dataopsmanifesto.org, 2017. Archived at perma.cc/3F5N-FUQ4
[17] Tejas Manohar. What is Reverse ETL: A Definition & Why It’s Taking Off. hightouch.io, November 2021. Archived at perma.cc/A7TN-GLYJ
[18] Simon O’Regan. Designing Data Products. towardsdatascience.com, August 2018. Archived at perma.cc/HU67-3RV8
[19] Camille Fournier. Why is it so hard to decide to buy? skamille.medium.com, July 2021. Archived at perma.cc/6VSG-HQ5X
[20] David Heinemeier Hansson. Why we’re leaving the cloud. world.hey.com, October 2022. Archived at perma.cc/82E6-UJ65
[21] Nima Badizadegan. Use One Big Server. specbranch.com, August 2022. Archived at perma.cc/M8NB-95UK
[22] Steve Yegge. Dear Google Cloud: Your Deprecation Policy is Killing You. steve-yegge.medium.com, August 2020. Archived at perma.cc/KQP9-SPGU
[23] Alexandre Verbitski, Anurag Gupta, Debanjan Saha, Murali Brahmadesam, Kamal Gupta, Raman Mittal, Sailesh Krishnamurthy, Sandor Maurice, Tengiz Kharatishvili, and Xiaofeng Bao. Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases. At ACM International Conference on Management of Data (SIGMOD), pages 1041–1052, May 2017. doi:10.1145/3035918.3056101
[24] Panagiotis Antonopoulos, Alex Budovski, Cristian Diaconu, Alejandro Hernandez Saenz, Jack Hu, Hanuma Kodavalla, Donald Kossmann, Sandeep Lingam, Umar Farooq Minhas, Naveen Prakash, Vijendra Purohit, Hugh Qu, Chaitanya Sreenivas Ravella, Krystyna Reisteter, Sheetal Shrotri, Dixin Tang, and Vikram Wakade. Socrates: The New SQL Server in the Cloud. At ACM International Conference on Management of Data (SIGMOD), pages 1743–1756, June 2019. doi:10.1145/3299869.3314047
[25] Midhul Vuppalapati, Justin Miron, Rachit Agarwal, Dan Truong, Ashish Motivala, and Thierry Cruanes. Building An Elastic Query Engine on Disaggregated Storage. At 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI), February 2020.
[26] Gwen Shapira. Compute-Storage Separation Explained. thenile.dev, January 2023. Archived at perma.cc/QCV3-XJNZ
[27] Ravi Murthy and Gurmeet Goindi. AlloyDB for PostgreSQL under the hood: Intelligent, database-aware storage. cloud.google.com, May 2022. Archived at archive.org
[28] Jack Vanlightly. The Architecture of Serverless Data Systems. jack-vanlightly.com, November 2023. Archived at perma.cc/UDV4-TNJ5
[29] Eric Jonas, Johann Schleier-Smith, Vikram Sreekanti, Chia-Che Tsai, Anurag Khandelwal, Qifan Pu, Vaishaal Shankar, Joao Carreira, Karl Krauth, Neeraja Yadwadkar, Joseph E Gonzalez, Raluca Ada Popa, Ion Stoica, David A Patterson. Cloud Programming Simplified: A Berkeley View on Serverless Computing. arxiv.org, February 2019.
[30] Betsy Beyer, Jennifer Petoff, Chris Jones, and Niall Richard Murphy. Site Reliability Engineering: How Google Runs Production Systems. O’Reilly Media, 2016. ISBN: 9781491929124
[31] Thomas Limoncelli. The Time I Stole $10,000 from Bell Labs. ACM Queue, volume 18, issue 5, November 2020. doi:10.1145/3434571.3434773
[32] Charity Majors. The Future of Ops Jobs. acloudguru.com, August 2020. Archived at perma.cc/GRU2-CZG3
[33] Boris Cherkasky. (Over)Pay As You Go for Your Datastore. medium.com, September 2021. Archived at perma.cc/Q8TV-2AM2
[34] Shlomi Kushchi. Serverless Doesn’t Mean DevOpsLess or NoOps. thenewstack.io, February 2023. Archived at perma.cc/3NJR-AYYU
[35] Erik Bernhardsson. Storm in the stratosphere: how the cloud will be reshuffled. erikbern.com, November 2021. Archived at perma.cc/SYB2-99P3
[36] Benn Stancil. The data OS. benn.substack.com, September 2021. Archived at perma.cc/WQ43-FHS6
[37] Maria Korolov. Data residency laws pushing companies toward residency as a service. csoonline.com, January 2022. Archived at perma.cc/CHE4-XZZ2
[38] Kousik Nath. These are the numbers every computer engineer should know. freecodecamp.org, September 2019. Archived at perma.cc/RW73-36RL
[39] Joseph M Hellerstein, Jose Faleiro, Joseph E Gonzalez, Johann Schleier-Smith, Vikram Sreekanti, Alexey Tumanov, and Chenggang Wu. Serverless Computing: One Step Forward, Two Steps Back. At Conference on Innovative Data Systems Research (CIDR), January 2019.
