Postgres Logical Replication with Rust

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Postgres Logical Replication (the ability to listen to the write-ahead-log) is an extremely useful tool that opens up a lot of use cases which has been put to great effect by companies like Materialize[0] and Redhat with Debezium[1]. Recently there was a discussion here on 'push-based output patterns'[2] where I saw it was not immediately obvious how to implement this pattern with existing tooling. This project seeks to demonstrate how to implement logical replication using Rust that can be easily set up and experimented with. [0] https://materialize.com/ [1] https://debezium.io/ [2] https://news.ycombinator.com/item?id=33370649

Visit product View on Hacker NewsNov 24, 2022

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

84%84% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

30%30% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

10%10% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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