Building a CQRS/ES web application in Elixir using Phoenix

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A case study describing how I built a web app following the Command Query Responsibility Segregation and event sourcing (CQRS/ES) pattern. In Elixir using the Phoenix Framework. https://10consulting.com/2017/01/04/building-a-cqrs-web-application-in-elixir-using-phoenix/ It uses two open-source Elixir libraries I've authored to provide the building blocks for such applications: * EventStore: A CQRS event store that uses PostgreSQL (v9.5 or later) as the underlying storage engine. [1] * Commanded: Provides support for command registration and dispatch; hosting and delegation to aggregate roots; event handling; and long running process managers. [2] It's an unorthodox approach to building Phoenix web apps. The article details why you might consider applying it. [1] https://github.com/slashdotdash/eventstore [2] https://github.com/slashdotdash/commanded

Visit product View on Hacker NewsJan 5, 2017

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apps, using, open · Missing: mac, agents, macos

29%29% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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