[40] Frank McSherry, Michael Isard, and Derek G. Murray. Scalability! But at What COST? At 15th USENIX Workshop on Hot Topics in Operating Systems (HotOS), May 2015.
[41] Cindy Sridharan. Distributed Systems Observability: A Guide to Building Robust Systems. Report, O’Reilly Media, May 2018. Archived at perma.cc/M6JL-XKCM
[42] Charity Majors. Observability — A 3-Year Retrospective. thenewstack.io, August 2019. Archived at perma.cc/CG62-TJWL
[43] Benjamin H. Sigelman, Luiz André Barroso, Mike Burrows, Pat Stephenson, Manoj Plakal, Donald Beaver, Saul Jaspan, and Chandan Shanbhag. Dapper, a Large-Scale Distributed Systems Tracing Infrastructure. Google Technical Report dapper-2010-1, April 2010. Archived at perma.cc/K7KU-2TMH
[44] Rodrigo Laigner, Yongluan Zhou, Marcos Antonio Vaz Salles, Yijian Liu, and Marcos Kalinowski. Data management in microservices: State of the practice, challenges, and research directions. Proceedings of the VLDB Endowment, volume 14, issue 13, pages 3348–3361, September 2021. doi:10.14778/3484224.3484232
[45] Sam Newman. Building Microservices, second edition. O’Reilly Media, 2021. ISBN: 9781492034025
[46] Chris Richardson. Microservices: Decomposing Applications for Deployability and Scalability. infoq.com, May 2014. Archived at perma.cc/CKN4-YEQ2
[47] Mohammad Shahrad, Rodrigo Fonseca, Íñigo Goiri, Gohar Chaudhry, Paul Batum, Jason Cooke, Eduardo Laureano, Colby Tresness, Mark Russinovich, Ricardo Bianchini. Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider. At USENIX Annual Technical Conference (ATC), July 2020.
[48] Luiz André Barroso, Urs Hölzle, and Parthasarathy Ranganathan. The Datacenter as a Computer: Designing Warehouse-Scale Machines, third edition. Morgan & Claypool Synthesis Lectures on Computer Architecture, October 2018. doi:10.2200/S00874ED3V01Y201809CAC046
[49] David Fiala, Frank Mueller, Christian Engelmann, Rolf Riesen, Kurt Ferreira, and Ron Brightwell. Detection and Correction of Silent Data Corruption for Large-Scale High-Performance Computing,” at International Conference for High Performance Computing, Networking, Storage and Analysis (SC), November 2012. doi:10.1109/SC.2012.49
[50] Anna Kornfeld Simpson, Adriana Szekeres, Jacob Nelson, and Irene Zhang. Securing RDMA for High-Performance Datacenter Storage Systems. At 12th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud), July 2020.
[51] Arjun Singh, Joon Ong, Amit Agarwal, Glen Anderson, Ashby Armistead, Roy Bannon, Seb Boving, Gaurav Desai, Bob Felderman, Paulie Germano, Anand Kanagala, Jeff Provost, Jason Simmons, Eiichi Tanda, Jim Wanderer, Urs Hölzle, Stephen Stuart, and Amin Vahdat. Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google’s Datacenter Network. At Annual Conference of the ACM Special Interest Group on Data Communication (SIGCOMM), August 2015. doi:10.1145/2785956.2787508
[52] Glenn K. Lockwood. Hadoop’s Uncomfortable Fit in HPC. glennklockwood.blogspot.co.uk, May 2014. Archived at perma.cc/S8XX-Y67B
[53] Cathy O’Neil: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing, 2016. ISBN: 9780553418811
[54] Supreeth Shastri, Vinay Banakar, Melissa Wasserman, Arun Kumar, and Vijay Chidambaram. Understanding and Benchmarking the Impact of GDPR on Database Systems. Proceedings of the VLDB Endowment, volume 13, issue 7, pages 1064–1077, March 2020. doi:10.14778/3384345.3384354
[55] Martin Fowler. Datensparsamkeit. martinfowler.com, December 2013. Archived at perma.cc/R9QX-CME6
[56] Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 (General Data Protection Regulation). Official Journal of the European Union L 119/1, May 2016.
（未完待续）
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